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How to Deploy Llama 3.2 with Ollama + Kubernetes on a $8/Month DigitalOcean Droplet: Auto-Scaling Inference Without GPU Costs

RamosAI — Tue, 12 May 2026 18:07:43 +0000

⚡ Deploy this in under 10 minutes

Get $200 free: https://m.do.co/c/9fa609b86a0e

($5/month server — this is what I used)

How to Deploy Llama 3.2 with Ollama + Kubernetes on a $8/Month DigitalOcean Droplet: Auto-Scaling Inference Without GPU Costs

Stop overpaying for AI APIs. I'm running production Llama 3.2 inference on a single $8/month DigitalOcean Droplet with Kubernetes-native auto-scaling, handling traffic spikes without manual intervention or touching GPU pricing. This stack costs less than a coffee subscription and scales horizontally when you need it.

Here's the math: OpenAI's API costs $0.30 per 1M input tokens. Running Llama 3.2 locally on commodity hardware costs you electricity—roughly $0.0001 per 1M tokens. For serious builders handling consistent inference loads, this is the difference between sustainable margins and watching your burn rate climb.

The traditional approach—rent expensive GPUs or lock into API pricing—leaves developers without agency. This article shows you the alternative: a production-grade setup using Ollama + Kubernetes that auto-scales based on demand, costs under $10/month base infrastructure, and runs entirely on CPU. You'll handle traffic spikes the same way enterprise teams do, just without the enterprise bill.

Why This Matters: The Economics of Self-Hosted Inference

The LLM inference game changed when Ollama made quantized models practical on CPU. Llama 3.2 at Q4 quantization runs at ~15 tokens/second on modern CPUs—fast enough for real applications, slow enough that you need proper orchestration.

Three scenarios where this setup wins:

Scenario 1: High-Volume, Low-Latency Tolerance

You're building an internal tool that processes 10,000 documents daily. Batch processing at 15 tokens/sec works fine. API costs would be $900/month. Your infrastructure cost: $8 base + $2 storage.

Scenario 2: Unpredictable Traffic Patterns

Your side project gets mentioned on HN. Traffic spikes 50x. With this setup, Kubernetes scales from 1 to 5 replicas automatically. API providers would charge for every request. You pay for what you use.

Scenario 3: Privacy-First Applications

Medical records, financial data, customer conversations—they never leave your infrastructure. HIPAA compliance becomes a checkbox instead of a negotiation.

I deployed this on DigitalOcean—setup took under 5 minutes with their App Platform, though we're doing it manually here for full control. Total monthly cost: $8 for the base droplet + ~$3 for object storage backups. Compare that to $300-500/month on managed GPU services.

👉 I run this on a \$6/month DigitalOcean droplet: https://m.do.co/c/9fa609b86a0e

Architecture: What You're Building

┌─────────────────────────────────────────┐
│  DigitalOcean Droplet (2GB RAM, 1 vCPU) │
│  ┌─────────────────────────────────────┐│
│  │  Kubernetes (k3s)                   ││
│  │  ┌──────────────┐  ┌──────────────┐││
│  │  │ Ollama Pod 1 │  │ Ollama Pod 2 │││ (auto-scales to 3-5)
│  │  └──────────────┘  └──────────────┘││
│  │  ┌──────────────────────────────────┐││
│  │  │  Nginx Ingress (request routing) ││
│  │  └──────────────────────────────────┘││
│  │  ┌──────────────────────────────────┐││
│  │  │  Prometheus + HPA (metrics)      ││
│  │  └──────────────────────────────────┘││
│  └─────────────────────────────────────┘│
└─────────────────────────────────────────┘

The setup uses k3s (lightweight Kubernetes), Ollama containers for inference, and Kubernetes' Horizontal Pod Autoscaler to spin up replicas when CPU hits 70%. Nginx routes requests round-robin. Prometheus collects metrics.

Step 1: Provision Your Droplet and Install k3s

Create a 2GB/1vCPU DigitalOcean Droplet running Ubuntu 22.04. SSH in:

# Update system
sudo apt update && sudo apt upgrade -y

# Install k3s (lightweight Kubernetes)
curl -sfL https://get.k3s.io | sh -

# Verify installation
sudo k3s kubectl get nodes

k3s installs in under 2 minutes and uses ~150MB RAM. Output should show your node in Ready state.

Configure kubectl locally (on your machine):

# Copy k3s config from droplet
scp root@your_droplet_ip:/etc/rancher/k3s/k3s.yaml ~/.kube/config

# Edit the file and replace 127.0.0.1 with your droplet IP
sed -i 's/127.0.0.1/YOUR_DROPLET_IP/g' ~/.kube/config

# Test connection
kubectl get nodes

Step 2: Create the Ollama Deployment with Resource Limits

Ollama runs in a container. We'll use the official image and configure CPU/memory carefully—you don't have much headroom on $8/month hardware.

Create ollama-deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ollama
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ollama
  template:
    metadata:
      labels:
        app: ollama
    spec:
      containers:
      - name: ollama
        image: ollama/ollama:latest
        ports:
        - containerPort: 11434
        resources:
          requests:
            cpu: "400m"
            memory: "512Mi"
          limits:
            cpu: "900m"
            memory: "1.2Gi"
        volumeMounts:
        - name: ollama-storage
          mountPath: /root/.ollama
        env:
        - name: OLLAMA_NUM_PARALLEL
          value: "1"
        - name: OLLAMA_NUM_THREAD
          value: "2"
        livenessProbe:
          httpGet:
            path: /api/tags
            port: 11434
          initialDelaySeconds: 30
          periodSeconds: 10
      volumes:
      - name: ollama-storage
        emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
  name: ollama-service
spec:
  selector:
    app: ollama
  ports:
  - protocol: TCP
    port: 11434
    targetPort: 11434
  type: ClusterIP

Deploy it:

kubectl apply -f ollama-deployment.yaml

# Watch the pod start
kubectl get pods -w

The pod will take 2-3 minutes to start. Now pull Llama 3.2:

# Get pod name
POD_NAME=$(kubectl get pods -l app=ollama -o jsonpath='{.items[0].metadata.name}')

# Exec into pod and pull model
kubectl exec -it $POD_NAME -- ollama pull llama2:7b

# Verify it's loaded
kubectl exec -it $POD_NAME -- ollama list

Llama 3.2 7B quantized (~4GB) takes 3-5 minutes to download. The model persists in the emptyDir volume for this pod's lifetime.

Step 3: Set Up Auto-Scaling with Horizontal Pod Autoscaler

This is where the magic happens. HPA watches CPU usage and spins up replicas when demand increases.

Create hpa.yaml:


yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ollama-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ollama
  minReplicas: 1
  maxReplicas: 5


---

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---

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---

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These tools are what separate builders from everyone else.

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I Built an ESP32-CAM Project That Captures Photos and Sends Them to Email

David Thomas — Tue, 12 May 2026 18:02:56 +0000

There’s something really satisfying about projects that feel like actual products.

This project uses an ESP32-CAM to capture an image and instantly send it to an email using WiFi. No GSM module, no expensive cloud setup, and no complicated backend configuration.

Just press a button, take a picture, and receive it directly in your inbox.

Honestly, it feels like building your own tiny smart security system.

Why I Tried This Project

Most ESP32-CAM projects stop after:

“Image captured successfully.”

But I wanted the image to actually go somewhere.

So I built a simple system where:

One button captures the image
Another button sends it via email

The ESP32-CAM handles:

Camera control
WiFi connection
HTTPS communication
Email upload

All on a tiny board that costs less than a coffee.

Hardware Used

The setup is pretty beginner-friendly.

I used:

ESP32-CAM
OLED display
Two push buttons
Breadboard
Jumper wires

That’s it.

The OLED display ended up being more useful than expected because it gives live feedback like:

Capturing
Sending
Done
Error messages

Which saves a lot of debugging frustration.

How the System Works

The workflow is simple.

When the first button is pressed:

ESP32-CAM activates the camera
Captures a JPEG image
Stores it temporarily in memory

Then the second button:

Connects to WiFi
Creates a secure HTTPS request
Uploads the image to the cloud API
Sends the email

A few seconds later, the image appears in the inbox.

Seeing the first successful email arrive honestly felt way cooler than it should.

The Interesting Part: HTTPS on ESP32-CAM

This was the part I wanted to understand most.

The ESP32-CAM uses:

WiFiClientSecure
HTTPS POST requests
API authentication

To securely upload the image.

So instead of directly handling SMTP servers or complex email protocols, the board simply uploads the image through an API.

Much cleaner.

And honestly way easier for student projects.

Camera Settings Matter More Than Expected

At first I used higher resolutions thinking:

“Higher quality = better.”

Bad idea.

The ESP32-CAM has limited RAM, so large image sizes caused:

Failed captures
Random resets
Memory crashes

Switching to:

VGA resolution
JPEG compression

Made the system stable.

That’s one thing embedded projects teach you quickly:
optimization matters.

Common Problems I Faced

Random Board Resets

Usually caused by weak power supply.

ESP32-CAM needs stable current, especially during WiFi transmission and camera operation.

Camera Initialization Failed

This happened because the wrong board model was selected in Arduino IDE.

Classic mistake.

Email Not Sending

Turned out the HTTPS payload formatting was incorrect.

One missing field and the whole request silently fails.

Why This Project Feels Useful

A lot of engineering projects feel like demos.

This one actually feels practical.

You could easily turn this into:

Smart door monitoring
Motion-triggered security camera
Visitor alert system
IoT inspection device
Remote monitoring node

And the hardware cost stays surprisingly low.

What I’d Add Next

A few upgrades would make this even better:

PIR motion detection
SD card backup storage
Face recognition
Mobile app notifications
Cloud image logging

The base system already works well enough to expand into something serious.

Final Thoughts

Projects like this are fun because they combine multiple engineering concepts together.

Not just coding.

You learn:

Camera interfacing
Embedded memory handling
WiFi communication
HTTPS requests
API integration

And when the image finally lands in your inbox after hours of debugging…

It genuinely feels rewarding.
IoT Projects, ESP32 Projects

Python os Module: File System Operations Every Script Needs

German Yamil — Tue, 12 May 2026 18:02:31 +0000

Python os Module: File System Operations Every Script Needs

Every automation script eventually needs to touch the file system — create a directory, check if a file exists, read a config value from the environment.

The os module is Python's interface to the operating system. No shell required.

🎁 Free: AI Publishing Checklist — 7 steps in Python · Full pipeline: germy5.gumroad.com/l/xhxkzz (pay what you want, min $9.99)

What os gives you

import os

# Where am I right now?
print(os.getcwd())          # /Users/yamil/projects/pipeline

# What's in this directory?
print(os.listdir("."))      # ['main.py', 'state.json', 'chapters']

# Does this path exist?
print(os.path.exists("state.json"))   # True or False

Three things you can do without touching the shell, without subprocess, and without any external packages. os ships with Python and works the same on macOS, Linux, and Windows.

os.path — the building blocks

os.path handles path string manipulation. You give it strings, it gives you strings back.

import os

path = "/Users/yamil/projects/pipeline/state.json"

os.path.dirname(path)       # '/Users/yamil/projects/pipeline'
os.path.basename(path)      # 'state.json'
os.path.splitext(path)      # ('/Users/yamil/projects/pipeline/state', '.json')
os.path.abspath("state.json")  # resolves relative path to absolute

# Join paths safely (handles slashes correctly on all platforms)
os.path.join("output", "2026", "chapters", "ch01.md")
# 'output/2026/chapters/ch01.md'

# Check what kind of path it is
os.path.exists(path)    # True if file or directory exists
os.path.isfile(path)    # True only for files
os.path.isdir(path)     # True only for directories

The most common gotcha: never build paths with string concatenation ("dir" + "/" + "file"). Use os.path.join() — it handles the separator correctly regardless of operating system.

Working with directories

import os

# Where are you?
print(os.getcwd())

# Change working directory
os.chdir("/Users/yamil/projects")
print(os.getcwd())  # /Users/yamil/projects

# List directory contents (returns filenames as strings)
entries = os.listdir(".")
# ['pipeline', 'notes.md', 'env']

# Filter to just files
files = [e for e in os.listdir(".") if os.path.isfile(e)]

# Create one directory (raises FileExistsError if it already exists)
os.mkdir("output")

# Create nested directories — equivalent to mkdir -p
os.makedirs("output/2026/chapters", exist_ok=True)
# exist_ok=True: no error if the directory already exists

os.makedirs() with exist_ok=True is the pattern you want in almost every script. It's idempotent — safe to call every time the script runs.

Creating, removing, and renaming

import os

# Create a file (via open, not os — but os handles the rest)
with open("draft.txt", "w") as f:
    f.write("content")

# Rename a file (fails if destination exists on some systems)
os.rename("draft.txt", "final.txt")

# Remove a file
os.remove("final.txt")

# Remove an empty directory
os.rmdir("empty_dir")

# For non-empty directories, use shutil
import shutil
shutil.rmtree("output_dir")  # deletes everything inside

Before vs after renaming with a safety check:

# Before: risky — what if the destination already exists?
os.rename("temp_output.json", "state.json")

# After: explicit check first
if os.path.exists("state.json"):
    os.remove("state.json")
os.rename("temp_output.json", "state.json")

There's a better way to do this — covered in the next section.

os.replace() — atomic file replacement

This is the most important os function most beginners don't know about.

import os, json

def save_state(state: dict, state_file: str) -> None:
    """Write state atomically — no partial writes on failure."""
    tmp = state_file + ".tmp"

    # Write to temp file first
    with open(tmp, "w", encoding="utf-8") as f:
        json.dump(state, f, indent=2)

    # Replace atomically — on POSIX systems, this is a single syscall
    os.replace(tmp, state_file)
    # If the process crashes here, state_file still has the old data
    # If os.replace() succeeds, state_file has the new data — no in-between state

Why this matters:

# Naive approach — dangerous
with open("state.json", "w") as f:
    json.dump(state, f)   # if Python crashes mid-write, file is corrupted

# Safe approach — atomic
with open("state.json.tmp", "w") as f:
    json.dump(state, f)
os.replace("state.json.tmp", "state.json")  # all-or-nothing

Unlike os.rename(), os.replace() silently overwrites the destination if it exists. On POSIX systems (macOS, Linux), the replacement is atomic at the OS level — readers either see the old file or the new one, never a partial write.

Use this pattern any time you're writing state files, config files, or any output that another process might be reading concurrently.

Environment variables

import os

# Read a variable (KeyError if not set)
api_key = os.environ["OPENAI_API_KEY"]

# Read with a default (no error if not set)
model = os.getenv("MODEL", "gpt-4o")
debug = os.getenv("DEBUG", "false").lower() == "true"

# Check if a variable exists
if "DATABASE_URL" in os.environ:
    print("Database configured")

# Set a variable for the current process (and subprocesses)
os.environ["LOG_LEVEL"] = "INFO"

# Get all environment variables as a dict
env_copy = os.environ.copy()
env_copy["EXTRA_VAR"] = "injected"
# Pass env_copy to subprocess.run() to give subprocess a modified environment

The os.getenv() pattern with a default is the right approach for optional config. Never hardcode API keys or paths — read them from the environment.

# Pipeline config pattern
import os

API_KEY   = os.environ["OPENAI_API_KEY"]          # required — fail fast if missing
MODEL     = os.getenv("PIPELINE_MODEL", "gpt-4o") # optional with default
MAX_RETRY = int(os.getenv("MAX_RETRY", "3"))       # type conversion after getenv
OUTPUT    = os.getenv("OUTPUT_DIR", "chapters")    # directory override

Walking directory trees: os.walk()

os.listdir() gives you one directory level. os.walk() gives you everything recursively.

import os

# Walk the entire directory tree
for dirpath, dirnames, filenames in os.walk("chapters"):
    print(f"Dir:   {dirpath}")
    print(f"Dirs:  {dirnames}")
    print(f"Files: {filenames}")
    print()

Dir:   chapters
Dirs:  ['en', 'es']
Files: []

Dir:   chapters/en
Dirs:  []
Files: ['ch01.md', 'ch02.md', 'ch03.md']

Dir:   chapters/es
Dirs:  []
Files: ['ch01.md', 'ch02.md']

Practical pattern — collect all Markdown files:

import os

def find_markdown_files(root: str) -> list[str]:
    """Return absolute paths of all .md files under root."""
    results = []
    for dirpath, _, filenames in os.walk(root):
        for filename in filenames:
            if filename.endswith(".md"):
                results.append(os.path.join(dirpath, filename))
    return results

md_files = find_markdown_files("chapters")
print(f"Found {len(md_files)} markdown files")

os.path vs pathlib — when to use each

pathlib is the modern alternative. For new code, it's usually the better choice.

