DEV Community: Ranjan Dailata

Unstructured Text to Business Decisions with Clocked by Rezolve.ai and n8n

Ranjan Dailata — Sun, 18 Oct 2026 03:21:32 +0000

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Extract and route support tickets with Clocked Extract and Decide n8n workflow

Support Ticket Data Extraction - Analysis & Routing Automation with Clocked.ai

Support Ticket Structured Data Extraction with Clocked.ai

Introduction

Organizations generate enormous amounts of unstructured data every day: IT support tickets, customer emails, invoices, incident reports, meeting notes, insurance documents, and operational logs.

The challenge is not simply extracting text. It is understanding the information, identifying what matters, applying business rules, and deciding what should happen next.

In this article, you will learn on how to build an AI-powered unstructured data analysis pipeline using Clocked by Rezolve.ai and n8n.

Note - This blog post consists of the code snippets in Python. However, the design and development of the workflows are accomplished using n8n. You may refer and incorporate the code snippet or extend the workflow based on your comfort level.

IT support ticket analyzer is an example utilized to show how the same architecture can be adapted to other enterprise workflows.

1. The problem: unstructured data is difficult to operationalize

Consider this support ticket:

Ticket: INC-59301
Requester: Dana Whitfield
Email: dana@example.com
Device: LAPTOP-204
The VPN disconnects every ten minutes. Outlook also crashes on launch.
Error: Authentication token expired.
Restarting the VPN client did not help. Reinstalling Outlook did not help.
Assigned engineer: Priya Raman.
Please call tomorrow afternoon.

Update, 14:20: The affected device is LAPTOP-207, not LAPTOP-204. Priya remains assigned. No restoration estimate has been provided.

A conventional workflow might store this as a block of text. A useful AI workflow should identify the ticket ID, requester, email, affected applications, error message, troubleshooting attempts, corrected device ID, and callback preference.

It could then answer additional questions:

Is this primarily a VPN, identity, or application issue?
Should the ticket be escalated?
Which support team should handle it?
Is the available information sufficient to recommend a next step?
Which fields require human verification?

These are related but different tasks. Extracting a device ID is not the same as deciding where to route a ticket.

2. The architecture

Our pipeline separates orchestration, extraction, decision-making, and execution.

The components have distinct responsibilities:

n8n: Orchestrates triggers, API calls, branching, retries, and integrations.
Clocked: Extracts structured information from supported source content.
Python or another validation layer: Checks required fields, conflicting values, and business constraints.
System One: Evaluates explicitly defined questions and returns structured decisions, scores, or probabilities.
Business systems: Execute approved actions and retain the resulting records.

Clocked capabilities should be selected based on the task. In particular, do not assume every model accepts images or generates arbitrary JSON; check the supported inputs and outputs of the selected model.

3. Step one: ingest and prepare the source

The first stage collects data from a webhook, ticketing system, email, document upload, or scheduled job.

In our example, the workflow starts manually in n8n. A production workflow could start when a new ticket is created.

The initial preparation stage should:

Assign a stable correlation ID to the processing job.
Preserve the original ticket content.
Identify the source and timestamp.
Check whether the payload is empty or malformed.
Remove or protect sensitive data when required by organizational policy.

The original source should remain available for traceability. Do not overwrite it with a cleaned-up or AI-generated version.

4. Step two: extract structured information with Clocked

Clocked is the extraction stage: Define the fields the application needs.

For example:


import json
import os
import sys
import uuid
from typing import Any

import requests


API_URL = "https://clocked.rezolve.ai/clocked/extract"
API_KEY = os.getenv("CLOCKED_API_KEY")

if not API_KEY:
    raise RuntimeError(
        "Missing CLOCKED_API_KEY environment variable."
    )


TICKET_TEXT = """
Ticket: INC-59301
Requester: Dana Whitfield
Email: dana@example.com
Device: LAPTOP-204

The VPN disconnects every ten minutes. Outlook also crashes on launch.
The error message is: Authentication token expired.
Restarting the VPN client did not help.
Reinstalling Outlook did not help.

Assigned engineer: Priya Raman
Please call tomorrow afternoon.

Update, 14:20: Dana confirms the affected device is LAPTOP-207,
not LAPTOP-204. Priya remains assigned.
No restoration estimate has been provided.
"""


EXTRACTION_FIELDS = {
    "ticket_id": "The support ticket identifier.",
    "requester": "The requester's full name.",
    "email": "The requester's email address.",
    "device": {
        "description": (
            "The currently affected device identifier. "
            "Prefer explicit corrections in later updates."
        )
    },
    "assigned_engineer": "The assigned engineer's full name.",
    "error_message": "The exact error message reported.",
    "affected_applications": {
        "description": (
            "All applications or clients reported as malfunctioning, "
            "including the VPN client and Outlook when supported "
            "by the source."
        ),
        "list": True,
        "max_items": 10,
    },
    "attempted_fixes": {
        "description": (
            "All troubleshooting actions already attempted, "
            "preserving their meaning."
        ),
        "list": True,
        "max_items": 10,
    },
    "callback_time": (
        "The requested callback time exactly as written. "
        "Do not infer a calendar date."
    ),
    "restoration_estimate": (
        "An explicit date or time by which service will be restored. "
        "Return null if no estimate is provided."
    ),
}


def extract_support_ticket(ticket_text: str) -> dict[str, Any]:
    """Send a support ticket to Clocked.ai and return extracted fields."""

    if not ticket_text.strip():
        raise ValueError("Ticket text cannot be empty.")

    payload = {
        "text": ticket_text,
        "instructions": (
            "Extract only facts supported by the ticket. "
            "Prefer explicit corrections in later updates over earlier "
            "values. Preserve source wording where appropriate. "
            "Return null for missing scalar facts and do not guess. "
            "Include all explicitly reported affected applications "
            "and attempted troubleshooting actions."
        ),
        "fields": EXTRACTION_FIELDS,
    }

    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
        "Accept": "application/json",
        "Idempotency-Key": str(uuid.uuid4()),
    }

    response = requests.post(
        API_URL,
        headers=headers,
        json=payload,
        timeout=60,
    )

    response.raise_for_status()
    result = response.json()

    # Clocked Extract returns each extracted field as an object
    # containing a "value" property.
    raw_fields = result.get("fields")

    if not isinstance(raw_fields, dict):
        raise ValueError(
            "Unexpected API response: missing 'fields' object. "
            f"Response: {json.dumps(result, ensure_ascii=False)}"
        )

    extracted = {
        name: raw_fields.get(name, {}).get("value")
        if isinstance(raw_fields.get(name), dict)
        else None
        for name in EXTRACTION_FIELDS
    }