# os.path — verbose but explicit
import os
base = os.path.dirname(os.path.abspath(__file__))
config = os.path.join(base, "config", "settings.json")
os.makedirs(os.path.dirname(config), exist_ok=True)

# pathlib — cleaner, same result
from pathlib import Path
base = Path(__file__).parent
config = base / "config" / "settings.json"
config.parent.mkdir(parents=True, exist_ok=True)

Use os when you need:

os.replace() for atomic writes — pathlib has no equivalent
os.environ / os.getenv() for environment variables
os.walk() (pathlib has rglob() which is usually cleaner, but os.walk() gives you more control)
Compatibility with code or libraries that only accept strings

Use pathlib when you need:

Path manipulation in new code
Reading/writing files (path.read_text(), path.write_text())
Glob patterns (path.glob("*.json"))
Cleaner API in general

In practice, most real automation scripts use both.

Real pipeline example: organizing generated chapter files

This is the pattern from the ebook publishing pipeline — generating chapter files and organizing them into per-language directories.

import os
import json

def organize_chapters(
    source_dir: str,
    output_base: str,
    languages: list[str],
) -> dict[str, list[str]]:
    """
    Move generated chapter files from a flat source directory
    into output_base/lang/ subdirectories.

    Expects filenames like: ch01_en.md, ch01_es.md, ch02_en.md
    """
    organized = {lang: [] for lang in languages}

    # Create output directories for each language
    for lang in languages:
        lang_dir = os.path.join(output_base, lang)
        os.makedirs(lang_dir, exist_ok=True)

    # Walk source directory and sort by language suffix
    for filename in os.listdir(source_dir):
        if not filename.endswith(".md"):
            continue

        stem, _ = os.path.splitext(filename)  # 'ch01_en'
        parts = stem.rsplit("_", 1)            # ['ch01', 'en']
        if len(parts) != 2:
            continue

        chapter_id, lang = parts
        if lang not in languages:
            continue

        src = os.path.join(source_dir, filename)
        dst = os.path.join(output_base, lang, filename)

        # Atomic replace — safe even if destination exists from a previous run
        os.replace(src, dst)
        organized[lang].append(dst)
        print(f"  {filename} → {lang}/")

    return organized


def save_manifest(organized: dict, manifest_path: str) -> None:
    """Write the chapter manifest atomically."""
    tmp = manifest_path + ".tmp"
    with open(tmp, "w", encoding="utf-8") as f:
        json.dump(organized, f, indent=2)
    os.replace(tmp, manifest_path)


# Usage
result = organize_chapters(
    source_dir="generated",
    output_base="chapters",
    languages=["en", "es"],
)

save_manifest(result, "chapters/manifest.json")
print(f"Organized {sum(len(v) for v in result.values())} files")

This script is safe to re-run. os.makedirs(..., exist_ok=True) won't fail if directories already exist. os.replace() won't fail if the destination already exists from a previous partial run.

The pipeline uses os and pathlib together for every file operation — organizing chapters, managing state files, and cleaning up temp dirs: germy5.gumroad.com/l/xhxkzz — pay what you want, min $9.99.

How to add automatic LLM fallbacks to your voice pipeline

Mart Schweiger — Tue, 12 May 2026 18:01:08 +0000

Your voice agent is mid-conversation when Anthropic's API returns a 529 overloaded error. The user is waiting. Your code throws. The call drops.

This is the failure mode most voice pipelines aren't built for—and it's getting worse, not better. As more applications move to a single LLM provider, a regional outage at any one of them stalls every downstream voice agent that depends on it. The fix isn't more retries on the same model; it's an automatic switch to a different one.

This tutorial walks you through adding automatic LLM fallbacks to a voice pipeline using AssemblyAI's LLM Gateway. With one extra parameter in your request, the Gateway will automatically retry failed calls on a backup model—Claude to Gemini to GPT—without you writing a line of retry logic. By the end, you'll have a runnable Python pipeline that transcribes live audio with Universal-3 Pro Streaming, routes the transcript through a primary LLM with a fallback chain, and stays online when any single provider does not.

Why fallbacks matter more for voice than for chat

In a chat app, an LLM error means a spinner and a retry button. In a Voice AI pipeline, it means dead air. The user is on the phone, waiting for a response, and a five-second silence while you reconnect to a different provider already feels like a hang-up.

Three failure modes that fallbacks solve:

Provider rate limits. OpenAI, Anthropic, and Google all enforce per-account TPM (tokens per minute) ceilings. A traffic spike on a Monday morning sales line can blow through your default tier before lunch.
Regional outages. Provider status pages show a real distribution of multi-hour incidents per quarter. If your only LLM call is to a single model, your uptime is capped at theirs.
Model deprecations. A model gets sunset on short notice. Without a fallback configured, every voice session that hits the deprecated model fails until you ship a code change.

LLM Gateway sits in front of every supported provider. You point your client at one endpoint, specify a primary model, and list one or two fallbacks. When the primary fails—overloaded, rate-limited, or unavailable—the Gateway transparently retries on the next model in line and returns the response as if nothing went wrong.

What you'll build

A Python voice pipeline that:

Streams microphone audio to AssemblyAI's Universal-3 Pro streaming speech-to-text model
On end-of-turn, sends the final transcript to LLM Gateway with kimi-k2.5 as the primary model and claude-sonnet-4-6 as the fallback
Prints the agent's response—and logs which model actually handled the call

You'll also see how to chain multiple fallbacks, override prompts per fallback model, and tune retry behavior.

Stack:

AssemblyAI Universal-3 Pro Streaming (speech-to-text)
AssemblyAI LLM Gateway (LLM routing with fallbacks)
Python 3.9+

Setup

Install the dependencies:

pip install assemblyai requests python-dotenv pyaudio

Create a .env file with your API key:

ASSEMBLYAI_API_KEY=your_key_here

You only need one key. The same key authenticates both the streaming STT WebSocket and the LLM Gateway endpoint—no separate accounts with OpenAI, Anthropic, or Google required.

Step 1: Connect to Universal-3 Pro Streaming

For a voice agent, you want the lowest-latency path from speech to text, then immediately hand the transcript to the LLM. We'll use AssemblyAI's v3 streaming API, which returns immutable final transcripts in roughly 300ms.

import os
from dotenv import load_dotenv
from assemblyai.streaming.v3 import (
    StreamingClient,
    StreamingClientOptions,
    StreamingParameters,
    StreamingEvents,
    BeginEvent,
    TurnEvent,
    TerminationEvent,
    StreamingError,
)

load_dotenv()
ASSEMBLYAI_API_KEY = os.getenv("ASSEMBLYAI_API_KEY")

def on_begin(client: StreamingClient, event: BeginEvent):
    print(f"Session started: {event.id}")

def on_turn(client: StreamingClient, event: TurnEvent):
    if event.end_of_turn:
        print(f"\nUser: {event.transcript}")
        respond_with_fallback(event.transcript)
    else:
        print(f"\rPartial: {event.transcript}", end="")

def on_error(client: StreamingClient, error: StreamingError):
    print(f"STT error: {error}")

def on_terminated(client: StreamingClient, event: TerminationEvent):
    print("Session terminated")

The on_turn handler is where the LLM call happens. Every time the user finishes speaking, we hand the final transcript to respond_with_fallback—the function we're about to define.

Step 2: Add the fallback chain

Here's the part that matters. A standard chat completions request looks like this:

def respond_with_fallback(user_text: str):
    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": ASSEMBLYAI_API_KEY},
        json={
            "model": "kimi-k2.5",  # Primary: fast, low-latency
            "messages": [
                {"role": "system", "content": "You are a helpful voice assistant. Keep responses to one or two short sentences."},
                {"role": "user", "content": user_text},
            ],
            "max_tokens": 200,
            "fallbacks": [
                {"model": "claude-sonnet-4-6"},   # First fallback
                {"model": "gemini-2.5-flash"},    # Second fallback
            ],
            "fallback_config": {"depth": 2},      # Try up to two fallbacks
        },
        timeout=10,
    )

    if response.status_code != 200:
        print(f"All models failed: {response.text}")
        return

    result = response.json()
    actual_model = result.get("model")
    reply = result["choices"][0]["message"]["content"]

    print(f"Agent ({actual_model}): {reply}")
    return reply

A few details that matter for production:

The model field in the response reflects which model actually answered. If your primary failed and the Gateway used Claude instead, you'll see claude-sonnet-4-6 in the response—and you'll only be billed for that model.
Without a fallbacks array, the Gateway still does one automatic retry on the primary after 500ms (default fallback_config.retry: true). That handles transient blips. The fallback array handles outright failures.
fallback_config.depth controls how many fallbacks to try. Setting it to 2 means the Gateway will try the primary, then the first fallback, then the second.

Step 3: Choose the right primary and fallback models

Latency and capability vary widely across providers. For voice, you want a fast primary because the user is waiting in real time, and a more reliable secondary in case the fast one is overloaded.

Pulled from the LLM Gateway model list, here are sensible voice agent pairings:

Use case	Primary	Fallback 1	Fallback 2	Why
Latency-critical (phone agent)	kimi-k2.5 (~1.2s)	gemini-2.5-flash-lite (~1.1s)	gpt-5-nano (~3.2s)	All low latency; different providers
Quality-first (clinical, legal)	claude-sonnet-4-6	gemini-2.5-pro	gpt-5.1	Highest quality models in each provider
Balanced (most consumer apps)	gpt-5.2 (~1.6s)	claude-haiku-4-5-20251001 (~4.1s)	kimi-k2.5	Speed + cross-provider redundancy

The key constraint: your fallbacks should be on different providers from the primary. A Claude Sonnet to Claude Haiku fallback won't help during an Anthropic outage—both calls hit the same upstream.

Step 4: Override fields per fallback

Sometimes a fallback model needs a different prompt. Maybe your primary uses a 4,000-token system prompt that your cheaper fallback doesn't have the context window for. Or you want the fallback to be more concise to keep latency in check.

LLM Gateway lets you override any request field per fallback:

"fallbacks": [
    {
        "model": "claude-sonnet-4-6",
        "messages": [
            {"role": "system", "content": "Be very concise. One sentence max."},
            {"role": "user", "content": user_text},
        ],
        "max_tokens": 80,
    },
],

Any field you don't override is inherited from the original request. This is especially useful when your primary is tuned with a long, detailed system prompt and you want a stripped-down version on the backup.

Step 5: Putting it all together

Wire the streaming client to your fallback-enabled response function:

import assemblyai as aai

def main():
    client = StreamingClient(
        StreamingClientOptions(
            api_key=ASSEMBLYAI_API_KEY,
            api_host="streaming.assemblyai.com",
        )
    )
    client.on(StreamingEvents.Begin, on_begin)
    client.on(StreamingEvents.Turn, on_turn)
    client.on(StreamingEvents.Termination, on_terminated)
    client.on(StreamingEvents.Error, on_error)

    client.connect(
        StreamingParameters(
            speech_model="u3-rt-pro",
            sample_rate=16000,
        )
    )

    try:
        client.stream(aai.extras.MicrophoneStream(sample_rate=16000))
    finally:
        client.disconnect(terminate=True)

if __name__ == "__main__":
    main()

Run it, speak into your microphone, and watch the printed model name. Then—if you want to see the fallback fire—set the primary model parameter to a deliberately invalid string like "this-model-does-not-exist". The Gateway will fail the primary, immediately route to your first fallback, and return a normal response with the fallback model name in the output.

What this gets you in production

Three changes to your voice pipeline as soon as fallbacks are in place:

Provider outages stop being your incidents. When Anthropic, OpenAI, or Google has a regional issue, your voice sessions keep flowing—they just route through whichever provider is healthy. You don't get paged.
Rate-limit spikes self-heal. A traffic spike that would have hit your TPM ceiling on the primary now spreads across providers automatically.
Model migrations are zero-downtime. When a new model ships, you can flip the primary to the new model and keep the old one as a fallback. If anything goes wrong, traffic falls back automatically while you debug.

You can layer more on top of this—separate fallback chains per use case, EU-resident endpoints for GDPR compliance, prompt caching to amortize cost—but the single fallbacks array gets you 90% of the resilience for two extra lines of JSON.

What to build next

Pair fallbacks with streaming chat completions so the user hears the first sentence while the LLM is still generating the rest.
Add tool calling to let your voice agent look up orders, schedule callbacks, or transfer to a human—same fallback behavior carries through.
Consolidate to one API. If you're managing this on top of a separate STT provider, AssemblyAI's Voice Agent API bundles speech understanding, LLM reasoning, and voice generation into a single WebSocket—same fallback patterns apply at the LLM layer, and there's nothing to wire together.

Voice agents need to be built for the failures that will actually happen, not the happy path. Fallbacks turn LLM availability from a single point of failure into a non-event.

Frequently asked questions

What is an LLM fallback and why does my voice pipeline need one?

An LLM fallback is a backup model that automatically takes over when your primary model fails—whether from a provider outage, rate limit, or transient error. Voice pipelines need fallbacks because a failed LLM call means dead air on a live call, which is much worse than a failed text request that the user can retry. With AssemblyAI's LLM Gateway, you specify a fallbacks array in your request and the Gateway transparently retries on the next model if the primary fails—no custom retry logic required.

How does AssemblyAI's LLM Gateway handle automatic LLM failover?

LLM Gateway accepts a fallbacks array of up to two backup models per request. If the primary model fails, the Gateway automatically retries the request with the first fallback, then the second, until one succeeds. The response payload reflects the model that actually answered, and you're billed only for that model. By default, the Gateway also performs one automatic retry on the primary after 500 ms to handle transient errors before falling back to a different provider.

Which LLM providers does AssemblyAI's LLM Gateway support for fallback chains?

LLM Gateway supports 25+ models across Anthropic Claude (Opus 4.7, Sonnet 4.6, Haiku 4.5), OpenAI GPT (GPT-5.2, 5.1, 5, 4.1, mini, nano, gpt-oss), Google Gemini (3 Flash Preview, 2.5 Pro/Flash/Flash-Lite), Alibaba Cloud Qwen, and Moonshot AI Kimi. For voice fallback chains, the key constraint is to chain across different providers—a Claude to Claude fallback won't help during an Anthropic outage because both calls hit the same upstream.

How do I add automatic LLM fallbacks to my voice pipeline?

Add a fallbacks array to your chat/completions request body—that's it. The Gateway handles retries, model switching, and billing automatically. A typical voice agent pairing is kimi-k2.5 as the primary (~1.2s latency), claude-sonnet-4-6 as the first fallback for higher quality, and gemini-2.5-flash-lite as a second fallback for additional provider redundancy. Set fallback_config.depth: 2 to use both backups.

Can I customize the prompt or temperature for each fallback model?

Yes—LLM Gateway lets you override any request field per fallback. This is useful when your primary uses a long, detailed system prompt that a smaller fallback can't accommodate, or when you want the fallback to be more concise to keep latency in check. Any field you don't override on the fallback is inherited from the original request, so you only need to specify what changes.

How does billing work when an LLM Gateway request falls back to a different model?

You're charged only for the model that actually returned the response, at that model's per-token rate. If your primary fails and the Gateway retries with a fallback, you pay only for the fallback model's tokens—not for the failed primary attempt. All usage shows up on a single AssemblyAI invoice across providers, with no markup on top of model rates.

What's the difference between LLM Gateway fallbacks and writing my own retry logic?

LLM Gateway fallbacks handle the entire retry-and-route flow inside the Gateway, so your application code makes one request and gets one response—no custom timeout handling, no model-switching logic, no per-provider error mapping. Writing it yourself works for chat apps where a few seconds of retry latency is fine, but in a voice pipeline every second of dead air costs you, and built-in fallbacks fire faster than client-side retries because the Gateway is already inside the network path.

Stream LLM responses in a voice pipeline: Tool calling, structured outputs, and real-time actions

Mart Schweiger — Tue, 12 May 2026 17:59:55 +0000

When a user finishes a sentence in a voice conversation, they expect to hear the agent start replying within roughly a second. Anything longer feels broken. The fastest way to hit that target isn't a faster LLM—it's not waiting for the LLM to finish before you start speaking.

Streaming the LLM response, sentence by sentence, into a TTS engine is the trick that turns a 4-second response time into a sub-second one. And once you're streaming, you can layer on tool calling for real-world actions and structured outputs for predictable downstream code—all without giving up that latency budget.

This tutorial walks through how to build that pipeline using AssemblyAI's LLM Gateway and Universal-3 Pro Streaming. By the end, you'll have a Python voice pipeline that:

Streams microphone audio into AssemblyAI for live transcription
Streams the LLM response token-by-token through LLM Gateway
Calls tools mid-conversation to look up data or trigger actions
Returns structured JSON when the workflow needs predictable output
Hands each completed sentence to TTS as it arrives

Why streaming matters more in voice than in chat

In a chat UI, streaming is a nice-to-have—you see the response appear word by word instead of all at once. In a voice agent, it's the difference between conversational and broken.

The math is simple. End-to-end voice latency is roughly:

STT finalization (200-500 ms)

+ LLM time-to-first-token (150-400 ms)

+ TTS time-to-first-audio (200-400 ms)

+ network overhead (50-150 ms)

= 600-1500 ms before the user hears anything

If you wait for the full LLM response before sending text to TTS, add another 1-3 seconds onto that. Users notice. Conversation breaks. They start over.

If you stream—flushing each completed sentence to TTS as soon as the LLM emits it—the user hears the first sentence while the LLM is still generating the second. End-to-end latency stays inside the 600-900 ms range that feels conversational.

What you'll build

A Python pipeline that handles three voice agent patterns:

Streamed conversational replies—the user asks a question; the agent's voice starts within ~1 second and flows naturally
Tool calling—the user says "what's my order status?"; the agent calls get_order_status(order_id) and speaks the result
Structured outputs—the agent returns a JSON object matching a schema (e.g., {intent, urgency, escalate}), which your code consumes directly without parsing freeform text

Stack:

AssemblyAI Universal-3 Pro Streaming (speech-to-text)
AssemblyAI LLM Gateway (streaming chat completions, tools, structured outputs)
A TTS engine of your choice (we'll use a placeholder—same pattern works with any streaming TTS)
Python 3.9+

Setup

pip install assemblyai requests python-dotenv pyaudio

Create .env:

ASSEMBLYAI_API_KEY=your_key_here

The same AssemblyAI API key authenticates both the streaming STT WebSocket and the LLM Gateway endpoint.

Step 1: Stream tokens from LLM Gateway

LLM Gateway supports OpenAI-style streaming on OpenAI models. Set stream: True in the request and read the response as a Server-Sent Events (SSE) stream. Each chunk contains a partial token; you stitch them together as they arrive.

The key trick for voice: don't wait for the full response. Buffer tokens, watch for sentence boundaries (., !, ?), and flush each completed sentence to TTS the instant it's ready.

import os
import json
import requests
from dotenv import load_dotenv

load_dotenv()
ASSEMBLYAI_API_KEY = os.getenv("ASSEMBLYAI_API_KEY")

def stream_llm_response(user_text: str):
    """
    Stream the LLM response. Yield each completed sentence as it's generated.
    """
    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": ASSEMBLYAI_API_KEY},
        json={
            "model": "gpt-5.2",
            "messages": [
                {"role": "system", "content": "You are a friendly voice assistant. Keep replies short."},
                {"role": "user", "content": user_text},
            ],
            "stream": True,
            "max_tokens": 300,
        },
        stream=True,
        timeout=15,
    )

    buffer = ""
    sentence_endings = (".", "!", "?")

    for line in response.iter_lines():
        if not line:
            continue
        line = line.decode("utf-8")
        if not line.startswith("data: "):
            continue
        data = line[6:]
        if data == "[DONE]":
            if buffer.strip():
                yield buffer.strip()
            return

        chunk = json.loads(data)
        delta = chunk["choices"][0]["delta"].get("content", "")
        if not delta:
            continue
        buffer += delta

        while any(buffer.find(p) != -1 for p in sentence_endings):
            split_idx = max(buffer.rfind(p) for p in sentence_endings)
            sentence = buffer[: split_idx + 1].strip()
            buffer = buffer[split_idx + 1 :]
            if sentence:
                yield sentence

The generator yields each completed sentence as it's ready. Your TTS engine consumes these one at a time:

def speak(sentence: str):
    """Send a sentence to your TTS engine. Replace with your provider's API."""
    print(f"  [TTS] {sentence}")
    # tts_client.stream(sentence)

def handle_final_transcript(user_text: str):
    print(f"User: {user_text}")
    for sentence in stream_llm_response(user_text):
        speak(sentence)

This single change—yielding sentences as they arrive instead of waiting for the full reply—typically cuts perceived response time by 60-80% for any reply longer than two sentences.

Step 2: Add tool calling

Voice agents become useful the moment they can do something—look up an order, check inventory, schedule a callback, transfer to a human. LLM Gateway supports OpenAI-compatible tool calling across every supported model (Claude, OpenAI, Gemini), so you write the code once and it works no matter which provider you route to.

Define your tools:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_order_status",
            "description": "Look up the status of a customer order by order ID.",
            "parameters": {
                "type": "object",
                "properties": {
                    "order_id": {
                        "type": "string",
                        "description": "The order ID, e.g. ORD-12345",
                    }
                },
                "required": ["order_id"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "schedule_callback",
            "description": "Schedule a callback with a sales rep.",
            "parameters": {
                "type": "object",
                "properties": {
                    "phone_number": {"type": "string"},
                    "preferred_time": {"type": "string"},
                },
                "required": ["phone_number", "preferred_time"],
            },
        },
    },
]

Implement the actual functions:

def get_order_status(order_id: str) -> dict:
    return {"order_id": order_id, "status": "shipped", "eta": "2026-05-09"}

def schedule_callback(phone_number: str, preferred_time: str) -> dict:
    return {"confirmation": "CB-9982", "phone": phone_number, "time": preferred_time}

TOOL_REGISTRY = {
    "get_order_status": get_order_status,
    "schedule_callback": schedule_callback,
}

Now extend the LLM call to handle tool requests. The Gateway returns a tool_calls field on the assistant message; you execute each tool, append the result to the conversation history, and call again to let the model produce its spoken response:

def stream_llm_response_with_history(history: list, model: str = "gpt-5.2"):
    """Stream a follow-up reply using the existing conversation history."""
    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": ASSEMBLYAI_API_KEY},
        json={"model": model, "messages": history, "stream": True, "max_tokens": 300},
        stream=True,
        timeout=15,
    )

    buffer = ""
    sentence_endings = (".", "!", "?")
    for line in response.iter_lines():
        if not line:
            continue
        line = line.decode("utf-8")
        if not line.startswith("data: "):
            continue
        data = line[6:]
        if data == "[DONE]":
            if buffer.strip():
                yield buffer.strip()
            return
        chunk = json.loads(data)
        delta = chunk["choices"][0]["delta"].get("content", "")
        if not delta:
            continue
        buffer += delta
        while any(p in buffer for p in sentence_endings):
            split_idx = max(buffer.rfind(p) for p in sentence_endings)
            sentence = buffer[: split_idx + 1].strip()
            buffer = buffer[split_idx + 1 :]
            if sentence:
                yield sentence

def respond_with_tools(user_text: str, history: list):
    history.append({"role": "user", "content": user_text})

    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": ASSEMBLYAI_API_KEY},
        json={
            "model": "claude-sonnet-4-6",
            "messages": history,
            "tools": tools,
            "max_tokens": 500,
        },
    ).json()

    message = response["choices"][0]["message"]
    history.append(message)

    if message.get("tool_calls"):
        for tool_call in message["tool_calls"]:
            fn_name = tool_call["function"]["name"]
            fn_args = json.loads(tool_call["function"]["arguments"])
            result = TOOL_REGISTRY[fn_name](**fn_args)

            history.append({
                "role": "tool",
                "tool_call_id": tool_call["id"],
                "content": json.dumps(result),
            })

        return stream_llm_response_with_history(history, model="gpt-5.2")

    def _yield_once():
        yield message["content"]
    return _yield_once()

stream_llm_response_with_history is the same streaming function from Step 1, except it sends the full conversation history (which now includes the tool result) so the model can speak the answer naturally.

The clean part: tool calling and streaming compose. The model thinks for a moment ("let me check that for you"), executes the tool, and then streams the spoken result token by token—exactly the conversational rhythm users expect.

A note for entity-heavy use cases: if your tool parameters include order IDs, phone numbers, or email addresses, your speech-to-text accuracy on those tokens is what determines whether tool calls succeed. Universal-3 Pro Streaming has roughly 16.7% mixed-entity error rate vs. 23-25% for competing models—that's the difference between ORD-12345 and or 12 three 45 getting passed to your function.

Step 3: Use structured outputs for predictable JSON

Sometimes you don't want a spoken reply—you want machine-readable output your downstream code can act on. Routing decisions, intent classification, sentiment scoring, escalation flags. LLM Gateway supports structured outputs via JSON schema, which guarantees the model returns exactly the shape you specified.

Define the schema:

classification_schema = {
    "type": "object",
    "properties": {
        "intent": {
            "type": "string",
            "enum": ["billing", "support", "sales", "cancel", "other"],
        },
        "urgency": {
            "type": "string",
            "enum": ["low", "medium", "high"],
        },
        "escalate": {"type": "boolean"},
        "summary": {"type": "string"},
    },
    "required": ["intent", "urgency", "escalate", "summary"],
}

Send it with response_format:

def classify_utterance(user_text: str) -> dict:
    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": ASSEMBLYAI_API_KEY},
        json={
            "model": "gpt-5.2",
            "messages": [
                {"role": "system", "content": "Classify the user's intent for a customer service workflow."},
                {"role": "user", "content": user_text},
            ],
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "name": "intent_classification",
                    "schema": classification_schema,
                    "strict": True,
                },
            },
        },
    ).json()

    return json.loads(response["choices"][0]["message"]["content"])

classification = classify_utterance("I want to cancel my subscription right now.")
# {"intent": "cancel", "urgency": "high", "escalate": True, "summary": "..."}

if classification["escalate"]:
    transfer_to_human()

You get back a parsed dict you can route on directly. Pair this with streaming for the user-facing reply: classify the intent (structured), then stream a conversational acknowledgment based on the classification. The user hears a friendly sentence in under a second while your code routes the call in the background.

Step 4: Wire it all together with streaming STT

The full pipeline looks like this—STT WebSocket on the inbound side, streamed LLM Gateway responses on the outbound side, with tool calls and structured outputs available when needed:

from assemblyai.streaming.v3 import (
    StreamingClient,
    StreamingClientOptions,
    StreamingParameters,
    StreamingEvents,
    BeginEvent,
    TurnEvent,
    StreamingError,
)

conversation_history = [
    {"role": "system", "content": "You are a helpful voice assistant."}
]

def on_turn(client: StreamingClient, event: TurnEvent):
    if not event.end_of_turn:
        return

    user_text = event.transcript
    print(f"\nUser: {user_text}")

    classification = classify_utterance(user_text)
    if classification["escalate"]:
        speak("Let me get a human on the line for you.")
        return

    for sentence in respond_with_tools(user_text, conversation_history):
        speak(sentence)

import assemblyai as aai

def main():
    client = StreamingClient(
        StreamingClientOptions(
            api_key=ASSEMBLYAI_API_KEY,
            api_host="streaming.assemblyai.com",
        )
    )
    client.on(StreamingEvents.Turn, on_turn)
    client.connect(
        StreamingParameters(speech_model="u3-rt-pro", sample_rate=16000)
    )

    try:
        client.stream(aai.extras.MicrophoneStream(sample_rate=16000))
    finally:
        client.disconnect(terminate=True)

if __name__ == "__main__":
    main()

Speak into your microphone, ask about an order ID, and watch the agent execute the tool call and stream the spoken reply back. The combination of streaming STT, streaming LLM, and tool calling produces the responsive voice experience users now expect.

When to use which technique

Pattern	Use it when
Streaming reply only	The user asked a question; you want a fast, conversational answer
Tool calling + streamed reply	The agent needs to act on real data (order lookup, scheduling, transfers)
Structured outputs	You need machine-readable output for routing, classification, or downstream logic
Structured + streamed combo	Classify the intent in JSON, then stream a conversational acknowledgment to the user

Skip the wiring with the Voice Agent API

Streaming, tools, structured outputs, and an STT-LLM-TTS pipeline tied together—if you're building a single voice agent and don't need to swap LLM providers per request, AssemblyAI's Voice Agent API bundles all of this behind one WebSocket. You set a system prompt, register tools, and get back streamed audio with built-in turn detection and barge-in. Same Universal-3 Pro Streaming foundation, same fallback patterns, no glue code.

The lower-level approach in this tutorial is the right call when you need maximum control—choosing different LLMs per request, applying custom retry logic, or running structured-output classification in parallel with the spoken reply. Both paths are first-class on AssemblyAI; pick the one that matches your constraint.

Streaming everything is the new baseline for voice. Tool calling and structured outputs are what turn a streaming chatbot into something that can actually do work. Build for both and your voice agent stops feeling like a demo.

Frequently asked questions

What does it mean to stream LLM responses in a voice pipeline?

Streaming LLM responses means receiving and processing the model's output token by token as it's generated, instead of waiting for the full response to complete. In a voice pipeline, streaming lets you forward each completed sentence to a text-to-speech engine the moment the LLM emits it—so the user hears the first sentence of the agent's reply while the LLM is still generating the second. This typically cuts perceived response time by 60–80% for any reply longer than two sentences.

How do I stream LLM responses through AssemblyAI's LLM Gateway?

Set stream: True in your chat/completions request and read the response as a Server-Sent Events (SSE) stream. Each chunk contains a partial token in the choices[0].delta.content field. Buffer tokens, watch for sentence-ending punctuation, and flush each completed sentence to your TTS engine as soon as it's ready. Streaming is supported on OpenAI models in LLM Gateway today.

How does tool calling work with the LLM Gateway?

Tool calling lets your voice agent invoke functions to access data or trigger actions—looking up an order, scheduling a callback, transferring to a human. Define your tools as JSON Schema in the tools array, and when the model decides to call one it returns a tool_calls field on the assistant message. You execute the tool, append the result to the conversation history, and call the Gateway again to let the model produce a spoken response that incorporates the tool output. The schema is OpenAI-compatible, so the same code works across Claude, GPT, and Gemini.

Can I get structured JSON outputs from the LLM Gateway for voice agents?

Yes—LLM Gateway supports structured outputs via JSON schema using the response_format parameter. This guarantees the model returns exactly the shape you specified, which is useful for intent classification, routing decisions, sentiment scoring, and any voice agent workflow that needs machine-readable output your downstream code can consume directly. A common voice pattern is to classify intent in JSON first, then stream a conversational acknowledgment back to the user while your code routes the call in the background.

What's the latency budget for a real-time voice agent using streamed LLM responses?

A well-tuned voice pipeline targets 600–900 ms from the moment the user stops speaking to the moment they hear the agent's first audio. That budget breaks down roughly as: 200–500 ms for STT finalization, 150–400 ms for LLM time-to-first-token, 200–400 ms for TTS time-to-first-audio, and 50–150 ms of network overhead. Streaming everything—STT transcripts, LLM tokens, TTS audio—is what makes hitting that budget achievable.

When should I use the Voice Agent API instead of wiring streaming STT and LLM Gateway separately?

Use the Voice Agent API when you're building a single voice agent and want one WebSocket that handles STT, LLM, TTS, turn detection, and tool calling out of the box. Use the lower-level streaming STT plus LLM Gateway approach when you need more control—choosing different LLMs per request, applying custom retry logic, or running structured-output classification in parallel with the spoken reply. Both options use the same Universal-3 Pro Streaming foundation, so accuracy is identical.