    # Keep confidence and evidence offsets for auditing.
    metadata = {
        name: {
            key: raw_fields[name].get(key)
            for key in ("confidence", "start", "end")
            if key in raw_fields[name]
        }
        for name in EXTRACTION_FIELDS
        if isinstance(raw_fields.get(name), dict)
    }

    return {
        "extracted_fields": extracted,
        "field_metadata": metadata,
        "model": result.get("model"),
        "timing": result.get("timing"),
    }


def main() -> None:
    ticket_text = (
        sys.stdin.read()
        if not sys.stdin.isatty()
        else TICKET_TEXT
    )

    try:
        output = extract_support_ticket(ticket_text)

        print(json.dumps(output, indent=2, ensure_ascii=False))

        with open(
            "extracted_ticket.json",
            "w",
            encoding="utf-8",
        ) as file:
            json.dump(
                output,
                file,
                indent=2,
                ensure_ascii=False,
            )

        print("\nSaved results to extracted_ticket.json")

    except requests.HTTPError as exc:
        print(
            f"Clocked.ai HTTP error: {exc}\n"
            f"Response: {exc.response.text if exc.response else ''}",
            file=sys.stderr,
        )
        sys.exit(1)

    except requests.RequestException as exc:
        print(f"API request failed: {exc}", file=sys.stderr)
        sys.exit(1)

    except (ValueError, RuntimeError) as exc:
        print(f"Extraction failed: {exc}", file=sys.stderr)
        sys.exit(1)


if __name__ == "__main__":
    main()

The extraction instructions matter just as much as the schema:

Extract only information supported by the source. Preserve exact identifiers, error messages, and dates where possible. Apply explicit corrections in later updates. Do not invent missing values, infer an unprovided restoration estimate, or turn a vague callback preference into an exact time.

Example output

The target structure for this ticket would look like:

{
  "requester": "Dana Whitfield",
  "ticket_id": "INC-59301",
  "email": "dana@example.com",
  "device": "LAPTOP-207",
  "assigned_engineer": "Priya Raman",
  "error_message": "Authentication token expired.",
  "affected_applications": [
    "VPN client",
    "Outlook"
  ],
  "attempted_fixes": [
    "Restarting the VPN client",
    "Reinstalling Outlook"
  ],
  "callback_time": "tomorrow afternoon",
  "restoration_estimate": null
}

This is an illustrative target output, not a claim that every API invocation will return precisely these values.

Notice the device ID: LAPTOP-207 supersedes LAPTOP-204 because the latest update explicitly corrects the earlier value.

Likewise, restoration_estimate is null because the source provides no estimate. The callback preference remains “tomorrow afternoon” rather than being converted into an invented appointment time.

Important: Configure the actual output schema to match the API's supported field definitions and output format. If the service represents missing values differently, normalize them in your application.

5. Step three: validate before making decisions

Extraction output is not automatically trustworthy business data.

Before sending it to System One, validate it in Python or your preferred application layer.

def validate_ticket(ticket: dict) -> list[str]:
    errors = []

    required_fields = [
        "ticket_id",
        "requester",
        "device",
        "assigned_engineer",
    ]

    for field in required_fields:
        if not ticket.get(field):
            errors.append(f"Missing required field: {field}")

    if ticket.get("restoration_estimate") is None:
        # A missing estimate is allowed.
        pass

    if not ticket.get("affected_applications"):
        errors.append("No affected applications identified")

    return errors

This is a starting point, not a complete production validator. Add field types, email validation, allowed identifier formats, source-specific requirements, and checks for contradictory updates.

For example, if two updates disagree about the affected device and neither clearly supersedes the other, send the ticket for review rather than choosing arbitrarily.

6. Step four: Use Decide (System One) model for structured decisions

Once the data is structured and validated, System One can help evaluate questions with explicit answer choices, ordered scores, or conditions.

Example A: ticket classification

Define the possible categories:

{
  "question": "What is the primary support category?",
  "options": [
    "Network / VPN",
    "Identity / Authentication",
    "Email / Outlook",
    "Hardware",
    "Other"
  ]
}

The ticket mentions both a VPN disconnection and an expired authentication token, as well as Outlook crashes. Classification should consider the full context and the organization's definition of a primary category.

The workflow might classify the ticket as an identity/authentication issue, but that is a decision to evaluate not a guaranteed result.

Example B: escalation assessment

Define a structured urgency scale:

{
  "question": "What escalation level is appropriate?",
  "options": [
    "Routine",
    "Priority",
    "Urgent",
    "Critical"
  ]
}

The organization should define the escalation criteria separately. For example, a single-user issue with a workaround may have different priority from a widespread outage affecting business-critical operations.

The model's output should be combined with explicit rules such as business impact, affected-user count, service-level agreements, and incident policies.

Example C: determine whether human review is needed

A condition question could assess whether the ticket contains conflicting or insufficient information.

The application can combine that result with deterministic checks:

def needs_human_review(
    validation_errors: list[str],
    confidence: float,
    threshold: float = 0.85,
) -> bool:
    return bool(validation_errors) or confidence < threshold

Here, confidence is an illustrative application input. Connect it to the actual probability or confidence output documented for the selected model; do not assume every API response uses the same field or scale.

A probability is a signal, not a guarantee of correctness. Calibrate thresholds against labeled tickets from your own organization.

7. Step five: orchestrate the entire process in n8n

A simplistic example workflow uses four visible nodes:

Manual Trigger - Starts the workflow during development.
Support Ticket Input with Extractor Rules - Defines the ticket text and requested fields in JSON.
Unique ID Generator - Creates an identifier for the processing request. It's a mandatory one for the Clocked API.
Rezolve.ai Ticket Analyzer - Sends the extraction request.

For production usages, extend the workflow with validation, decision evaluation, and routing nodes.

A practical sequence is:

Trigger on a new ticket.
Normalize the input and preserve the original source.
Generate a correlation ID.
Call Clocked API to extract the requested fields.
Parse and validate the returned data.
Call Clocked Decide (System One) API for classification or other defined decisions.
Evaluate business rules and confidence thresholds.
Route uncertain or conflicting results to a human reviewer.
Update the ticketing system only when the applicable checks pass.
Record the outcome for auditing and evaluation.

Connecting to Clocked

Use the Clocked API Documentation to learn about the Clocked API usages. The following request illustrates the extraction pattern; verify the endpoint, authentication, and payload schema against your enabled service before deployment.

import os
import uuid
import requests

CLOCKED_API_KEY = os.environ["CLOCKED_API_KEY"]

def extract_ticket(ticket_text: str, fields: dict) -> dict:
    response = requests.post(
        "https://clocked.rezolve.ai/clocked/extract",
        headers={
            "Authorization": f"Bearer {CLOCKED_API_KEY}",
            "Idempotency-Key": str(uuid.uuid4()),
        },
        json={
            "text": ticket_text,
            "fields": fields,
        },
        timeout=120,
    )

    response.raise_for_status()
    return response.json()

This example assumes a text-based payload. If your configured extraction API requires an images array or another source representation, use its documented schema instead. Refer Clocked Extract

In production, reuse the same idempotency key when retrying the same logical request if the API supports that behavior. Add bounded retries, appropriate error handling, request logging without sensitive ticket contents, and monitoring.

Connecting to System One

Use the official Clocked Decide System One to configure the selected model, request format, authentication, and decision primitives.

Keep the integration modular:

import os
import requests
from typing import Any

CLOCKED_DECIDE_URL = "https://clocked.rezolve.ai/decide"


def evaluate_with_system_one(ticket: dict[str, Any]) -> dict[str, Any]:
    """
    Evaluate a structured support ticket using Clocked Decide.