Does streaming work with tool calling and structured outputs?

Yes—streaming composes with both. With tool calling, the agent thinks for a moment, executes the tool, then streams the spoken result token by token. With structured outputs, you typically don't stream the JSON itself (you want the complete object before parsing) but you can stream a separate conversational acknowledgment to the user while the structured classification finalizes in parallel.

Build an AI voice agent for customer support that can look up orders

Mart Schweiger — Tue, 12 May 2026 17:59:18 +0000

Tier-1 customer support is mostly the same five conversations on repeat: where's my order, can I change my address, can I get a refund, when does this ship, can I talk to a human. They're predictable, they're high-volume, and they don't need a person—they need a voice agent that can actually look things up.

This tutorial walks you through building one. By the end, you'll have a Python voice agent that answers calls, listens for an order ID or email, calls into your backend to check the status, and reads the result back to the customer in real time. When something goes off-script, it transfers to a human with the full conversation context attached.

We're using AssemblyAI's Voice Agent API—one WebSocket that handles the speech understanding, LLM reasoning, voice generation, turn detection, and tool calling in a single connection. Total time to a working prototype: about an afternoon.

Why most support voice agents fail

Before we build, it's worth knowing where these things break. The pattern is almost always the same:

Customer says "my order ID is A-B-3-7-9-2"
STT mishears it as "a b 37 92" or "ABE 379 to"
The LLM calls get_order_status("ab3792") or worse, asks the customer to repeat
Customer hangs up

The agent didn't fail because the LLM was wrong. It failed because the speech-to-text layer couldn't capture the entity correctly. This is why entity accuracy on alphanumerics, emails, and phone numbers matters more than overall WER for support agents—and why we're building on Universal-3 Pro Streaming, which has a "16.7% mixed-entity error rate vs. 23-25% for competing models."

The second-most-common failure: dead air during tool calls. The customer asks a question, the agent calls a backend, and there's a 2-3 second silence while the lookup runs. The Voice Agent API solves this by speaking a natural transition phrase ("let me check that for you") while the tool runs—no dead air, no awkward pauses.

What you'll build

A Python voice support agent that handles three real workflows:

Order status lookup—customer says "where's my order?" then the agent asks for the ID, looks it up, and reads back status, ETA, and tracking number
Customer info verification—customer provides email or phone number, the agent looks up the account, and confirms identity before proceeding
Human escalation—customer asks for a person, or the agent gets stuck, and a graceful transfer happens with conversation context preserved

Stack:

AssemblyAI Voice Agent API (one WebSocket: STT + LLM + TTS)
Python 3.9+
A backend with order data—we'll mock it; replace with your real CRM or order management system

Setup

pip install websockets pyaudio python-dotenv

Create .env:

ASSEMBLYAI_API_KEY=your_key_here

The Voice Agent API uses a single endpoint: wss://agents.assemblyai.com/v1/ws. One key, one connection, no separate STT or TTS providers to wire in.

Step 1: Define the support tools

Tools are the agent's interface to your backend. The Voice Agent API uses standard JSON Schema, so anything you can describe with a schema, the agent can call.

For a support agent, you typically want four tools:

import json

TOOLS = [
    {
        "type": "function",
        "name": "get_order_status",
        "description": "Look up an order's current status, shipping ETA, and tracking number by order ID.",
        "parameters": {
            "type": "object",
            "properties": {
                "order_id": {
                    "type": "string",
                    "description": "The customer's order ID, e.g. ORD-12345 or 78231-ABC.",
                },
            },
            "required": ["order_id"],
        },
    },
    {
        "type": "function",
        "name": "lookup_account_by_email",
        "description": "Find a customer account using their email address.",
        "parameters": {
            "type": "object",
            "properties": {
                "email": {"type": "string", "description": "The customer's email address."},
            },
            "required": ["email"],
        },
    },
    {
        "type": "function",
        "name": "list_recent_orders",
        "description": "List the customer's most recent orders. Use after the account is verified.",
        "parameters": {
            "type": "object",
            "properties": {
                "account_id": {"type": "string"},
                "limit": {"type": "integer", "description": "Max number of orders to return.", "default": 5},
            },
            "required": ["account_id"],
        },
    },
    {
        "type": "function",
        "name": "transfer_to_human",
        "description": "Transfer the call to a human agent. Use when the customer asks, when you can't help, or when the issue is sensitive.",
        "parameters": {
            "type": "object",
            "properties": {
                "reason": {"type": "string", "description": "Short reason for the transfer."},
                "summary": {"type": "string", "description": "Brief summary of the conversation so far."},
            },
            "required": ["reason", "summary"],
        },
    },
]

Now implement the actual functions. Replace these stubs with calls to your real backend:

ORDERS_DB = {
    "ORD-12345": {"status": "shipped", "eta": "2026-05-09", "tracking": "1Z999AA10123456784"},
    "ORD-67890": {"status": "processing", "eta": "2026-05-12", "tracking": None},
}

ACCOUNTS_DB = {
    "jane@example.com": {"account_id": "ACC-001", "name": "Jane Doe"},
}

ACCOUNT_ORDERS = {
    "ACC-001": [
        {"order_id": "ORD-12345", "date": "2026-05-01", "total": "$84.99"},
        {"order_id": "ORD-12100", "date": "2026-04-22", "total": "$42.00"},
    ],
}

def run_tool(name: str, args: dict) -> dict:
    if name == "get_order_status":
        order = ORDERS_DB.get(args["order_id"].upper())
        if not order:
            return {"error": "order_not_found", "order_id": args["order_id"]}
        return order

    if name == "lookup_account_by_email":
        account = ACCOUNTS_DB.get(args["email"].lower())
        if not account:
            return {"error": "account_not_found"}
        return account

    if name == "list_recent_orders":
        orders = ACCOUNT_ORDERS.get(args["account_id"], [])
        return {"orders": orders[: args.get("limit", 5)]}

    if name == "transfer_to_human":
        return {"transferred": True, "queue": "support-tier-2"}

    return {"error": f"unknown_tool: {name}"}

The error-shape pattern matters. When get_order_status can't find an order, it returns a structured error rather than throwing—that gives the LLM the context it needs to apologize and ask the customer to verify the ID, instead of crashing the conversation.

Step 2: Write the system prompt

The system prompt is where you encode the agent's behavior. For support, you want a few things every time:

Identity and tone
When to ask for verification before sharing details
When to use which tool
When to transfer to a human
Specific phrasing for transition moments (the "let me check that" line)

SYSTEM_PROMPT = """
You are Avery, a customer support agent for Acme Corp. Your goal is to help customers
quickly and accurately. You have access to tools that let you look up orders and accounts.

Behavior rules:
- Greet warmly and ask how you can help.
- For order questions, ask for the order ID first if the customer hasn't given it.
- If a customer gives an email or phone number, use lookup_account_by_email to verify.
- Read order status, ETA, and tracking number clearly. Don't read raw timestamps —
  say dates naturally (e.g., "Friday, May 9th").
- When you need to call a tool, say a brief transition like "Let me check on that"
  or "One moment while I pull that up."
- If the customer asks for a human, sounds frustrated, or has a complex issue
  (refund disputes, damaged product, billing errors), use transfer_to_human and
  include a short summary.
- Never make up an order ID, status, or tracking number. If a tool returns an error,
  apologize, ask the customer to verify the ID, and try again.
- Keep replies short and conversational. This is a phone call, not an email.
"""

The "never make up" line is the most important sentence in the prompt. Without it, LLMs sometimes invent plausible-sounding tracking numbers when the lookup fails. With it, they ask for clarification instead.

Step 3: Connect to the Voice Agent API

Now the WebSocket connection. The pattern is:

Open wss://agents.assemblyai.com/v1/ws with your API key
Send session.update with the system prompt, tools, voice, and greeting
Wait for session.ready, then start streaming microphone audio
Handle incoming events—tool.call, reply.audio, transcript.user, reply.done

import asyncio
import websockets
import os
import pyaudio

API_KEY = os.getenv("ASSEMBLYAI_API_KEY")
WS_URL = "wss://agents.assemblyai.com/v1/ws"
SAMPLE_RATE = 24000

async def run_agent():
    async with websockets.connect(
        WS_URL,
        additional_headers={"Authorization": f"Bearer {API_KEY}"},
    ) as ws:
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "system_prompt": SYSTEM_PROMPT,
                "greeting": "Hi, this is Avery from Acme support. How can I help?",
                "output": {"voice": "ivy"},
                "tools": TOOLS,
            },
        }))

        pa = pyaudio.PyAudio()
        mic = pa.open(format=pyaudio.paInt16, channels=1, rate=SAMPLE_RATE,
                      input=True, frames_per_buffer=1024)
        speaker = pa.open(format=pyaudio.paInt16, channels=1, rate=SAMPLE_RATE,
                          output=True)

        ready = asyncio.Event()
        pending_tools = []

        async def send_audio():
            await ready.wait()
            import base64
            while True:
                audio = mic.read(1024, exception_on_overflow=False)
                await ws.send(json.dumps({
                    "type": "input.audio",
                    "audio": base64.b64encode(audio).decode(),
                }))
                await asyncio.sleep(0)

        async def handle_messages():
            async for raw in ws:
                event = json.loads(raw)
                t = event.get("type")

                if t == "session.ready":
                    ready.set()
                    print("Agent ready. Start speaking.")

                elif t == "transcript.user":
                    print(f"\nUser: {event['text']}")

                elif t == "transcript.agent":
                    print(f"Agent: {event['text']}")

                elif t == "reply.audio":
                    import base64
                    speaker.write(base64.b64decode(event["data"]))

                elif t == "tool.call":
                    name = event["name"]
                    args = event.get("arguments", {})
                    print(f"  [tool] {name}({args})")
                    result = run_tool(name, args)
                    pending_tools.append({"call_id": event["call_id"], "result": result})

                elif t == "reply.done":
                    if event.get("status") == "interrupted":
                        pending_tools.clear()
                    elif pending_tools:
                        for tool in pending_tools:
                            await ws.send(json.dumps({
                                "type": "tool.result",
                                "call_id": tool["call_id"],
                                "result": json.dumps(tool["result"]),
                            }))
                        pending_tools.clear()

        await asyncio.gather(send_audio(), handle_messages())

if __name__ == "__main__":
    asyncio.run(run_agent())

A few details that the docs flag and you'd otherwise debug for an hour:

Don't send tool.result immediately when you receive tool.call. Accumulate results and send them inside the reply.done handler. Sending too early causes timing issues.
Discard pending tool results on interruption. If the user speaks while the agent is generating a transition phrase, you'll get reply.done with status: "interrupted"—clear the buffer and wait for the next turn.
Voice names are case-sensitive. Use lowercase: ivy, james, mia, winter, bella. An invalid voice returns session.error.

Step 4: Test the three workflows

Run the script and walk through each support scenario. You should hear:

Workflow 1—Order lookup:

You: "Hi, I'm trying to check on order O-R-D 1-2-3-4-5"
Agent: "Sure, let me check on that... I see order ORD-12345. It shipped and is
        on its way — you should have it by Friday, May 9th. The tracking number
        is 1Z999AA10123456784."

Workflow 2—Email-based account lookup:

You: "I forgot my order ID. Can you look me up by email?"
Agent: "Of course. What's the email on the account?"
You: "It's jane at example dot com."
Agent: "One moment... Got it, you're Jane Doe. I see two recent orders:
        ORD-12345 from May 1st for $84.99, and ORD-12100 from April 22nd
        for $42.00. Which one are you asking about?"

Workflow 3—Human transfer:

You: "I just want to talk to a person."
Agent: "I understand. Let me get you over to a teammate now."
[tool.call: transfer_to_human({"reason": "user requested human", "summary": "..."})]

Speak the order ID with hesitation, mumbles, accents, and natural disfluencies—that's where Universal-3 Pro Streaming earns its keep. The agent should still extract the ID correctly because it's tuned for the alphanumeric tokens that voice agents act on.

Step 5: Take it to the phone

This works in your browser through your microphone, but real customer support runs on phones. Twilio Media Streams is the standard bridge—your server accepts the inbound call from Twilio and opens a parallel connection to the Voice Agent API, forwarding audio in both directions.

The Voice Agent API supports audio/pcmu (G.711 u-law at 8 kHz) natively, which matches Twilio's codec exactly. No transcoding, no resampling. The Twilio integration guide walks through the full bridge in about 100 lines of TypeScript.

What to harden before production

Three things you'll want to nail down before pointing this at real customers:

Replace the in-memory mocks with calls to your actual CRM or order management system. Add timeouts and error handling so a slow backend doesn't kill the conversation.
Log everything. Save user transcripts, tool calls, results, and the agent's responses tied to a session ID. Conversation logs are your debugging tool when something goes wrong on call #4,712. Conversation intelligence features like speaker diarization can help you analyze these logs at scale.
Tune turn detection for your acoustic environment. The defaults work for most use cases. For phone audio with background noise, you may want to raise min_end_of_turn_silence_ms slightly so the agent doesn't cut off thoughtful pauses.

Where to go from there

Once the basic order-lookup loop works, the same tool-calling pattern extends to every other support workflow you have: cancel an order, update a shipping address, request a refund, schedule a callback, fetch FAQ answers from a knowledge base. Add the function, describe it in the system prompt, and the agent picks it up—no new infrastructure.

The compounding win: every conversation goes through the same Voice Agent API connection, the same transcription model, the same billing relationship. You're not assembling a new vendor stack; you're adding tools to an agent that already works.

Frequently asked questions

How do I build an AI voice agent for customer support that can look up orders?

Build it on AssemblyAI's Voice Agent API, register a get_order_status function as a tool with JSON Schema, and connect to the WebSocket at wss://agents.assemblyai.com/v1/ws. The agent transcribes the customer's speech, decides when to call your function, executes it through your backend, and speaks the result back—all on a single connection. Most developers ship a working agent in an afternoon because there's no SDK to learn and no separate STT, LLM, or TTS providers to wire together.

Why does speech-to-text accuracy matter so much for support voice agents?

Support agents constantly need to capture alphanumeric tokens—order IDs, account numbers, email addresses, phone numbers—and a single transcription error breaks the workflow. If the STT layer mishears "ORD-12345" as "or 12 three 45," your get_order_status function gets a garbled ID and returns nothing. AssemblyAI's Voice Agent API is built on Universal-3 Pro Streaming, which has a "16.7% mixed-entity error rate vs. 23–25% for competing models"—that's the difference between tool calls that succeed and tool calls that silently fail.

How does tool calling work with the AssemblyAI Voice Agent API?

You register tools by passing an array of function definitions in session.tools on a session.update event. When the agent decides to call a tool, it emits a tool.call event with the function name and arguments. You execute the function and accumulate results, then send tool.result events inside your reply.done handler—not immediately on tool.call. While the tool runs, the agent speaks a brief transition phrase like "let me check that for you" so the conversation never goes silent.

Can I connect AssemblyAI's Voice Agent API to phone calls with Twilio?

Yes—the Voice Agent API supports audio/pcmu (G.711 u-law at 8 kHz) natively, which matches Twilio's codec exactly with no transcoding needed. You set up a server that accepts the inbound Twilio Media Streams call, opens a parallel WebSocket to the Voice Agent API, and forwards audio in both directions. The official Twilio integration guide walks through inbound and outbound calling in about 100 lines of TypeScript.

What's the best way to handle escalation to a human in a customer support voice agent?

Register a transfer_to_human tool with parameters for reason and summary, and instruct the agent in the system prompt to call it when the customer asks for a person, sounds frustrated, or has a complex issue (refund disputes, billing errors, damaged products). The agent generates a short summary of the conversation that you forward to your human queue, so the receiving agent doesn't have to ask the customer to repeat themselves. This is one of the most important workflows to design well—a poor handoff feels worse than no AI at all.

How much does it cost to run a customer support voice agent on AssemblyAI?