    NOTE:
    Confirm the exact request schema and authentication requirements
    against your Clocked Decide API documentation.
    """
    api_key = os.environ.get("CLOCKED_API_KEY")
    if not api_key:
        raise RuntimeError("CLOCKED_API_KEY environment variable is missing")

    payload = {
        "state": ticket,
        "questions": {
            "ticket_category": {
                "type": "choice",
                "instructions": "Choose the primary support category.",
                "criteria": {
                    "network_vpn": "VPN connectivity or network problems",
                    "identity_authentication": "Authentication or expired-token problems",
                    "email_application": "Outlook or email application problems",
                    "hardware": "Physical device or hardware problems",
                    "other": "Issues not covered by the other categories",
                },
            },
            "urgency": {
                "type": "score",
                "instructions": (
                    "Assess ticket urgency using the available evidence "
                    "and the defined levels."
                ),
                "criteria": [
                    "Routine",
                    "Priority",
                    "Urgent",
                    "Critical",
                ],
            },
            "requires_human_review": {
                "type": "noul",
                "instructions": (
                    "Is human review required before taking action? "
                    "Consider conflicting information, missing critical "
                    "details, and uncertainty."
                ),
            },
        },
    }

    response = requests.post(
        CLOCKED_DECIDE_URL,
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
            "Accept": "application/json",
        },
        json=payload,
        timeout=30,
    )

    response.raise_for_status()
    return response.json()


if __name__ == "__main__":
    ticket = {
        "ticket_id": "INC-59301",
        "requester": "Dana Whitfield",
        "device": "LAPTOP-207",
        "assigned_engineer": "Priya Raman",
        "error_message": "Authentication token expired.",
        "affected_applications": ["VPN client", "Outlook"],
        "attempted_fixes": [
            "Restarting the VPN client",
            "Reinstalling Outlook",
        ],
        "callback_time": "tomorrow afternoon",
        "restoration_estimate": None,
    }

    result = evaluate_with_system_one(ticket)
    print(result)

Keeping this boundary explicit makes it easier to test extraction and decision-making independently.

8. Beyond IT support: Enterprise use cases

The same architecture applies to many business workflows.

Use case	Extract structured data	Evaluate decisions
Invoice processing	Supplier, invoice number, total, due date	Duplicate risk, exception routing, approval path
Customer support	Customer ID, product, issue, requested resolution	Category, priority, escalation
HR operations	Employee request, leave dates, policy references	Request type, missing evidence, routing
Compliance review	Entities, dates, obligations, source references	Risk category, review requirement, policy checks
Incident management	Affected services, timestamps, error messages	Severity, owning team, escalation
Procurement	Supplier, line items, contract references	Policy exceptions, approval requirements

These are potential applications, not guarantees of model accuracy. Each requires its own schema, decision definitions, access controls, and evaluation dataset.

For high-impact processes, use the model to support decisions rather than silently authorize payments, change access permissions, or make consequential employment decisions.

9. Security, privacy, and governance

Enterprise adoption requires more than a successful API response.

Protect sensitive data

Store API keys in a secrets manager or protected environment variables.
Send only the data required for the task.
Restrict access to tickets, invoices, employee records, and model outputs.
Establish retention rules for prompts, request logs, and extracted values.
Confirm the service's data handling, retention, and contractual terms before sending sensitive information.

Preserve provenance

Keep the original source or a secure reference to it.
Record which extraction schema and model configuration were used.
Link extracted fields to the source document or passage where practical.
Distinguish directly observed values from model-derived classifications.

Keep consequential actions controlled

Require explicit policy checks before updating critical systems.
Use human review for ambiguous, conflicting, or low-confidence results.
Maintain an audit trail for approvals and automated actions.
Treat external document text as data, not as instructions that can override system controls.

10. Measure the quality of the pipeline

Do not evaluate the workflow solely on whether it returned valid JSON.

Build a labeled test set containing ordinary examples, missing fields, contradictory updates, malformed inputs, and difficult edge cases.

Measure:

Field-level accuracy: How often extracted values match the labeled source.
Correction accuracy: Whether later, explicit updates override earlier values correctly.
Missing-value behavior: Whether absent information stays absent.
Classification quality: Precision, recall, and confusion matrices for categories.
Routing quality: Whether the selected team or escalation level matches the expected decision.
Review rate: How often a human must intervene.
Operational performance: Latency, failure rate, retry rate, and cost per record.

Track extraction errors separately from decision errors. This helps teams identify whether a problem originates in reading the source, interpreting the information, applying business rules, or executing the downstream action.

11. A practical implementation roadmap

Phase 1 - Extract

Choose one workflow, such as support tickets. Define the schema and test extraction against labeled examples.

Phase 2 - Validate

Add deterministic validation, correction handling, missing-value checks, and duplicate detection.

Phase 3 - Decide

Introduce Clocked Decide (System One) for classification, scoring, and condition checks. Evaluate model outputs against reviewed examples.

Phase 4 - Orchestrate

Connect n8n to the ticketing system and add controlled routing, retry handling, and human-review branches.

Phase 5 - Govern and improve

Monitor quality, review failures, update schemas and policies, and rerun regression tests before deployment changes.

Conclusion

Unstructured data becomes more useful when organizations separate what the source says, what the system infers, and what the business decides to do.

Clocked supports multiple APIs that one could leverage as part of the automation work. However, this blog post focuses primarily on the structured extraction and decision handling via the Clocked.ai extract and decide APIs; One could leverage as part of the n8n automation and can connect the stages into a repeatable workflow.

The result is a modular pipeline that can turn tickets, emails, and documents into structured records, evaluated decisions, and governed business actions.

Start with one narrow use case, define measurable acceptance criteria, and keep humans in control wherever uncertainty or business risk warrants it.

Resources

Internals of building a Customer Sentiment Intelligence with SerpAPI + System One

Ranjan Dailata — Sat, 10 Oct 2026 17:43:30 +0000

Source Code

Here's the link to the opensource code - serpapi-customer-sentiment

Introduction

Manual review triage is a critical bottleneck. As review volumes grow daily, high-priority signals like churn risks and serious complaints routinely get buried under a mountain of noise. Without automation, feedback remains fragmented across multiple platforms, and escalations surface only after customers are already lost.

Customer Sentiment Intelligence (CSI) solves this by automating the entire "listen-score-act" loop. Built as a lightweight Python reference implementation, CSI aggregates public data like Yelp reviews and Google News into a single command. It instantly normalizes, deduplicates, and scores each item across seven dimensions, using an explainable rule engine to route data into four clear business actions: DISCARD, QUEUE, ANALYZE, or ESCALATE.

Stack: Python 3.11+ · FastAPI · SerpAPI (Yelp Search, Yelp Reviews, Google News) · TypeSafe Jev / System One (or Laya gateway) · optional OpenAI-compatible LLM · Pydantic validation · pytest suite.

In this post I'll walk through the architecture, the scoring contract, the decision engine, and the exact code - so you can run it, extend it, or steal the pattern for your own triage problem.