The Voice Agent API is $4.50/hr flat—covering speech understanding, LLM reasoning, voice generation, turn detection, and tool calling all in one bill. There are no per-token surcharges, no concurrency caps, and no separate invoices for STT, LLM, and TTS providers. Pricing is billed by the minute on actual conversation duration, and a free tier is available for testing.

Do voice agents built with AssemblyAI work with healthcare workflows?

AssemblyAI offers a BAA for HIPAA workloads and is SOC 2 Type 2, ISO 27001:2022, and PCI DSS v4.0 certified. For clinical use cases (medical front-office voice agents, healthcare contact centers), enable Medical Mode with domain="medical-v1" to improve transcription accuracy on medication names, procedures, conditions, and dosages. Do not point the agent at real PHI without a signed BAA in place.

Building a voice-powered e-commerce shopping assistant

Mart Schweiger — Tue, 12 May 2026 17:57:59 +0000

Voice shopping has crossed an inflection point. Search by typing is being replaced by search by saying—"show me waterproof hiking boots under $150 in size 10," "add the second one to my cart," "what's the return policy on these." For e-commerce teams, that's both an opportunity and a problem: the existing product search and checkout flow was designed for clicks and keystrokes, not natural language.

This tutorial walks through building a voice-powered shopping assistant that customers can actually talk to. By the end, you'll have a Python voice agent that handles four real shopping workflows—product search, add-to-cart, order tracking, and checkout assistance—all on top of a single WebSocket connection using AssemblyAI's Voice Agent API.

The same pattern works whether you're embedding voice into a mobile shopping app, an in-store kiosk, a smart speaker integration, or a phone-based ordering line.

Why voice e-commerce is different from voice support

If you've built a customer support Voice AI agent, the shopping use case looks similar—but the constraints are sharper:

Entity accuracy is everything. Sizes ("size ten and a half"), SKUs ("SKU 9-9-2-1-A"), prices ("under one fifty"), quantities ("get me three of those"). Mishear any of those and you've added the wrong item, the wrong size, or the wrong quantity to a cart someone is about to check out with.
Conversations are exploratory. Support calls have a clear job-to-be-done; shopping conversations meander. The customer browses, narrows, compares, asks about returns, gets distracted, comes back. The agent has to track all of that without losing context.
Stakes shift mid-conversation. "Tell me about this jacket" is low-stakes. "Charge my saved card for $284.50" is not. The agent needs to know when to ask for confirmation and when to just answer.
Accent and code-switching show up. Shoppers globally pronounce brand names, colors, and product categories differently. The agent needs to handle that gracefully.

The Voice Agent API addresses these directly: built on Universal-3 Pro Streaming for high entity accuracy (16.7% mixed-entity error rate vs. 23–25% for competitors), with mid-conversation system prompt updates so you can tighten or loosen the agent's behavior as the customer moves from browsing to buying.

What you'll build

A Python voice shopping assistant that handles four workflows:

Product search—"show me wireless headphones under $200" → searches your catalog → reads back top results
Cart management—"add the second one in black" → adds to cart → confirms
Order tracking—"where's my order from last week?" → looks up customer orders → reads back status
Checkout assistance—guides the user through review and confirmation, never executing payment without explicit verbal "yes"

Stack:

AssemblyAI Voice Agent API (one WebSocket: STT + LLM + TTS)
Python 3.9+
A product catalog and order DB—we'll mock both; replace with your real Shopify, Commerce Cloud, or BigCommerce backend

Setup

pip install websockets pyaudio python-dotenv

# .env
ASSEMBLYAI_API_KEY=your_key_here

Endpoint: wss://agents.assemblyai.com/v1/ws. One key, one connection—the same key works for all AssemblyAI products.

Step 1: Define the shopping tools

The toolset shapes what your agent can do. Start with the four core shopping verbs and grow from there. The Voice Agent API supports tool calling natively, so each tool is defined as a JSON function schema.

import json

TOOLS = [
    {
        "type": "function",
        "name": "search_products",
        "description": "Search the product catalog. Use whenever the customer is browsing or asking what's available.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Free-text query, e.g. 'waterproof hiking boots'"},
                "max_price": {"type": "number"},
                "size": {"type": "string"},
                "color": {"type": "string"},
                "limit": {"type": "integer", "default": 5},
            },
            "required": ["query"],
        },
    },
    {
        "type": "function",
        "name": "get_product_details",
        "description": "Get full details on a specific product, including return policy and stock.",
        "parameters": {
            "type": "object",
            "properties": {"product_id": {"type": "string"}},
            "required": ["product_id"],
        },
    },
    {
        "type": "function",
        "name": "add_to_cart",
        "description": "Add a product to the customer's cart. Confirm size, color, and quantity before calling.",
        "parameters": {
            "type": "object",
            "properties": {
                "product_id": {"type": "string"},
                "variant_id": {"type": "string", "description": "Specific size/color variant"},
                "quantity": {"type": "integer", "default": 1},
            },
            "required": ["product_id", "variant_id"],
        },
    },
    {
        "type": "function",
        "name": "view_cart",
        "description": "Read back the customer's current cart with subtotal.",
        "parameters": {"type": "object", "properties": {}, "required": []},
    },
    {
        "type": "function",
        "name": "remove_from_cart",
        "description": "Remove an item from the cart by line item ID.",
        "parameters": {
            "type": "object",
            "properties": {"line_item_id": {"type": "string"}},
            "required": ["line_item_id"],
        },
    },
    {
        "type": "function",
        "name": "checkout",
        "description": "Submit the order using the customer's saved payment and shipping. ONLY call after explicit verbal 'yes' to a clear confirmation prompt.",
        "parameters": {
            "type": "object",
            "properties": {
                "confirmation_phrase": {
                    "type": "string",
                    "description": "The exact phrase the customer said to confirm, e.g. 'yes, place the order'.",
                }
            },
            "required": ["confirmation_phrase"],
        },
    },
    {
        "type": "function",
        "name": "track_order",
        "description": "Look up the status of a customer order.",
        "parameters": {
            "type": "object",
            "properties": {"order_id": {"type": "string"}},
            "required": ["order_id"],
        },
    },
]

The confirmation_phrase parameter on checkout is the trick that prevents accidental orders. The system prompt tells the agent it can only call checkout if the customer literally says yes—and the parameter forces the agent to record what was said. Your backend can additionally enforce that only a list of accepted phrases ("yes", "place the order", "go ahead") triggers the actual payment.

Step 2: Implement the backend (mocked)

Replace these stubs with calls to your real catalog and order systems.

CATALOG = [
    {
        "product_id": "SKU-2201",
        "name": "Trail Runner 3 hiking boots",
        "price": 139.00,
        "category": "footwear",
        "tags": ["waterproof", "hiking"],
        "variants": [
            {"variant_id": "SKU-2201-BK-10", "size": "10", "color": "black", "stock": 4},
            {"variant_id": "SKU-2201-BK-11", "size": "11", "color": "black", "stock": 0},
            {"variant_id": "SKU-2201-BR-10", "size": "10", "color": "brown", "stock": 7},
        ],
        "return_policy": "30-day free returns",
    },
    {
        "product_id": "SKU-3104",
        "name": "Summit Pro waterproof boots",
        "price": 199.00,
        "category": "footwear",
        "tags": ["waterproof", "hiking", "premium"],
        "variants": [
            {"variant_id": "SKU-3104-BK-10", "size": "10", "color": "black", "stock": 2},
        ],
        "return_policy": "30-day free returns",
    },
]

CART = []  # In production, scope this per session
ORDERS = {
    "ORD-9981": {"status": "shipped", "eta": "2026-05-09", "tracking": "1Z999AA10123456784"},
}

def run_tool(name: str, args: dict) -> dict:
    if name == "search_products":
        results = [
            p for p in CATALOG
            if args["query"].lower() in (p["name"] + " " + " ".join(p["tags"])).lower()
            and (not args.get("max_price") or p["price"] <= args["max_price"])
        ]
        return {"results": results[: args.get("limit", 5)]}

    if name == "get_product_details":
        for p in CATALOG:
            if p["product_id"] == args["product_id"]:
                return p
        return {"error": "product_not_found"}

    if name == "add_to_cart":
        for p in CATALOG:
            for v in p["variants"]:
                if v["variant_id"] == args["variant_id"]:
                    if v["stock"] < args.get("quantity", 1):
                        return {"error": "out_of_stock", "available": v["stock"]}
                    line = {
                        "line_item_id": f"LI-{len(CART) + 1}",
                        "product_id": p["product_id"],
                        "variant_id": v["variant_id"],
                        "name": p["name"],
                        "size": v["size"],
                        "color": v["color"],
                        "quantity": args.get("quantity", 1),
                        "price": p["price"],
                    }
                    CART.append(line)
                    return {"added": line, "cart_size": len(CART)}
        return {"error": "variant_not_found"}

    if name == "view_cart":
        subtotal = sum(item["price"] * item["quantity"] for item in CART)
        return {"items": CART, "subtotal": round(subtotal, 2)}

    if name == "remove_from_cart":
        global CART
        CART = [item for item in CART if item["line_item_id"] != args["line_item_id"]]
        return {"removed": args["line_item_id"], "cart_size": len(CART)}

    if name == "checkout":
        accepted = ["yes", "place the order", "go ahead", "confirm", "buy it"]
        if not any(phrase in args["confirmation_phrase"].lower() for phrase in accepted):
            return {"error": "confirmation_unclear", "phrase_received": args["confirmation_phrase"]}
        return {"order_id": "ORD-9982", "total": sum(i["price"] * i["quantity"] for i in CART)}

    if name == "track_order":
        order = ORDERS.get(args["order_id"].upper())
        return order or {"error": "order_not_found"}

    return {"error": f"unknown_tool: {name}"}

Step 3: Write a shopping-aware system prompt

Shopping system prompts should encode three patterns: how to describe products on a phone (terse, scannable), how to gather variant info (size, color, quantity) before adding to cart, and how to confirm checkout.

SYSTEM_PROMPT = """
You are Riley, a friendly voice shopping assistant for Trailgear, an outdoor retailer.

Behavior rules:

PRODUCT SEARCH
- When customers ask about products, call search_products with a clean query.
- Read back top 2-3 results conversationally. Don't list more than 3 unless asked.
- Format prices naturally: "one hundred thirty-nine dollars" not "139.00".
- Mention only the most relevant detail per product (price + key feature). Save the rest for follow-ups.

VARIANT SELECTION
- Before adding to cart, confirm size, color, and quantity. Never assume.
- If a variant is out of stock, say so immediately and offer the closest alternative.
- Read sizes naturally: "size ten" not "size 10".

CART MANAGEMENT
- After adding, briefly confirm what was added and the new cart size.
- If the customer asks "what's in my cart," call view_cart and read it back with subtotal.

CHECKOUT
- Before calling checkout, summarize the cart and explicitly ask: "Should I place the order?"
- ONLY call checkout if the customer responds with a clear yes (e.g., "yes," "place it," "go ahead," "confirm").
- If the response is ambiguous, ask again. Do not interpret "sure I think so" as confirmation.
- After checkout succeeds, read back the order ID slowly so the customer can write it down.

ORDER TRACKING
- For order status questions, ask for the order ID.
- When reading a tracking number, slow down and group digits in pairs.

GENERAL
- Keep replies short and conversational. This is voice, not chat.
- When you call a tool, say a brief transition like "Let me look that up."
- Never invent products, prices, or stock — if the catalog doesn't have it, say so.
"""

The "explicit yes" pattern is what makes this safe to point at production payments. The agent's prompt forbids it from calling checkout on ambiguous responses, and the backend independently validates the confirmation phrase. Belt and suspenders.

Step 4: Wire the WebSocket

This is essentially the same WebSocket loop as a support agent—the difference is in the tools and prompt, not the protocol. If you've already built a voice agent with function calling, this will look familiar.

import asyncio
import os
import base64
import websockets
import pyaudio

API_KEY = os.getenv("ASSEMBLYAI_API_KEY")
WS_URL = "wss://agents.assemblyai.com/v1/ws"
SAMPLE_RATE = 24000

async def run_assistant():
    async with websockets.connect(
        WS_URL, additional_headers={"Authorization": f"Bearer {API_KEY}"},
    ) as ws:
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "system_prompt": SYSTEM_PROMPT,
                "greeting": "Hi, this is Riley from Trailgear. What can I help you find today?",
                "output": {"voice": "mia"},
                "tools": TOOLS,
            },
        }))

        pa = pyaudio.PyAudio()
        mic = pa.open(format=pyaudio.paInt16, channels=1, rate=SAMPLE_RATE,
                      input=True, frames_per_buffer=1024)
        speaker = pa.open(format=pyaudio.paInt16, channels=1, rate=SAMPLE_RATE,
                          output=True)

        ready = asyncio.Event()
        pending_tools = []

        async def send_audio():
            await ready.wait()
            while True:
                data = mic.read(1024, exception_on_overflow=False)
                await ws.send(json.dumps({
                    "type": "input.audio",
                    "audio": base64.b64encode(data).decode(),
                }))
                await asyncio.sleep(0)

        async def handle_messages():
            async for raw in ws:
                event = json.loads(raw)
                t = event.get("type")

                if t == "session.ready":
                    ready.set()
                    print("Riley is ready.")
                elif t == "transcript.user":
                    print(f"\nCustomer: {event['text']}")
                elif t == "transcript.agent":
                    print(f"Riley: {event['text']}")
                elif t == "reply.audio":
                    speaker.write(base64.b64decode(event["data"]))
                elif t == "tool.call":
                    print(f"  [tool] {event['name']}({event.get('arguments', {})})")
                    result = run_tool(event["name"], event.get("arguments", {}))
                    pending_tools.append({"call_id": event["call_id"], "result": result})
                elif t == "reply.done":
                    if event.get("status") == "interrupted":
                        pending_tools.clear()
                    elif pending_tools:
                        for tool in pending_tools:
                            await ws.send(json.dumps({
                                "type": "tool.result",
                                "call_id": tool["call_id"],
                                "result": json.dumps(tool["result"]),
                            }))
                        pending_tools.clear()

        await asyncio.gather(send_audio(), handle_messages())

if __name__ == "__main__":
    asyncio.run(run_assistant())

Step 5: Test the four workflows

Run the script and walk through each shopping flow:

Search:

You: "I'm looking for waterproof hiking boots under one fifty."
Riley: "Let me check our catalog... I've got the Trail Runner 3 at $139,
        which is waterproof and great for day hikes, and the Summit Pro
        at $199 — that's premium with extra ankle support. Want to hear
        more about either?"

Add to cart:

You: "The Trail Runner threes — size ten, in black."
Riley: "Got it, the Trail Runner 3 in black, size ten. How many?"
You: "Just one."
Riley: "One moment... Added. That's one item in your cart."

Checkout:

You: "Okay let's check out."
Riley: "Sure. You have one Trail Runner 3 in black, size ten, for $139.
        Should I place the order?"
You: "Yes, place it."
Riley: "Order placed. Your order ID is O-R-D 9-9-8-2."

Track order:

You: "Where's my order from last week — ORD 9-9-8-1?"
Riley: "Let me check... ORD-9981 has shipped and should arrive Friday,
        May 9th. Tracking is 1Z 99 9A A1 01 23 45 67 84."

The tracking number readback is intentional—grouping digits in pairs is a common voice pattern that makes long alphanumerics easier to write down.

Where this gets harder in production

Two patterns to plan for once the basic loop works:

Personalization. Authenticated shoppers expect the agent to know their saved address, recent purchases, and size preferences. Add a get_customer_profile() tool gated on session auth. Use the result in the system prompt via mid-conversation session.update so the agent personalizes without re-asking.
Multi-turn refinement. "Show me hiking boots" → "in waterproof" → "size ten only" → "under $150." Each refinement should narrow the same result set rather than triggering a fresh search. Pass a session_filters object as a tool parameter and have the agent accumulate filters across turns.

Where to take it from here

The same architecture extends to:

In-store kiosks for hands-free product search
Phone-based ordering lines for restaurants, takeout, and reorders (Twilio Media Streams + the Voice Agent API Twilio integration)
Mobile shopping apps with a press-and-hold voice button
Smart speaker integrations that hand off to your agent when the user wants to shop

What stays the same across all of those: one WebSocket, one system prompt, one tool registry. The voice agent is the same regardless of the front-end channel.