1. The problem

Customer Experience (CX) teams doing manual review hit four walls:

Noisy data - The vast majority of reviews and articles are low-relevance or redundant, yet every item still has to be read and judged by hand.
Inconsistent triage - Relevance, sentiment, severity, and churn risk are assessed subjectively. Priorities differ per person, per day.
Late escalation - Serious complaints surface after customers are already lost, or land on the wrong team without shared context.
No structured record - Feedback lives in many formats and places, so sentiment trends never feed product, support, or leadership decisions.

The fix isn't a dashboard. It's a pipeline with four properties: collect without scrapers, normalize into one schema, score deterministically, route explainably.

2. Architecture: five stages, one payload

The pipeline (backend/app/pipeline.py) runs four steps per invocation:

class CustomerSentimentPipeline:
    def run(self, company, source="yelp", location=None, search=None,
            reviews=None, include_news=True, analysis=None,
            gl="us", hl="en"):
        evidence = self.discover(
            company, source, location, search, reviews,
            include_news, gl, hl
        )
        results = []

        for item in evidence:
            try:
                system_one_result = self.system_one.evaluate(
                    item, analysis=analysis
                )
                decision = make_decision(system_one_result)

                record = {
                    "evidence": item,
                    "jev": system_one_result,   # stable output key,
                    "decision": decision,       # whichever backend scored it
                }

                if decision["decision"] in {"ANALYZE", "ESCALATE"}:
                    record["deep_analysis"] = analyze(
                        item, system_one_result, self.settings
                    )

                results.append(record)

            except Exception as exc:
                results.append({
                    "evidence": item,
                    "error": str(exc),
                    "decision": {
                        "decision": "QUEUE",
                        "reason": "Processing error; manual review required",
                    },
                })

        return {
            "company": company,
            "evidence_count": len(evidence),
            "summary": self._summary(results),
            "results": results,
        }

Three design decisions worth noting:

Per-item failure isolation. Each item is evaluated inside its own try/except. One bad item can't kill the run - it fails safe into QUEUE with a reason. Nothing silently disappears.
Backend-agnostic scoring. SYSTEM_ONE_CLIENTS = {"jev": JevClient, "laya": LayaClient} - both expose the same evaluate(evidence, analysis=...) contract, and the output key is always jev regardless of which engine scored it. Swapping backends is a one-line .env change (SYSTEM_ONE_PROVIDER=laya), not a code change.
LLM spend is gated. analyze() only runs for ANALYZE/ESCALATE items. Without OPENAI_ADMIN_KEY it returns prompt_only - fully usable, zero cost.

3. Stage 1 - Collect: SerpAPI, no scrapers

Discovery (discover()) does two SerpAPI calls. First, find_place() hits engine=yelp with the search term and a structured find_loc ("City, State, Country" built from the location object), scans organic_results, and takes the first place_ids[0]. Then reviews() hits engine=yelp_reviews with that place_id, paginating up to the requested limit (49 per page, sort_by mapped to Yelp's options). Optionally, news() hits engine=google_news for the latest coverage of the company.

# CLI - same pipeline, stdout or file
python -m app.main --company "The Plaza" --city "New York" --state NY --country US
python -m app.main --company "The Plaza" --city "New York" --state NY --country US \
  --review-limit 50 --sort-by newest --output output.json
python -m app.main --company "The Plaza" --city "New York" --state NY --country US --no-news

Key detail: Yelp Search requires a location, so when the structured location is empty the pipeline falls back to the gl country name (e.g. us - United States). No location, no place_id, no reviews - fail fast with a clear RuntimeError, not an empty payload.

4. Stage 2 - Normalize and deduplicate: the evidence layer

Every review and article becomes one record in a shared schema (backend/app/evidence.py):

import hashlib

def _id(text):
    return hashlib.sha256(text.encode("utf-8")).hexdigest()[:16]

def normalize_review(company, review):
    comment = review.get("comment") or {}
    text = comment.get("text") or review.get("text") or ""
    return {
        "evidence_id": _id(f"{company}|yelp_review|{text}|{review.get('date')}"),
        "company": company,
        "source": "yelp",
        "source_type": "customer_review",
        "published": review.get("date"),
        "rating": review.get("rating"),
        "author": (review.get("user") or {}).get("name"),
        "text": text,
        "url": review.get("link") or (review.get("user") or {}).get("link"),
    }

def deduplicate(evidence):
    seen, result = set(), []
    for item in evidence:
        if item["evidence_id"] not in seen and item.get("text"):
            seen.add(item["evidence_id"])
            result.append(item)
    return result

Deterministic sha256 IDs (truncated to 16 hex chars) make dedup cheap and repeatable across runs - the same review always maps to the same ID, so re-runs and overlapping news/review fetches never double-count. Empty-text items are rejected, so each real signal is analyzed exactly once. Nine canonical fields, no surprises downstream.

5. Stage 3 - Score: seven typed questions, one model call

This is the heart of the system. Each evidence item is sent to Jev / System One (POST {base}/v1/systemone, 45s timeout, Bearer key) with a state block (company, source, rating, text, URL) and seven fixed questions (JevClient.questions() in backend/app/jev_client.py):

#	Question	Type	What it returns
1	`relevant`	`noul`	0–1 confidence the item concerns customer experience
2	`sentiment`	`choice`	`positive` / `neutral` / `negative` + confidence + full probability distribution
3	`intent`	`choice`	`praise`, `complaint`, `question`, `request`, `recommendation`, `warning`, `general_feedback`
4	`topic`	`choice`	`product`, `pricing`, `support`, `delivery`, `staff`, `quality`, `technology`, `billing`, `other`
5	`severity`	`score`	0–4 graded scale with a legend (`No issue` - `Critical issue`)
6	`requires_escalation`	`noul`	0–1 confidence a human needs to look
7	`churn_risk`	`score`	0–4 graded scale (`No churn signal` - `Very strong churn signal`)

Three things I like about this contract:

Typed answers, not free text. noul (yes/no + confidence), choice (pick + per-option probabilities), score (graded + legend). The downstream decision engine never parses textual content - it reads numbers. That's what makes the routing deterministic.
relevant is always on. The decision engine requires it, so the analysis toggles can disable the other six per request (send {"sentiment": false, ...} to skip dimensions and save time/money) but never relevance.
One call, seven answers. The full question set rides in a single {"model", "state", "questions"} POST. No N+1 fan-out, no stitching partial results.

6. Stage 4 - Decide: 12 lines that replace a triage meeting

The entire routing engine is make_decision() in backend/app/decision.py - ordered rules, first match wins:

def make_decision(jev_result):
    answers = jev_result.get("answers", jev_result)

    relevant = float(_answer(answers, "relevant").get("noul", 0.0))
    sentiment_conf = float(_answer(answers, "sentiment").get("confidence", 0.0))
    escalation = float(_answer(answers, "requires_escalation").get("noul", 0.0))

    churn = _answer(answers, "churn_risk").get("score")
    churn = float(churn) if churn is not None else 0.0

    if relevant < 0.60:
        return {"decision": "DISCARD", "reason": "Low customer relevance"}

    if sentiment_conf and sentiment_conf < 0.70:
        return {"decision": "QUEUE", "reason": "Low sentiment confidence"}

    if escalation >= 0.80 or churn >= 3.0:
        return {"decision": "ESCALATE", "reason": "Material customer risk signal"}

    return {"decision": "ANALYZE", "reason": "Relevant customer signal"}

Decision	Trigger	Meaning
`DISCARD`	relevance < 0.60	Not materially about CX - drop it
`QUEUE`	sentiment confidence < 0.70, or any processing error	Ambiguous - human review required
`ANALYZE`	relevant, confident, no strong risk signal	Feed to LLM deep analysis for insight
`ESCALATE`	escalation ≥ 0.80 or churn risk ≥ 3.0	Material risk - immediate attention

Why this ordering matters: relevance gates everything (don't score what isn't about you), confidence gates actionability (don't act on guesses), and risk short-circuits to escalation before the default ANALYZE path. Every branch returns a human-readable reason, so the routing is auditable - tune the four thresholds in one place to match your risk appetite.

7. Stage 5 - Deep analysis: spend the LLM only where it pays

analyze() in backend/app/deep_analysis.py builds a prompt from the evidence + Jev output and asks for seven structured fields: customer_issue, observed_sentiment, likely_root_cause, customer_impact, churn_implication, recommended_business_response, product_or_service_opportunity - with explicit instructions to separate observed facts from hypotheses and never invent unsupported facts. Temperature 0.1, 60s timeout, any OpenAI-compatible endpoint via OPENAI_BASE_URL.

The cost control is the point: in a typical 120-item run, ~98 items DISCARD, ~4 QUEUE, and only ~18 reach the LLM. You pay for insight on the 15% that matters, not tokens on the 85% that doesn't.

8. The interface: CLI, HTTP, and the payload contract

The FastAPI layer (backend/app/api.py) is thin by design - Pydantic validation, then straight into the pipeline:

@app.post("/customer-sentiment/analyze")
def analyze(request: SentimentRequest):
    try:
        settings = Settings.from_env()
        settings.validate()
        return CustomerSentimentPipeline(settings).run(
            company=request.company,
            source=request.source,
            location=request.location.model_dump() if request.location else None,
            search=request.search.model_dump(exclude_none=True),
            reviews=request.reviews.model_dump(exclude_none=True),
            include_news=request.include_news,
            analysis=request.analysis.model_dump(exclude_none=True),
            gl=request.gl,
            hl=request.hl,
        )
    except Exception as exc:
        raise HTTPException(status_code=500, detail=str(exc)) from exc

# run it
uvicorn app.api:app --reload   # from backend/
curl -X POST localhost:8000/customer-sentiment/analyze \
  -H 'Content-Type: application/json' \
  -d '{"company": "The Plaza", "location": {"city": "New York", "state": "NY", "country": "US"}}'

Config lives in backend/.env: SERPAPI_KEY (required), SYSTEM_ONE_PROVIDER (jev default / laya), then exactly one matching credential (TYPESAFE_API_KEY or LAYA_API_KEY) - Settings.validate() enforces this before any run. Optional: JEV_MODEL, JEV_BASE_URL, LAYA_*, OPENAI_ADMIN_KEY / OPENAI_MODEL / OPENAI_BASE_URL, CORS_ORIGINS.

Every run returns the same contract: company, evidence_count, summary (decision_counts + sentiment_counts), and results[] where each item holds evidence, jev (answers), decision (with reason), and deep_analysis for ANALYZE/ESCALATE - or an error record that still carries a QUEUE decision.

9. Extending it: four starting points

More sources. Add X/Twitter, Reddit, or app-store reviews in serpapi_client.py + evidence.py - the normalizer/dedup pattern already handles heterogeneous inputs.
Tune the thresholds. The four numbers in decision.py (0.60 / 0.70 / 0.80 / 3.0) encode a risk appetite. Calibrate against labeled data for your domain.
Point the LLM at a real endpoint. Set the OPENAI_* vars to swap prompt_only briefs for full structured responses.
Schedule it. Cron the CLI or poll the API on a timer and you've got a sentiment-tracking dashboard with trend history.

The test suite (backend/tests/: pipeline, decision, evidence, clients, CORS) runs under pytest, so extensions land with a safety net.

Key Insights

Pipelines are simply ordered steps that transform messy input into usable output.
APIs simplify data collection and reduce fragility compared to scraping.
Normalizing data upfront makes downstream analysis smoother.
AI can be framed as structured Q&A rather than opaque predictions.
Smart categorization helps teams focus on urgent or actionable feedback.
Cost‑effective design ensures scalability without overspending.

Building a Customer Sentiment Intelligence with SerpAPI + System One

Ranjan Dailata — Sat, 10 Oct 2026 17:43:04 +0000

Introduction

Imagine you just opened a coffee shop. Every day, new reviews roll in on Yelp, and news articles mention your shop here and there. Some are lovely. Some are angry. Most are... just noise.

Now imagine hundreds of them every week.

You want to read the ones that matter - the unhappy customer who's about to stop coming, the person begging for oat milk. But finding those few needles in a mountain of feedback is exhausting. By the time you spot the problem, the customer is already gone.

That's the exact problem this project solves. And the best part? the idea is simple enough that a total beginner can follow it.

In this post I'll walk you through Customer Sentiment Intelligence - a small Python project that reads public feedback and sorts it into four tidy buckets. If you've heard words like "API" or "AI" but never quite knew what they meant, you're in exactly the right place.

What you'll learn

By the end, you'll understand:

What a pipeline is (spoiler: it's just steps, like a recipe)
What an API is and why it saves you from writing scrapers
How a computer can "read" a review and give it a score
How a few simple rules can sort feedback automatically
A smart habit: only spend money where it counts

No math. No degree. Just curiosity.

The big idea, in one sentence

Feed a company name into the script, and it hands you back a neat list of every review and news article, each one understood, scored, and sorted into the right bucket.

Think of it like a friendly mail sorter at a post office. Mail comes in messy and mixed-up. The sorter reads each piece, decides what it is, and drops it into the right bin. The proposed solution does the same thing - but for customer feedback instead of letters.

Here's the journey a single review takes. It comes in messy, gets cleaned up into a consistent shape, gets read and scored, drops neatly into a bucket, and - only when it's important enough - gets a deeper AI look.

Five words you'll see, explained simply

Pipeline - A set of steps done in order, like following a recipe. Step 1, then Step 2, then Step 3. That's it. Nothing more mystical than that.
API - Short for "Application Programming Interface." Fancy name for "a way for two programs to talk to each other." Think of it like a waiter: you tell the waiter what you want, they go get it from the kitchen, and bring back your food. You never have to cook it yourself.
Sentiment - Just the feeling behind some words. Positive (happy), neutral (meh), or negative (upset).
Score - A number the computer gives something after "reading" it, like a rating out of 4 stars. It's the computer's way of saying "how big is this?"
Triage - A hospital word for "sort patients by how urgent they are." it's used to sort feedback by how much it needs your attention.

That's honestly most of the vocabulary. Let's build.

Step 1 - Collect the feedback (without writing a scraper)

First we need the reviews. You could try to grab them yourself by copying and pasting from the Yelp website - but that's slow, fragile, and against most sites' rules.

Instead, use a service called SerpApi. Remember the waiter analogy? SerpApi is our waiter for search data. You ask it, "Hey, find the Yelp page for this coffee shop and give me its latest reviews," and it hands back clean, organized data. No messy HTML, no scraping, no maintenance.