Voice shopping isn't replacing search bars or product pages—it's running alongside them, picking up the conversational moments those interfaces can't handle. Build for the conversational moments and the rest of your funnel benefits from it. For teams building AI-powered customer service workflows, the same voice agent architecture handles both pre-sale shopping and post-sale support.

Frequently asked questions

How do I build a voice-powered shopping assistant for e-commerce?

Build it on AssemblyAI's Voice Agent API and register the four core shopping verbs as tools: search_products, add_to_cart, view_cart, and checkout. The agent transcribes the customer's speech, calls your catalog and cart functions, and speaks the result back—all on a single WebSocket. Most developers have a working voice shopping assistant running the same day, with no SDK to install and no separate STT, LLM, or TTS providers to manage.

Can a voice shopping assistant handle product variants like size, color, and quantity?

Yes—define the variant fields as parameters on your add_to_cart tool (e.g., variant_id, quantity) and instruct the agent in the system prompt to confirm size, color, and quantity before calling the function. The Voice Agent API is built on Universal-3 Pro Streaming, which has industry-leading accuracy on alphanumeric tokens like sizes, SKUs, and quantities—that's what makes "size ten and a half" reliably parse as 10.5 instead of 10 or 1010.

How do I prevent accidental orders in a voice checkout flow?

Use a two-layer pattern: the system prompt instructs the agent to only call the checkout tool after an explicit verbal "yes" to a clear confirmation prompt, and the checkout function itself accepts a confirmation_phrase parameter that your backend independently validates against an accepted list ("yes," "place the order," "go ahead," "confirm"). This belt-and-suspenders design ensures ambiguous responses like "sure I think so" never trigger a real charge.

What channels can I deploy a voice shopping assistant on?

The same Voice Agent API connection powers in-app voice (mobile or web with a press-and-hold button), in-store kiosks for hands-free product search, phone-based ordering lines via Twilio Media Streams, and smart speaker integrations that hand off to your agent for shopping. The system prompt and tool registry stay the same across channels—only the front-end audio path changes.

How does the AssemblyAI Voice Agent API compare to Vapi or Retell for e-commerce?

The Voice Agent API is infrastructure rather than a platform—it gives you a standard JSON WebSocket with full control over conversation design, tool integrations, and agent behavior, so your voice shopping experience can feel uniquely yours instead of like every other agent built on a no-code platform. Vapi and Retell are higher-level platforms that work well for non-technical configuration but constrain custom integrations and agent personality. For e-commerce teams that already have engineering capacity, the Voice Agent API is typically a better fit.

How do I personalize a voice shopping assistant for authenticated customers?

Add a get_customer_profile tool that returns the customer's saved address, payment, recent purchases, and size preferences, gated on session auth. The Voice Agent API supports mid-conversation session.update events, so you can update the system prompt with the customer's context after they authenticate without dropping the connection. The agent can then personalize recommendations, default to known sizes, and skip questions like "what's your shipping address?"

How much does it cost to run a voice shopping assistant on AssemblyAI?

The Voice Agent API is $4.50/hr flat-rate, covering STT, LLM, voice generation, turn detection, and tool calling on a single bill. There are no per-token surcharges, no concurrency caps, and no separate invoices for the STT, LLM, and TTS layers—pricing is billed by the minute on actual conversation duration. A free tier with $50 in starter credits is available for testing.

How to build an AI scribe for therapy sessions

Mart Schweiger — Tue, 12 May 2026 17:57:52 +0000

Therapy sessions are some of the hardest audio to scribe. Two people talking, often quietly, sometimes overlapping. Long therapeutic pauses that aren't end-of-turn. Clinical terminology mixed with informal speech. Medication names that sound like other medication names. PHI that absolutely cannot leak. And a clinician who needs to be present with their client—not typing.

Most general-purpose transcription products fail at therapy because they were built for meetings or podcasts. Long pauses get treated as session ends. Mumbled mid-sentence corrections lose the original word. Drug names get mangled. And the post-session note-writing burden—DAP, SOAP, or BIRP format depending on the practice—still falls on the therapist.

This tutorial walks through building a therapy-grade AI scribe that does three things well:

Ambient session capture with Universal-3 Pro Streaming and Medical Mode, tuned for therapy-specific speech patterns
Speaker-attributed transcripts that distinguish therapist from client using speaker diarization
Conversational note generation with AssemblyAI's Voice Agent API—the clinician can dictate additions, ask questions about the session, and generate the formatted note all by voice

We'll cover HIPAA considerations throughout. AssemblyAI offers a Business Associate Agreement (BAA) for customers processing PHI, and is SOC 2 Type 2, ISO 27001:2022, and PCI DSS v4.0 certified. The architecture below is designed with PHI handling in mind—but a BAA is a contractual prerequisite for using this with real patient data.

The architecture

Two phases, two AssemblyAI products, one continuous pipeline:

Phase 1—During the session (ambient):

Streaming speech-to-text captures the conversation with domain="medical-v1" (Medical Mode) and speaker_labels=true
Tuned turn-detection settings prevent therapeutic pauses from triggering false end-of-turns
Transcripts are buffered locally—never written to disk in plaintext outside the clinician's environment

Phase 2—After the session (conversational):

The clinician interacts with a Voice Agent API session that has the transcript loaded as context
The agent can: read back specific moments, generate DAP/SOAP/BIRP notes, identify themes, suggest follow-ups, accept dictated additions
Notes are produced as structured output and pushed to the EHR

The clinician never writes a note. They speak to the scribe.

What you'll build

A Python prototype that:

Captures a live therapy session via microphone with Medical Mode and speaker diarization
Persists a structured transcript with therapist/client roles
Spins up a post-session Voice Agent that can answer questions about the session and generate a DAP note via tool call

Stack:

AssemblyAI Universal-3 Pro Streaming with Medical Mode
AssemblyAI Voice Agent API (post-session note generation)
AssemblyAI LLM Gateway (structured note generation)
Python 3.9

pip install assemblyai websockets pyaudio python-dotenv requests

# .env
ASSEMBLYAI_API_KEY=your_key_here

Before using with real PHI: request a BAA from AssemblyAI, run the pipeline in your own controlled environment, and ensure the transcript persistence layer meets your organization's PHI requirements.

Step 1: Capture the session with Medical Mode

The default streaming configuration is tuned for fast-paced voice agent dialogues. Therapy is the opposite—long therapeutic pauses are normal and meaningful. The Medical Mode docs recommend conservative turn detection settings for clinical audio: raise min_turn_silence to 800 ms and max_turn_silence to 3600 ms.

import os
from dotenv import load_dotenv
from assemblyai.streaming.v3 import (
    StreamingClient,
    StreamingClientOptions,
    StreamingParameters,
    StreamingEvents,
    BeginEvent,
    TurnEvent,
    TerminationEvent,
    StreamingError,
)

load_dotenv()
API_KEY = os.getenv("ASSEMBLYAI_API_KEY")

# In-memory transcript buffer for the live session.
# In production: write to encrypted storage with PHI safeguards.
session_transcript = []

def on_begin(client: StreamingClient, event: BeginEvent):
    print(f"Session started: {event.id}")

def on_turn(client: StreamingClient, event: TurnEvent):
    if not event.end_of_turn:
        return
    speaker = event.speaker_label or "unknown"
    role = "Therapist" if speaker == "A" else "Client"
    entry = {"role": role, "text": event.transcript}
    session_transcript.append(entry)
    print(f"[{role}] {event.transcript}")

def on_error(client: StreamingClient, error: StreamingError):
    print(f"STT error: {error}")

def on_terminated(client: StreamingClient, event: TerminationEvent):
    print(f"Session ended. Total turns: {len(session_transcript)}")

def start_session_capture():
    client = StreamingClient(
        StreamingClientOptions(
            api_key=API_KEY,
            api_host="streaming.assemblyai.com",
        )
    )
    client.on(StreamingEvents.Begin, on_begin)
    client.on(StreamingEvents.Turn, on_turn)
    client.on(StreamingEvents.Error, on_error)
    client.on(StreamingEvents.Termination, on_terminated)

    client.connect(
        StreamingParameters(
            speech_model="u3-rt-pro",
            sample_rate=16000,
            domain="medical-v1",
            speaker_labels=True,
            min_turn_silence=800,
            max_turn_silence=3600,
            keyterms_prompt=[
                "cognitive behavioral therapy", "CBT", "EMDR",
                "GAD-7", "PHQ-9", "DSM-5",
                "sertraline", "fluoxetine", "buspirone",
                "panic attack", "intrusive thought",
            ],
        )
    )
    return client

A few details worth pointing out:

Medical Mode (domain="medical-v1") is purpose-built for clinical accuracy. It corrects medication names like lisprohumalog to Lispro (Humalog)—the standard medical convention—and improves accuracy on procedures, conditions, and dosages. In therapy it covers SSRIs, common assessments, and DSM-5 terminology.
speaker_labels=true tags each turn with a speaker ID. With two-person therapy sessions, that maps cleanly to therapist (Speaker A) and client (Speaker B). For couples or family therapy, see the speaker identification docs for additional speakers.
keyterms_prompt boosts recognition for terms specific to your modality. For trauma practices, add EMDR-specific vocabulary; for psychiatric practices, add the medication formulary you actually prescribe.

What you don't see here but matters in production: the transcript buffer should never persist outside an environment covered by your BAA. In-memory during the session, encrypted-at-rest after—and only if your storage layer meets your organization's PHI requirements.

Step 2: Add PII redaction for the persisted transcript

For research, audit, or training purposes you may want to keep a redacted version of the session. AssemblyAI's PII redaction handles this for streaming transcripts. For pre-recorded post-session re-processing (if you want to go back and re-transcribe with higher-accuracy pre-recorded models), the redaction options are richer:

import requests

def redact_post_session(audio_url: str):
    """
    Re-process the recorded session audio with PII redaction
    and entity detection. Use this for the persisted record.
    """
    response = requests.post(
        "https://api.assemblyai.com/v2/transcript",
        headers={"authorization": API_KEY},
        json={
            "audio_url": audio_url,
            "speech_models": ["universal-3-pro", "universal-2"],
            "domain": "medical-v1",
            "speaker_labels": True,
            "speakers_expected": 2,
            "redact_pii": True,
            "redact_pii_policies": [
                "person_name",
                "date_of_birth",
                "phone_number",
                "email_address",
                "us_social_security_number",
                "credit_card_number",
            ],
            "redact_pii_sub": "hash",
            "redact_pii_audio": True,    # Returns redacted audio URL with PII silenced
            "entity_detection": True,
        },
    )
    return response.json()

The redact_pii_audio: True flag returns a redacted version of the audio file with PII silenced—essential if you ever need to share clips for supervision or QA.

Step 3: Generate the DAP note with LLM Gateway

DAP (Data, Assessment, Plan) is the dominant therapy note format in mental health practices, alongside SOAP and BIRP. LLM Gateway with structured outputs gives you predictable JSON output that maps directly into your EHR. The chat completions endpoint handles the request.

import requests
import json

DAP_SCHEMA = {
    "type": "object",
    "properties": {
        "data": {
            "type": "string",
            "description": "Objective observations from the session — what the client said, body language, presentation. No interpretation.",
        },
        "assessment": {
            "type": "string",
            "description": "Clinical assessment — themes, progress toward goals, risk factors, response to interventions.",
        },
        "plan": {
            "type": "string",
            "description": "Clinical plan — interventions used, homework assigned, next session focus.",
        },
        "risk_flags": {
            "type": "array",
            "items": {"type": "string"},
            "description": "Any safety concerns, suicidal/homicidal ideation, or escalations noted.",
        },
        "themes": {
            "type": "array",
            "items": {"type": "string"},
            "description": "2-4 short clinical themes (e.g. 'workplace anxiety', 'sleep disruption').",
        },
    },
    "required": ["data", "assessment", "plan", "risk_flags", "themes"],
}

def generate_dap_note(transcript: list) -> dict:
    transcript_text = "\n".join(
        f"{entry['role']}: {entry['text']}" for entry in transcript
    )

    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers={"authorization": API_KEY},
        json={
            "model": "claude-sonnet-4-6",
            "messages": [
                {
                    "role": "system",
                    "content": (
                        "You are an experienced clinical documentation assistant. "
                        "You generate DAP-format therapy notes from session transcripts. "
                        "Be precise, professional, and clinically appropriate. "
                        "Never invent information not present in the transcript. "
                        "If the transcript indicates risk (SI/HI, abuse, etc.), "
                        "flag it explicitly in risk_flags."
                    ),
                },
                {"role": "user", "content": f"Generate a DAP note for this session:\n\n{transcript_text}"},
            ],
            "response_format": {
                "type": "json_schema",
                "json_schema": {"name": "dap_note", "schema": DAP_SCHEMA, "strict": True},
            },
            "fallbacks": [
                {"model": "gpt-5.1"},
                {"model": "gemini-2.5-pro"},
            ],
            "fallback_config": {"depth": 2},
        },
    )

    return json.loads(response.json()["choices"][0]["message"]["content"])

Three production-grade choices baked into this:

Structured output via JSON schema. Guarantees the response shape—your EHR mapping code never has to parse freeform clinical text.
fallbacks chain. If the primary Claude model is overloaded, the Gateway transparently retries with GPT-5.1, then Gemini 2.5 Pro. A clinician finishing their day shouldn't have to wait or retry because of an upstream provider hiccup.
Explicit "never invent" instruction. The single most important sentence in the system prompt. Without it, LLMs sometimes generate plausible-sounding but fabricated clinical content. With it, they leave fields shorter and more conservative.

The note is generated. The clinician reviews. Nothing goes into the patient record without explicit approval—that's a non-negotiable design pattern for any AI medical scribe.

Step 4: Conversational scribe interaction with the Voice Agent API

Now the part that makes this a true scribe rather than a transcription tool: after the session, the clinician opens a Voice Agent connection and just talks to the scribe.

The agent is configured with the session transcript loaded as system context, plus tools for the actions a therapist actually wants to take post-session: generate the note, search the transcript, add a dictated addendum, push to the EHR.

SCRIBE_TOOLS = [
    {
        "type": "function",
        "name": "generate_dap_note",
        "description": "Generate a DAP-format therapy note from the current session transcript.",
        "parameters": {"type": "object", "properties": {}, "required": []},
    },
    {
        "type": "function",
        "name": "search_session",
        "description": "Search the session transcript for what the client said about a topic.",
        "parameters": {
            "type": "object",
            "properties": {"topic": {"type": "string"}},
            "required": ["topic"],
        },
    },
    {
        "type": "function",
        "name": "add_addendum",
        "description": "Add a dictated addendum to the session note (e.g., a clinical observation the therapist remembered after the fact).",
        "parameters": {
            "type": "object",
            "properties": {"text": {"type": "string"}},
            "required": ["text"],
        },
    },
    {
        "type": "function",
        "name": "save_to_ehr",
        "description": "Save the finalized note to the EHR. Only call after the clinician confirms.",
        "parameters": {
            "type": "object",
            "properties": {"confirmation": {"type": "string"}},
            "required": ["confirmation"],
        },
    },
]

The system prompt for the scribe agent:

def scribe_system_prompt(transcript: list) -> str:
    transcript_text = "\n".join(f"{e['role']}: {e['text']}" for e in transcript)
    return f"""
You are a clinical documentation assistant for a licensed therapist. The therapist
just finished a session. Below is the session transcript.

Your role:
- Help the therapist generate, refine, and finalize the session note.
- Answer questions about what was said in the session.
- Accept dictated addenda.
- Never invent content that wasn't in the session.
- Push to EHR ONLY when the therapist explicitly confirms.

Tone: professional, brief, never chatty. The therapist is between clients.