Under the hood, the script does two little requests. First it searches Yelp for the business and grabs its permanent ID - think of it like a library card number for that shop. Then it uses that ID to pull up to 100 recent reviews. As a bonus, it can also fetch the latest news articles that mention the shop, so you're not just looking at reviews but the wider conversation around the business too.

Step 2 - Clean it up (make everything match)

Here's a hidden headache in programming: data is messy. A Yelp review stores the date in one field, the rating in another, and the customer's name somewhere else entirely. A news article has completely different fields. Comparing them is like trying to read a book where every page is a different size and language.

So, before we do anything smart, we normalize everything - meaning we reshape every piece of feedback into the same format, with the same boxes to fill in. Every review and every article gets the same nine slots: a unique ID, the company name, where it came from, what kind of source it is, when it was published, its rating, who wrote it, the actual text, and a link back to the original.

Now every review and every news article looks identical underneath: same company, same date field, same text field, same URL. The rest of our pipeline never has to worry about where the data came from.

The duplicate problem (and a clever trick)

Sometimes the same review shows up twice - maybe it appeared in two different fetches. Re-reading it wastes time and can skew our counts. So, we deduplicate: remove copies so each real review is counted exactly once.

How do we know two reviews are "the same"? We give each one a fingerprint. Behind the scenes, a small helper takes the key pieces of a review - the company, the text, and the date - and squeezes them into a short, fixed-length code using a standard technique called hashing.

Don't let the word "hash" scare you. All it does is turn some text into a short code. Same input equals the same code, every single time. So if two reviews produce the same fingerprint, we know they're identical and keep only one. Empty reviews with no text at all get tossed out too.

Step 3 - Understand each review (the "AI" part, demystified)

Now for the fun part: teaching the computer to understand what a review is actually saying.

We send each cleaned-up review to a scoring service (System One model) and ask it seven simple questions about the review. That's the whole "AI" - it's just a set of questions asked over and over.

Here are the seven questions in plain English:

Is this even about the customer's experience? (Relevant?) - A review about the weather isn't useful to us.
How do they feel? (Sentiment) - Happy, meh, or upset?
What are they trying to do? (Intent) - Complaining? Praising? Asking a question? Requesting something?
What's it about? (Topic) - Pricing? Support? The product itself?
How serious is it? (Severity) - A tiny nitpick, or a serious problem? Scored 0 to 4.
Does a human need to step in? (Needs escalation?)
Could this customer leave? (Churn risk) - Scored 0 to 4.

Notice we're not asking the AI to write an essay. We're asking simple, specific questions - and it answers with one of three easy formats:

Yes/No with a confidence (like "90% sure this is relevant")
Pick one from a list (like "this is a complaint")
A number from 0 to 4 (like "severity level 3")

Those three answer styles have technical names - noul, choice, and score - but you can just think of them as yes/no, pick-one, and how-many-stars.

Here's what one of those questions looks like behind the curtain. The relevance question, for example, is set up to expect a simple yes-or-no answer with a confidence score attached. Its instructions read like an ordinary sentence - "Is this materially relevant to the customer's experience?" - and it even spells out what counts as yes (directly concerns the customer) versus no (nothing to do with the customer experience). That clarity is the whole point: the better you define the question, the better the answer.

Step 4 - Sort into buckets (the sorting rules)

This is my favorite part, because it's only about a dozen lines of code and it does something genuinely useful: it decides what to do with each review.

We sort every review into one of four buckets:

Bucket	What it means	What you should do
DISCARD	Not about the customer experience at all	Ignore it
QUEUE	Unclear or something went wrong	A human takes a quick look
ANALYZE	A real, useful signal	Worth a deeper look
ESCALATE	Serious - a customer might be at risk	Act now!

The rules are checked top to bottom, and the first one that matches wins - just like a checklist. In plain English, the engine reads the scores it got back and walks through them in order. First, it checks relevance: if the review isn't really about the customer experience, it's discarded - no point wasting anyone's time. Next, if the model wasn't confident about how the customer felt, it's queued for a human to make the call rather than guessing. Then it checks for danger: if the review needs escalation, or the churn risk is high, it's flagged for immediate action. And if none of that fire, the review is treated as a normal, useful signal that's worth a deeper look.

Every bucket also gets a short, human-readable reason attached - something like "Low customer relevance" or "Material customer risk signal" - so you always know why something landed where it did.

Read it top to bottom and you'll see it's just common sense wearing a costume:

If it's not relevant (score under 0.60), don't waste time - discard it.
If the model isn't confident about the feeling (under 0.70), don't guess - queue it for a human.
If it needs escalation (0.80+) or the churn risk is high (3.0+), escalate it immediately.
Everything else is a normal signal - analyze it.

Those four numbers (0.60, 0.70, 0.80, 3.0) are just knobs you can turn. If you want to be extra careful, lower them so more things get a human's eyes. They all live in one place, so tuning is easy.

Step 5 - The optional AI deep-dive (and how to keep costs down)

For reviews in the ANALYZE and ESCALATE buckets, you could ask an AI model for a full write-up: What's the real issue? What probably caused it? How might we respond?

Remember, most reviews get discarded as noise. Why pay an AI to analyze "5 stars, love it!"? We already know what to do with that - nothing. So, the expensive AI step runs only on the important buckets.

And if you don't have an AI key set up at all? No problem. Before it would call a paid model, the script checks whether one is actually configured. If not, it simply hands back the exact question it would have asked - labelled as a draft prompt - instead of a paid answer. The feature fails gracefully instead of breaking.

One more safety habit: nothing disappears

What happens if something goes wrong halfway through - say, the scoring service has a hiccup on one weird review? A naive script would crash and lose everything. Not this one.

Each review is handled inside its own little safety net. The script tries to score and sort each one, but if something goes wrong on a particular review, it doesn't panic and it certainly doesn't lose your work. Instead, it catches the problem, quietly notes what happened, and routes that single review to the QUEUE bucket - the "a human should peek at this" bin.

So if one weird review causes a hiccup, it doesn't take down the whole batch. It just gets parked for human review, and everything else keeps flowing.

Try it yourself

Download Source Code

Want to see it work? You'll need Python installed and a free SerpApi key (they have a free tier). Getting started takes three friendly steps. First, set up a virtual environment - think of it as a clean little room just for this project, so its tools don't mix with anything else on your computer. Second, install the project's dependencies and drop your API keys into a small settings file. Third, run the script from your terminal and tell it which company to look up, along with the city, state, and country so it can find the right place on Yelp. In other words: create the room, install the ingredients, then cook.

If you'd rather use it as a tiny web service instead of a one-off command, you can start the server and then send it a request over the internet, pointing it at the same company and location. It responds in exactly the same way - the web version just wraps the same pipeline behind a friendly address.

Either way, you get back one clean summary: how many reviews you found, how many were positive, neutral, or negative, and a bucket for every single one.

What I hope you take away

You don't need to be an expert to build something genuinely useful. This whole project rests on ideas any beginner can hold in their head:

Break the problem into steps - collect, clean, understand, sort. That's a pipeline.
Use services, don't reinvent them - APIs save you from fragile scraping.
Normalize your data early - one consistent shape makes everything downstream easier.
Ask clear questions - "AI" is often just structured questions with structured answers.
Simple ordered rules can be powerful - a dozen readable lines can replace hours of manual sorting.
Fail safe, spend smart - never let one error crash everything, and never pay for work you don't need.