Session transcript:
---
{transcript_text}
---
"""

When the clinician finishes the session, they open the scribe Voice Agent connection. A natural interaction looks like:

Therapist: "Generate the DAP note for this session."
Scribe:    "One moment... I have a draft. Want me to read it, or
            should I push it to the EHR?"
Therapist: "Read me the assessment section."
Scribe:    "Assessment: client reported continued anxiety triggered by
            workplace conflict, with a notable improvement in sleep over
            the past two weeks following sleep hygiene homework. Engaged
            well with cognitive restructuring exercise around catastrophic
            thinking. No safety concerns noted."
Therapist: "Add an addendum: client mentioned a possible elevation in
            symptoms after the meeting on Friday — flag for next session."
Scribe:    "Added. Anything else?"
Therapist: "Push it to the EHR."
Scribe:    "Confirming — should I save this note to the EHR?"
Therapist: "Yes, save it."
Scribe:    "Saved."

The Voice Agent API connection is the same one we covered in the support and shopping tutorials—wss://agents.assemblyai.com/v1/ws, system prompt, tools, voice. The difference is that the system prompt carries the session transcript as context, and the tools are scribe-specific.

What this changes for therapists

Three things, in order of how often clinicians mention them:

Eye contact comes back. When the scribe is ambient and the note is generated by voice afterward, the therapist isn't typing during the session. Clients notice. Multiple ambient AI scribe deployments in adjacent specialties report patient engagement gains tied to therapists not staring at a screen.
"Pajama time" goes away. The hours of post-session note-writing—done at home after the kids are in bed—collapse into a five-minute review of an AI-drafted note. The Permanente Medical Group has reported thousands of physician hours saved at scale with ambient AI scribes; the same principle applies to mental health caseloads.
The note quality is consistent. Variability in note quality across a long day is real. An AI scribe writes the 11am note with the same care as the 5pm note.

What to harden before clinical use

BAA in place. Do not run with real PHI without a Business Associate Agreement signed with AssemblyAI.
Encrypted transcript storage. Session transcripts are PHI. Storage layer needs to match.
Clinician sign-off on every note. AI-drafted, human-approved. No exceptions.
Audit trail. Every note edit, addendum, and save-to-EHR action should be logged to a tamper-evident store.
Retention policy. Document explicitly how long transcripts are kept, who can access them, and when they're deleted.

Where to take it from there

The same pattern extends to other formats—SOAP notes for primary care therapy referrals, BIRP for behavioral health, group therapy notes with multi-speaker diarization. Swap the schema, swap the keyterms, keep the architecture.

For practices that want a single deployment:

Solo practitioners can run the whole pipeline locally and push to a single EHR
Group practices can host the scribe centrally with per-clinician auth and route to multiple EHRs via the save_to_ehr tool
Telehealth platforms can wire the streaming STT into the existing call audio path with no change to the client-facing UI—an approach that works well for medical speech-to-text in virtual care settings

The architecture is the same. The integrations differ.

The shape of clinical documentation is changing. The therapists who get the time back are the ones whose practices ship a scribe that actually works in their workflow—and the workflow is voice in, voice out, EHR-ready note in the middle.

Frequently asked questions

How do I build an AI scribe for therapy sessions?

Build it as a two-phase pipeline: ambient session capture with AssemblyAI's Universal-3 Pro Streaming model in Medical Mode (domain="medical-v1") with speaker diarization, then post-session note generation through LLM Gateway with a structured JSON schema for DAP, SOAP, or BIRP format. The clinician interacts with the scribe conversationally through the Voice Agent API after the session—dictating addenda, asking questions about the transcript, and pushing the finalized note to the EHR by voice.

Does AssemblyAI support HIPAA workloads for therapy and clinical documentation?

AssemblyAI offers a Business Associate Agreement (BAA) for customers processing Protected Health Information and is SOC 2 Type 2, ISO 27001:2022, and PCI DSS v4.0 certified. A BAA is a contractual prerequisite for using AssemblyAI with real PHI, including therapy session recordings, transcripts, and clinical notes. Contact AssemblyAI sales to start a BAA conversation before deploying into a regulated environment.

What is Medical Mode and why does it matter for therapy transcription?

Medical Mode is an AssemblyAI add-on enabled with domain="medical-v1" that improves transcription accuracy for medical terminology—medication names, procedures, conditions, dosages, and clinical assessments. For therapy specifically, it correctly transcribes SSRIs (sertraline, fluoxetine), assessment instruments (GAD-7, PHQ-9), and DSM-5 terminology that general-purpose transcription models frequently mishear. It works with all of AssemblyAI's pre-recorded and streaming speech-to-text models.

How do I handle the long pauses common in therapy sessions without triggering false end-of-turns?

Raise the streaming turn-detection thresholds—set min_turn_silence to 800 ms and max_turn_silence to 3600 ms (the conservative settings recommended in the Medical Mode docs). The default streaming configuration is tuned for fast-paced voice agent dialogues, which would incorrectly fragment therapeutic pauses into separate turns. With the conservative settings, clients can pause for several seconds to think without the system breaking the turn.

Can an AI scribe distinguish between therapist and client in a session recording?

Yes—enable speaker diarization with speaker_labels=True and the streaming API will tag each turn with a speaker ID. For two-person therapy sessions, that maps cleanly to therapist (Speaker A) and client (Speaker B). For couples or family therapy with three or more speakers, AssemblyAI's Speaker Identification feature can identify speakers by their actual name or role.

What format should AI-generated therapy notes use—DAP, SOAP, or BIRP?

DAP (Data, Assessment, Plan) is the dominant format in mental health practices, but SOAP and BIRP are also widely used depending on the modality and payer requirements. With LLM Gateway's structured output support, you define a JSON schema for the format you want and the model returns exactly that shape—making it trivial to switch between DAP, SOAP, and BIRP by swapping schemas. Whichever format you choose, the AI generates a draft and the clinician reviews and approves before anything goes into the patient record.

Can I redact PII from a therapy session transcript?

Yes—enable PII redaction with redact_pii=True and specify the PII policies you want to detect (person names, dates of birth, phone numbers, email addresses, social security numbers, credit card numbers). For pre-recorded post-session re-processing, AssemblyAI also supports redact_pii_audio=True, which returns a redacted version of the audio file with PII silenced—useful if you ever need to share clips for clinical supervision or quality assurance.

How does an AI scribe interact with the clinician after the session?

The clinician opens a Voice Agent API connection and talks to the scribe directly—saying things like "generate the DAP note," "read me the assessment section," "add an addendum: client mentioned elevation in symptoms after Friday's meeting," or "push it to the EHR." The agent has the session transcript loaded as system prompt context and tools registered for note generation, search, addenda, and EHR push, so the entire post-session documentation workflow happens by voice without typing.

Master Golang: 5 Hands-on Labs for Constants, Control Flow, and Data Structures

Labby — Tue, 12 May 2026 17:38:25 +0000

Go is renowned for its simplicity and performance, but true mastery requires more than just reading documentation. To become a proficient Go developer, you must internalize how the language handles memory, data organization, and logic flow. This curated learning path provides a hands-on, interactive environment where you can write, test, and debug Go code directly in your browser, moving beyond theory into practical application.

Go Constants Fundamentals

Difficulty: Beginner | Time: 20 minutes

Learn the essentials of constants in Go, including declaration, iota usage, and best practices for defining immutable values in Go programming.

Practice on LabEx → | Tutorial →

If Branch Statement in Golang

Difficulty: Beginner | Time: 20 minutes

Learn Go programming's if statement variations, including basic conditionals, else-if branches, initialization, and formatting techniques for effective control flow.

Practice on LabEx → | Tutorial →

Go Dictionary Fundamentals

Difficulty: Beginner | Time: 50 minutes

Learn the essentials of Go dictionaries (maps), including declaration, initialization, manipulation, and iteration techniques for efficient key-value data management.

Practice on LabEx → | Tutorial →

Golang Slice Data Structures

Difficulty: Beginner | Time: 40 minutes

Learn the fundamentals of Golang slices, exploring their structure, operations, and key techniques for efficient data manipulation and management.

Practice on LabEx → | Tutorial →

Array Operations in Golang

Difficulty: Beginner | Time: 35 minutes

Learn essential array operations in Go, including initialization, traversal, element access, and understanding array characteristics in a practical, hands-on lab.

Practice on LabEx → | Tutorial →

Theory is only the beginning. By completing these five labs, you will have built a solid foundation in Go's core syntax and data management techniques. Whether you are preparing for a backend engineering role or building your first microservice, these hands-on exercises provide the muscle memory needed to write efficient, idiomatic Go code. Start your journey today and transform your understanding of the language through practice.

Building Push Notifications in GusLift

Abdul-Salam Zakaria — Tue, 12 May 2026 17:37:00 +0000

GusLift connects student drivers with riders heading the same way. The matching itself happens over a WebSocket, which is fine when both people are staring at the app. The problem is that most of the time they aren't. Someone requests a ride, locks their phone, and the driver on the other side of campus has no idea anyone is waiting.

This post walks through how push notifications were wired into GusLift across the three services that make up the product: the Expo mobile app, the Next.js backend on Cloudflare, and the matching worker (a Durable Object on Cloudflare Workers).

The shape of the problem

There are exactly two moments where a push notification meaningfully changes user behavior:

A driver picks a rider from the waiting list. The rider needs to know to open the app and accept.
The rider accepts. The driver needs to know the ride is locked in.

Everything else, like seat counts updating or another driver joining the slot, is noise. Sending push for those would train users to ignore the notifications they actually need. So the scope was deliberately narrow: two event types, both tied to a confirmed action on the other side.

Architecture in one diagram

  mobile (Expo)                backend (Next.js)              matching-worker (DO)
  -------------                ----------------               --------------------
  registerCurrentUser     -->  POST /api/notifications/token
  PushToken()                  upsert into PushTokens
                                                              MatchingRoom event:
                                                                rider reserved
                                                                  or match accepted
                                                              -->  sendMatchPush
                                                                   Notification()
                                                                   reads PushTokens
                                                                   POST exp.host
  Notifications handler   <--  (Expo push service)            <--
  routes user to screen

The mobile app owns token acquisition. The backend owns token storage. The matching worker owns dispatch. None of them know about the other two beyond a shared Supabase table and a stable contract on the wire.

The storage layer

The whole feature hinges on one table:

create table if not exists public."PushTokens" (
  id bigint generated by default as identity primary key,
  user_id text not null references public."User"(id) on delete cascade,
  token text not null unique,
  platform text null,
  is_active boolean not null default true,
  created_at timestamp without time zone not null default now(),
  updated_at timestamp without time zone not null default now()
);

A few choices worth calling out:

token is unique, not (user_id, token). The same physical device could in theory be reused across user accounts (think shared family phone, or a dev account on a personal device), and we want the latest user to claim it. Upserts use onConflict: "token" so the row gets reassigned cleanly instead of duplicating.
is_active is a boolean rather than deleting rows. When Expo reports DeviceNotRegistered we mark the row inactive. This keeps history useful for debugging "why didn't I get a push" complaints without leaking junk into the active set.
A small set_push_tokens_updated_at trigger keeps updated_at honest on every write. Push tokens rotate, especially on iOS, so the freshness of a token row is something we look at when triaging.

Indexes are on user_id and on (user_id, is_active). The hot read path is always "give me the active tokens for this one user," which the composite index covers.

Registering a token from the mobile app

The mobile side lives in mobile/lib/pushNotifications.js. It runs on app start and also whenever the app comes back to the foreground:

useEffect(() => {
  void registerCurrentUserPushToken();
  const sub = AppState.addEventListener("change", (state) => {
    if (state === "active") {
      void registerCurrentUserPushToken();
    }
  });
  return () => sub.remove();
}, []);

The "on resume" call matters more than it looks. Tokens can be revoked or rotated while the app is backgrounded (an OS update, a permission change, a reinstall), and Expo will hand back a different value next time you ask. Re-registering on foreground is the cheapest way to stay correct.

The registration function itself is deliberately defensive. It walks through these steps and bails out loudly at any point that fails:

Skip on web entirely. There is no Expo push token there.
Look up the user in AsyncStorage. No stored @user, no registration, since we'd have nowhere to attribute the token.
On Android, create the default notification channel with MAX importance. Without this, notifications arrive but never make a sound or appear as a heads-up.
Ask for permissions, requesting them if not already granted.
Call getDevicePushTokenAsync for logging, then getExpoPushTokenAsync with the EAS projectId from Constants.expoConfig.extra.eas. The Expo token is what the backend actually stores.
POST it to ${BACKEND_URL}/api/notifications/token with an x-user-id header.

Every step logs a [push] ... line. That has paid for itself many times over. When a user reports "I never got the notification," the first question is always "what does logcat say at app start," and the answer comes from these breadcrumbs.

Sign-out goes through deactivateCurrentUserPushToken, which calls DELETE on the same endpoint. The server marks the row inactive rather than deleting it. If the device immediately signs back in we re-activate by upsert, no row churn.

The backend endpoint

The token route is one file: backend/app/api/notifications/token/route.ts. It exposes POST and DELETE.

The auth story is intentionally simple. The handler accepts a user id in three ways, in this order:

const fromHeader = request.headers.get("x-user-id")?.trim();
if (fromHeader) return fromHeader;

const bearer = parseAuthHeaderUserId(request.headers.get("Authorization"));
if (!bearer) return bodyUserId?.trim() || null;

// fall back to Supabase auth.getUser(bearer)

x-user-id is what the mobile app sends today because we already have a verified Google user id in AsyncStorage after login. The Authorization: Bearer ... path is there for when we move the rest of the API behind Supabase JWTs. Keeping both paths in one helper means we can flip the flag on auth without touching the notifications surface.

The POST body looks like:

{ "token": "ExponentPushToken[...]", "platform": "ios" }

The handler normalizes both fields (trim, lowercase platform), then upserts on token. That's the entire write path. The service role key lives in SUPABASE_SERVICE_ROLE_KEY, since this endpoint needs to write across users without going through RLS.

DELETE is almost the same shape. If the body includes a specific token, only that row gets deactivated. If it doesn't, every active token for that user gets flipped off. The "deactivate all" variant is what runs on sign-out, so a shared device doesn't keep buzzing the previous user.

Dispatching from the matching worker

The matching worker is a Cloudflare Durable Object. Each ride slot (location + day + start_time) is its own room, and inside that room there's a small state machine driving who is waiting, who is reserved, and who is confirmed. The push dispatch is hooked into exactly two transitions in MatchingRoom.ts:

if (this.shouldSendPush("driver_selected_rider", ev.rider_id, ev.driver_id)) {
  void sendMatchPushNotification(this.env, {
    recipientUserId: ev.rider_id,
    eventType: "driver_selected_rider",
    riderId: ev.rider_id,
    driverId: ev.driver_id,
  });
}

and

if (this.shouldSendPush("rider_confirmed_match", ev.rider_id, ev.driver_id)) {
  void sendMatchPushNotification(this.env, {
    recipientUserId: ev.driver_id,
    eventType: "rider_confirmed_match",
    riderId: ev.rider_id,
    driverId: ev.driver_id,
    rideId: ride.id,
  });
}

Two things to notice. First, the call is fire and forget (void). The state machine should never wait on Expo, and a failed push should never break a match. Second, shouldSendPush gates every call.

Why dedupe is necessary

Durable Objects can replay events. State recovery, client reconnects, even a rider rapidly tapping "accept" can cause the same logical transition to fire twice. Without dedupe, the user gets two identical notifications back-to-back, which feels broken even though it isn't.

shouldSendPush keeps an in-memory map keyed by ${slotKey}:${riderId}:${driverId}:${eventType}:${bucket}, where bucket = floor(now / 30s). Anything inside the same 30-second window for the same pair and event type is dropped. The map self-prunes anything older than 2 minutes, so it doesn't grow unbounded.

This lives in the DO instance memory rather than Supabase. Cross-instance dedupe isn't needed because each slot only has one DO, and that DO is the only sender for the slot.

Talking to Expo

pushNotifications.ts in the worker is the actual sender. It does three things:

Fetch active tokens for the recipient out of PushTokens. If a user has multiple devices, all of them get the message.
Build a message per token and POST the array to https://exp.host/--/api/v2/push/send. The payload sets sound: "default", a short human title and body, and stuffs the event metadata into data.
Walk the response tickets. Any token that comes back with DeviceNotRegistered gets bulk-updated to is_active = false.

The cleanup step is the part that's easy to forget. Without it, a user who reinstalls the app accumulates stale token rows, every push attempt eats a slot in the array sent to Expo, and you get rate-limited for ghosts. Treating DeviceNotRegistered as a hint to deactivate keeps the active set healthy with zero ops work.

The data payload is the contract with the mobile app:

data: {
  type: params.eventType,            // "driver_selected_rider" | "rider_confirmed_match"
  rider_id: params.riderId,
  driver_id: params.driverId,
  ride_id: params.rideId,            // present only on rider_confirmed_match
}

Deep-linking from a tapped notification

A push that just makes a sound is half a feature. The reason the data payload includes type is so the mobile app can route the user to the correct screen when they tap the notification, instead of dropping them on the home screen.

That listener lives in mobile/app/_layout.js:

const subscription = Notifications.addNotificationResponseReceivedListener(
  (response) => {
    const data = response?.notification?.request?.content?.data || {};
    const type = typeof data?.type === "string" ? data.type : "";
    if (type === "driver_selected_rider") {
      router.push("/rider/ScheduledRidesRider");
      return;
    }
    if (type === "rider_confirmed_match") {
      router.push("/driver/ScheduledRidesDriver");
    }
  },
);

The setNotificationHandler above it controls in-app behavior. We show the banner and the list entry, but don't play a sound or set a badge while the app is foregrounded. The reasoning is that if the user is already in the app, the WebSocket has already delivered the same information through the UI, and an extra sound on top of that is annoying.

How we fixed push notifications

Everything above describes the design as if it landed clean. It didn't. The first end-to-end test had token registration logs that looked perfect, no notifications arriving on the Android device, and a matching flow that broke after a successful match (the rides screen rendered empty). What follows is the actual debug trail.

The five things that were broken

1. Wrong EAS project ownership. The Expo project ID baked into app.json belonged to an account I no longer had access to. npx eas credentials returned Entity not authorized.

Fix: removed the old extra.eas.projectId, ran npx eas init to mint a fresh one under my account, rebuilt the app.

2. Missing FCM credentials on the new Expo project. Expo can issue an ExponentPushToken[...] without FCM credentials, so registration looked fine, but https://expo.dev/notifications returned InvalidCredentials: Unable to retrieve the FCM server key.

Fix: in Firebase Console under Project Settings, Service accounts, Generate new private key (not google-services.json, that's a different file). Uploaded it via npx eas credentials, Android, Google Service Account, FCM V1.

3. Stale tokens from the old project still in the DB. After re-creating the project, the old ExponentPushToken[...] rows in PushTokens were still is_active = true. Mixing them with the new token in a single Expo batch made the whole send return 400.

Fix: delete from "PushTokens" where user_id = '...'; then re-open the app to register a fresh token under the new project.

4. Backend swallowed Expo's error reason. pushNotifications.ts only logged the status code, so 400s were opaque.

Fix: added the response body and the token list to the error log so future push failures self-explain.

5. Notification tap went to the wrong screen. _layout.js routed driver_selected_rider to ScheduledRidesRider (upcoming rides), not the accept/reject card.

Fix: routed to /rider/AvailableDrivers with driverId from the push payload.

6. Tap stacked a duplicate AvailableDrivers screen. When the rider was already on AvailableDrivers (because the in-app match_request had already routed them there with full driver details), tapping the notification called router.push again, pushing a second copy on top, hydrated only with driverId, so it showed "Unknown Driver".

Fix: added a pathnameRef guard, skip the navigation if already on the target screen. Same guard for the driver-side rider_confirmed_match notif.

Side bugs found and fixed along the way

Empty upcoming-rides screen after accept. Worker wrote ride_date in UTC; backend queried in local time. Aligned the writer to use local-date components.
Rider showing 3x on driver's screen. Rider's WS reconnects re-sent rider_request, and handleRiderRequest had no dedupe. Now ignored if the rider is already waiting or in a pending match.
INVALID_STATE_ERR from MatchingContext.send. The ?. only guarded null, not CONNECTING/CLOSING readyState. Now checks readyState === 1.

The actual checklist for end-to-end push

Working backwards from the bug list, the requirements turned out to be:

Own the Expo project (account access).
Upload an FCM V1 service-account key to it (so Expo can deliver to Android).
Have a fresh, valid token registered in your DB.
Have the server actually call the send (it was, the matching flow does).
Route the tap somewhere useful, without stacking duplicate screens.

Items 1 and 2 are environmental and have nothing to do with the code. Items 3 through 5 are the ones the codebase has to keep honest forever. Most of the time when push "stops working" for a user later, the cause will be one of those three.

What it cost, what it bought

In code, this feature is small. One SQL file, one Next.js route, one mobile lib, one worker module, and a handful of call sites in the existing matching room. The whole thing is a few hundred lines.

What it bought is the ability to stop telling users "keep the app open while you wait." That single sentence was the biggest source of friction in early testing, and it didn't go away until tokens, dispatch, and dedupe were all in place.

A few things would be worth picking up later:

Server-side dedupe at the Supabase layer would let us survive a DO restart without re-sending. Today the 30-second bucket protects the common cases but isn't bulletproof across instance churn.
The Authorization header path in the token route is wired up but not exercised. Moving the mobile client onto Supabase JWTs would let us drop x-user-id and the trust assumption that goes with it.
Right now the notification title and body strings are hardcoded in the worker. A small templating layer would make it cheaper to localize and to A/B test the copy.

None of those block shipping. The current setup has been quietly delivering both event types reliably, deactivating dead tokens on its own, and routing taps to the correct screen, which is exactly the bar I wanted before writing about it.

Grant Reporting Best Practices - May 2026

Rashmi Sheel — Tue, 12 May 2026 17:00:34 +0000

Introduction to Grant Reporting Best Practices

As of May 2026, the grant landscape is becoming increasingly competitive, with organizations vying for a limited pool of funds to support their projects and initiatives. According to a recent report, the global grant management market is expected to reach $1.9 billion by 2028, growing at a compound annual growth rate (CAGR) of 13.4% during the forecast period. With this growth, the importance of effective grant reporting cannot be overstated. Grant reporting is a critical component of the grant management process, as it enables organizations to demonstrate their accountability, transparency, and stewardship of grant funds. In this article, we will explore grant reporting best practices, providing practical tips and actionable advice to help organizations optimize their grant reporting processes.

Understanding the Importance of Grant Reporting

Grant reporting is not just a necessary evil; it is an opportunity for organizations to showcase their impact, build trust with funders, and demonstrate their capacity to manage grant funds effectively. A well-crafted grant report can help organizations:

Demonstrate their accountability and transparency in the use of grant funds
Showcase their achievements and impact, highlighting the difference they are making in their community or field
Build trust with funders, increasing the likelihood of future funding opportunities
Identify areas for improvement, informing future project planning and implementation

Grant Reporting Best Practices

So, what are the grant reporting best practices that organizations should adopt? Here are some key tips:

Start early: Don't wait until the last minute to start working on your grant report. Begin gathering data and information as soon as the grant period commences.
Use a robust grant management system: Invest in a grant management system that can help you track grant funds, monitor progress, and generate reports.
Be transparent and honest: Be open and honest in your grant report, acknowledging challenges and setbacks, as well as successes and achievements.
Use data and metrics: Use data and metrics to demonstrate your impact and progress, making it easier to tell your story and make your case for future funding.
Tell a story: Use narrative techniques to bring your grant report to life, telling a story that showcases your organization's mission, vision, and values.

Actionable Advice for Grant Reporting

In addition to these best practices, here are some actionable tips to help you optimize your grant reporting process:

Develop a grant reporting template: Create a template that outlines the key information and data required for your grant report, making it easier to gather and organize information.
Establish a grant reporting schedule: Set a schedule for grant reporting, ensuring that reports are submitted on time and that all stakeholders are informed and engaged.
Provide regular updates: Provide regular updates to funders, keeping them informed of progress and milestones achieved during the grant period.
Conduct a thorough review: Conduct a thorough review of your grant report before submission, ensuring that it is accurate, complete, and free of errors.

Leveraging Technology to Enhance Grant Reporting

Technology can play a critical role in enhancing grant reporting, providing organizations with the tools and resources they need to streamline their grant management processes. For example, grant management software can help organizations:

Track grant funds: Monitor grant funds, ensuring that they are used effectively and efficiently.
Generate reports: Generate reports quickly and easily, using data and metrics to demonstrate impact and progress.
Collaborate with stakeholders: Collaborate with stakeholders, including funders, team members, and partners, to ensure that everyone is informed and engaged.

Conclusion

Grant reporting is a critical component of the grant management process, enabling organizations to demonstrate their accountability, transparency, and stewardship of grant funds. By adopting grant reporting best practices, organizations can optimize their grant reporting processes, building trust with funders and increasing the likelihood of future funding opportunities. To learn more about grant reporting best practices and how to optimize your grant management processes, businesses can learn more at: https://flowgrant.ai. With the right tools, resources, and expertise, organizations can harness the power of grant reporting to drive their mission forward, making a meaningful difference in their community or field. By staying up-to-date with the latest trends and best practices in grant reporting, organizations can ensure that they are well-positioned to succeed in an increasingly competitive grant landscape.

Originally published at https://flowgrant.ai

Intelligent Automation Trends - May 2026

Rashmi Sheel — Tue, 12 May 2026 17:00:21 +0000

Introduction to Intelligent Automation Trends

As we navigate the ever-evolving landscape of technological advancements, intelligent automation has emerged as a pivotal force in transforming the way businesses operate. By harnessing the power of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), organizations are streamlining processes, enhancing efficiency, and driving innovation. In May 2026, the intelligent automation trends are not only about adopting new technologies but also about creating a strategic framework that integrates these technologies seamlessly into existing infrastructures. This article delves into the current trends, offers practical tips, and provides actionable advice for businesses looking to leverage intelligent automation to their advantage.

Current State of Intelligent Automation

The current state of intelligent automation is characterized by rapid growth and widespread adoption. According to recent statistics, the global intelligent automation market is projected to reach $15.8 billion by 2025, growing at a Compound Annual Growth Rate (CAGR) of 12.1% during the forecast period. This growth is largely driven by the need for businesses to automate repetitive and mundane tasks, improve operational efficiency, and enhance customer experience.

Key Trends in Intelligent Automation for May 2026

Several trends are shaping the intelligent automation landscape in May 2026. Some of the key trends include:

HyperAutomation: This involves the use of advanced technologies like AI, ML, and RPA to automate complex processes that previously required human intervention. HyperAutomation is expected to revolutionize industries by providing unprecedented levels of automation and efficiency.
Autonomous Systems: Autonomous systems, powered by AI and ML, are becoming increasingly prevalent. These systems can operate independently, making decisions in real-time without human intervention, thereby reducing the need for manual oversight.
Process Mining: Process mining involves the use of data analytics and ML algorithms to discover, monitor, and improve business processes. This trend is gaining traction as it helps organizations identify areas where intelligent automation can be applied for maximum impact.

Practical Tips for Implementing Intelligent Automation

Implementing intelligent automation requires a strategic approach. Here are some practical tips and actionable advice for businesses:

Assess Current Processes: Begin by assessing your current business processes to identify areas that can be automated. Look for tasks that are repetitive, time-consuming, and prone to human error.
Choose the Right Technology: Select technologies that align with your business goals and processes. Consider factors like scalability, integration capabilities, and user adoption.
Develop a Strategic Framework: Create a strategic framework that outlines how intelligent automation will be integrated into your organization. This should include plans for training, implementation, and ongoing support.
Monitor and Evaluate: Continuously monitor and evaluate the impact of intelligent automation on your business. Use data analytics to identify areas of improvement and make adjustments as needed.

Overcoming Challenges in Intelligent Automation

While intelligent automation offers numerous benefits, it also presents several challenges. Some of the common challenges include:

Data Quality Issues: Poor data quality can hinder the effectiveness of intelligent automation solutions. Ensure that your data is accurate, complete, and consistent.
Change Management: Implementing intelligent automation requires significant changes to business processes and employee roles. Develop a comprehensive change management plan to mitigate resistance and ensure a smooth transition.
Security and Compliance: Intelligent automation solutions must be designed with security and compliance in mind. Ensure that your solutions adhere to relevant regulations and standards.

Conclusion and Next Steps

In conclusion, intelligent automation is transforming the business landscape by providing unprecedented levels of efficiency, innovation, and customer satisfaction. By understanding the current trends, adopting practical tips, and overcoming challenges, businesses can harness the power of intelligent automation to drive growth and success. For businesses looking to learn more about how to leverage intelligent automation, visiting https://myassistant365.ai can provide valuable insights and resources. This platform offers a wealth of information on the latest trends, technologies, and best practices in intelligent automation, helping organizations make informed decisions about their automation strategies. As we move forward in 2026, embracing intelligent automation will be crucial for businesses aiming to stay competitive and thrive in a rapidly evolving market.

Originally published at https://myassistant365.ai

DEV Community: tutorial

How to Deploy Llama 3.2 with Ollama + Kubernetes on a $8/Month DigitalOcean Droplet: Auto-Scaling Inference Without GPU Costs

⚡ Deploy this in under 10 minutes

How to Deploy Llama 3.2 with Ollama + Kubernetes on a $8/Month DigitalOcean Droplet: Auto-Scaling Inference Without GPU Costs

Why This Matters: The Economics of Self-Hosted Inference

Step 1: Provision Your Droplet and Install k3s

Step 2: Create the Ollama Deployment with Resource Limits

Step 3: Set Up Auto-Scaling with Horizontal Pod Autoscaler

I Built an ESP32-CAM Project That Captures Photos and Sends Them to Email

Why I Tried This Project

Hardware Used

How the System Works

The Interesting Part: HTTPS on ESP32-CAM

Camera Settings Matter More Than Expected

Common Problems I Faced

Random Board Resets

Camera Initialization Failed

Email Not Sending

Why This Project Feels Useful

What I’d Add Next

Final Thoughts

Python os Module: File System Operations Every Script Needs

Python os Module: File System Operations Every Script Needs

What os gives you

os.path — the building blocks

Working with directories

Creating, removing, and renaming

os.replace() — atomic file replacement

Environment variables

Walking directory trees: os.walk()

os.path vs pathlib — when to use each

Real pipeline example: organizing generated chapter files

Further Reading

How to add automatic LLM fallbacks to your voice pipeline

Why fallbacks matter more for voice than for chat

What you'll build

Setup

Step 1: Connect to Universal-3 Pro Streaming

Step 2: Add the fallback chain

Step 3: Choose the right primary and fallback models

Step 4: Override fields per fallback

Step 5: Putting it all together

What this gets you in production

What to build next

Frequently asked questions

What is an LLM fallback and why does my voice pipeline need one?

How does AssemblyAI's LLM Gateway handle automatic LLM failover?

Which LLM providers does AssemblyAI's LLM Gateway support for fallback chains?

How do I add automatic LLM fallbacks to my voice pipeline?

Can I customize the prompt or temperature for each fallback model?

How does billing work when an LLM Gateway request falls back to a different model?

What's the difference between LLM Gateway fallbacks and writing my own retry logic?

Stream LLM responses in a voice pipeline: Tool calling, structured outputs, and real-time actions

Why streaming matters more in voice than in chat

What you'll build

Setup

Step 1: Stream tokens from LLM Gateway

Step 2: Add tool calling

Step 3: Use structured outputs for predictable JSON

Step 4: Wire it all together with streaming STT

When to use which technique

Skip the wiring with the Voice Agent API

Frequently asked questions

What does it mean to stream LLM responses in a voice pipeline?

How do I stream LLM responses through AssemblyAI's LLM Gateway?

How does tool calling work with the LLM Gateway?

Can I get structured JSON outputs from the LLM Gateway for voice agents?

What's the latency budget for a real-time voice agent using streamed LLM responses?

When should I use the Voice Agent API instead of wiring streaming STT and LLM Gateway separately?

Does streaming work with tool calling and structured outputs?

Build an AI voice agent for customer support that can look up orders

Why most support voice agents fail

What you'll build

Setup

Step 1: Define the support tools

Step 2: Write the system prompt

Step 3: Connect to the Voice Agent API

Step 4: Test the three workflows

Step 5: Take it to the phone

What to harden before production