If you've been waiting for a "big" project to feel ready for, this is your sign: the best projects are just small, clear ideas stacked together.

Quick glossary

Pipeline - Steps done in order, like a recipe.
API - A way for two programs to talk, like a waiter between you and the kitchen.
Sentiment - The feeling behind words: positive, neutral, or negative.
Score - A number rating something, like 0-to-4 stars.
Triage - Sorting things by how urgently they need attention.
Normalize - Reshaping messy data into one consistent format.
Deduplicate - Removing copies so each item is counted once.
Fingerprint (hash) - A short code that's identical for identical input; handy for spotting duplicates.
noul / choice / score - The three answer styles: yes/no-with-confidence, pick-one-from-a-list, and a 0-to-4 rating.

Key Takeaways

APIs simplify data collection and reduce maintenance.
Normalizing data upfront saves time later.
AI can be framed as structured Q&A rather than complex black‑box predictions.
Smart categorization helps teams focus on what matters most.
Cost‑effective design ensures scalability without overspending.

Beyond Prompt Engineering: Welcome to State Engineering with Jev

Ranjan Dailata — Sun, 20 Sep 2026 03:58:56 +0000

Introduction

Welcome to a fresh perspective on how AI is evolving beyond the usual "feed it everything and ask" approach. If you've ever felt overwhelmed by the sheer volume of data you toss at language models, you're not alone. In this blog post, you will be guided with a smarter, leaner way of designing AI systems one that swaps bulky documents for crisp, decision-ready states. It's about giving AI what it truly needs to decide, not just to read.

For years, the standard has been simple: dump the entire resume, job description, customer chat, or medical record into a language model, then ask it to analyze or recommend. This works, but it treats AI like a magic black box that must digest every detail every time slow, costly, and sometimes unreliable. Enter Jev Optimized State, a game-changing mindset and architecture that rethinks the entire process.

Why Send the Whole Document When You Only Need the Facts?

Imagine building a recruitment platform. Traditionally, you send a lengthy job description and candidate resume straight into the AI prompt and ask, "Is this person a good fit?" The AI then tries to parse every sentence, juggling nuances and details.

Now, picture something different: before involving the AI, you extract and distill the essential facts into a structured, compact state. For example, a candidate's years of experience, skills, education level, and leadership history are neatly summarized in a JSON object. The job description is transformed similarly into a list of requirements and preferences.

This isn't just summarization it's decision-oriented state. Where a summary tells you what a document is about, an optimized state tells you what matters for making a decision. It's like handing the AI a set of puzzle pieces instead of the entire box of mixed pieces.

From "Give AI Everything" to "Give AI Just What It Needs"

This shift changes how we think about AI apps. Instead of asking the AI to produce a final, often fuzzy judgment, you ask it to evaluate specific, well-defined questions:

Does the candidate meet the minimum experience?
Does the candidate have Python and FastAPI skills?
Is leadership experience present?

These focused queries let the AI provide calibrated, probabilistic answers for each piece of evidence. Then, simple code combines those answers to make clear-cut decisions based on business rules. This division makes AI a trusted judge of nuance, while code handles the firm logic.

The Powerful Mental Model Shift

You used to obsess over "prompt engineering." Now, the real magic lies in state engineering: crafting the most relevant, structured, and reusable representation of your data before it ever reaches the AI. Better data in, better judgments out.

Think of it as:

Better Evidence - Better State - Better Questions - Better Decision

This approach reduces token usage dramatically no more sending thousands of words over and over. Instead, a compact state plus a handful of precise questions keeps things efficient and scalable.

What Is "Jev Optimized State"?

I use the term Jev Optimized State to describe a state representation designed specifically for machine decision-making.

It is:

Compact
Relevant
Structured
Evidence-oriented
Reusable
Easy to evaluate
Free from unnecessary narrative

Practical Examples

Resume Evaluation

Imagine this raw resume:

John has spent the last seven years working on backend
applications. At ABC Corp he developed Python services
using FastAPI and PostgreSQL. He also worked extensively
with AWS and Docker. He led a team of six engineers...

Instead of repeatedly sending this paragraph to a model, extract:

{
  "candidate": {
    "experience": {
      "total_years": 7,
      "backend_years": 7
    },
    "skills": {
      "python": true,
      "fastapi": true,
      "postgresql": true,
      "aws": true,
      "docker": true
    },
    "leadership": {
      "has_led_team": true,
      "team_size": 6
    }
  }
}

Now the state is reusable.

You can ask:

Meets experience requirement?
Meets Python requirement?
Meets FastAPI requirement?
Meets AWS requirement?
Meets leadership requirement?

This Generalizes Beyond Recruiting. The same pattern applies almost everywhere.

Customer Support

Raw:

10-message customer conversation

Optimized state:

{
  "issue": "duplicate_charge",
  "customer_frustration": "high",
  "payment_completed": true,
  "refund_requested": true,
  "account_verified": true
}

Jev questions:

Is this a billing issue?
Is a refund requested?
Is escalation required?
Is identity verification complete?

Healthcare

Raw:

Patient chart
+
Payer policy

Optimized state:

{
  "diagnosis": "neovascular AMD",
  "requested_drug": "Drug A",
  "prior_therapy": ["Drug B"],
  "response": "inadequate",
  "documentation": {
    "diagnosis": true,
    "prior_treatment": true
  }
}

Jev questions:

Qualifying diagnosis?
Prior therapy satisfied?
Inadequate response documented?
Required evidence present?

Then deterministic policy logic can combine those results.

This is particularly interesting for a prior-authorization system because the model can act as a clinical evidence interpreter, while the policy engine remains responsible for deterministic policy logic.

Financial Services

Raw:

Bank statements
+
Transaction history
+
Customer profile

Optimized state:

{
  "monthly_income": 8500,
  "debt_ratio": 0.32,
  "late_payments_12m": 1,
  "employment_years": 5
}

Questions:

Is income stable?
Is there evidence of payment stress?
Is employment history sufficient?

Then deterministic rules calculate eligibility according to the business policy.

Beyond Recruiting: A Universal Pattern

This pattern works everywhere. In customer support, you distill a conversation into key facts like "billing issue," "refund requested," or "account verified," then ask targeted questions. In healthcare, patient charts and policies become structured evidence for clinical decisions. Financial services transform bank statements and transaction histories into concise financial health indicators.

Humans Move Up the Chain

Rather than reading every document and making every decision, humans now focus on designing the evidence model, defining decision questions, setting thresholds, and monitoring outcomes. We become architects of decision systems, guiding AI and code to work together seamlessly.

Don't Just Shrink Documents Optimize for Decisions

Beware of the trap: stuffing entire documents into JSON isn't optimization. The goal is to keep only what's relevant for the decision and discard the rest.

And don't lose the trail of evidence. Including links to source text and confidence scores builds auditability and trust, essential for real-world applications.

The New AI Stack: Layers of Responsibility

Think of AI systems as layered:

Humans: Define policies and governance.
Application Code: Enforces rules, workflows, and security.
Jev: Handles semantic judgments, scoring, and classification.
LLMs: Perform extraction, complex reasoning, and language generation.

Each has its role. Jev isn't a replacement for LLMs but a complementary layer focusing on structured, typed decisions.

The Big Takeaway

This is more than a technical tweak; it's a fundamental rethinking of how we build AI-powered systems. From:

Document - Prompt - LLM - Answer

To:

Document - Evidence - State - Questions - Decisions - Code - Outcome

And with that, we unlock better efficiency, clearer roles for AI and code, and systems that scale smarter.

Questions You Should Be Asking

Before diving into your next AI project, pause and ask yourself:

What decision am I really trying to make?
What state does the model actually need?
Which parts require AI's judgment and which suit deterministic logic?
What's truly relevant to this decision?
Can improving evidence and state trump changing the model?

Looking Ahead

The future isn't about picking "LLM vs. Jev." It's about smartly combining their strengths. LLMs excel at generation and reasoning, while Jev shines at producing reliable, structured judgments. Together, they empower your applications to be both insightful and dependable.

So here's the final thought: Instead of asking AI to make your entire decision, give AI the smallest meaningful state and ask the smallest meaningful question. That's the heart of Jev Optimized State, and it's a fresh, exciting way to build AI systems that truly work for us.

Building an Evidence-Based Resume Analyzer: Why Understanding Beats Guessing

Ranjan Dailata — Sat, 19 Sep 2026 11:46:06 +0000

Introduction

Resume screening often looks straightforward: hand a resume to an AI, ask if the candidate fits, get a yes or no. But if you've ever felt uneasy about how confidently these systems can talk without showing their work, you're not alone. In this post, I want to share an approach that feels more like a seasoned engineer explaining a tough problem straightforward, honest, and practical rather than a polished marketing pitch.

What we're really tackling is this: How can AI truly assess a resume without falling into the trap of just echoing buzzwords? How do we know the candidate isn't just sprinkling keywords but actually delivering on those claims? The answer is a layered architecture that separates understanding from evaluation, and it's built around two key players: a Large Language Model (LLM) and Jev System One.

When AI Sounds Confident but the Evidence Says Otherwise

Imagine you feed a resume into an LLM and ask, “Is this person an AI Architect?” The model might confidently say yes, citing cloud-native systems, AI, Kubernetes, and so on. Sounds great, right? But dig a little deeper and you'll find the resume might never say they designed an end-to-end AI architecture or managed latency and cost tradeoffs. The model is connecting dots it hopes are there, not dots explicitly proven.

Here's the catch: keywords like AWS, AI, LLM are not evidence. They're signals, yes, but they don't prove ownership, depth, or impact. Compare these two snippets:

Resume A: “Worked with OpenAI APIs.”
Resume B: “Designed and deployed an enterprise RAG platform using OpenAI models, Azure AI Search, and PostgreSQL, reducing response latency by 38%.”

Both mention AI-related terms, but only one shows real, measurable achievement. Our system needs to see evidence, not just buzzwords.

The Role of the LLM: Translator, Not Judge

LLMs shine at turning messy, varied human language into structured, meaningful data. Resumes are full of inconsistent terms, abbreviations, implicit meanings, and nuanced project descriptions that simple rules can't handle. The job of the LLM here is to extract that structured evidence things like project ownership, technologies used, outcomes achieved not just a list of skills.

For example, instead of just “Python, AWS, AI,” the LLM might output:

{
"project": "Enterprise Knowledge Platform",
"role": "Lead Architect",
"ownership": ["system architecture", "AI service architecture", "RAG pipeline"],
"technologies": ["Python", "OpenAI", "Azure"],
"ai_capabilities": ["LLM", "RAG"],
"production": true,
"impact": {"latency_reduction": "38%"}
}

That's the kind of evidence that can actually be evaluated.

Tokenize Evidence, Not Just Language

Traditional resume parsers see keywords. We want them to see relationships and ownership. Instead of just “AWS” or “AI,” we get:

Role = Lead Architect
Ownership = End-to-End AI System
System = RAG Platform
Impact = 38% latency reduction

This structured evidence gives us something concrete to evaluate.

Enter Jev System One: The Evidence Evaluator

Jev is the engine that takes this evidence and scores it against explicit questions and criteria. For example:

How much production AI architecture experience is demonstrated?
0 = No evidence
1 = Experimental work
…
5 = Enterprise-scale AI architecture ownership

Instead of a vague yes/no from the LLM, Jev provides a clear, reproducible score based on well-defined criteria. This transforms the question from “Is this person an AI Architect?” to “What does the evidence say about their AI architecture experience?”

Architecture

Demo

State

{
  "resume": "SASHA BERNOULLI\nSan Francisco, CA | sasha.bernoulli@email.com | github.com/sashabernoulli | linkedin.com/in/sashabernoulli\n\nPROFESSIONAL SUMMARY\nExperienced Product Engineer building developer-focused tools and platforms. Expertise in full-stack development with deep specialization in frontend architecture and UI/UX for technical audiences. Proven track record of shipping products that increase developer productivity and improve developer experience.\n\nEXPERIENCE\n\nSenior Product Engineer | CloudSync Systems | San Francisco, CA | Jan 2022 - Present\n- Led frontend architecture redesign for cloud orchestration dashboard, reducing initial load time by 65% and improving TypeScript coverage from 42% to 98%\n- Designed and implemented real-time collaboration features using WebSockets and Operational Transformation for multi-developer workflows\n- Built internal API gateway and request optimization layer (Node.js/Express) that reduced backend calls by 40%, improving dashboard responsiveness\n- Mentored 3 junior engineers on frontend best practices and code quality standards\n- Tech Stack: React, TypeScript, Redux, Node.js, PostgreSQL, AWS\n\nProduct Engineer | DevTools Lab | San Francisco, CA | May 2021 - Dec 2021\n- Architected and launched IDE plugin marketplace with 50k+ downloads; designed Vue.js frontend with Electron integration\n- Implemented backend services for plugin discovery, versioning, and analytics (Python/FastAPI) handling 2M+ monthly requests\n- Optimized plugin installation pipeline, reducing time from 45s to 8s through lazy-loading and caching strategies\n- Built comprehensive monitoring and error tracking for frontend and backend systems using Datadog and custom logging\n- Tech Stack: Vue.js, Python, FastAPI, PostgreSQL, Redis, Docker\n\nSoftware Engineer | Nexus Networks | San Francisco, CA | Jan 2021 - Apr 2021\n- Developed interactive network topology visualization tool using D3.js and WebGL for rendering 10k+ nodes in real-time\n- Created REST API endpoints for network state management and device configuration (Go/Gin framework)\n- Implemented real-time data sync between frontend and backend using gRPC, reducing latency by 50%\n- Contributed to SDK documentation and developer guides for third-party integrations\n- Tech Stack: React, D3.js, Go, PostgreSQL, Kubernetes\n\nJunior Software Engineer | CodePath Systems | San Francisco, CA | Jun 2020 - Jan 2021\n- Built responsive web interfaces for code analysis tools using React and CSS-in-JS\n- Developed backend microservices for code parsing and analysis (Java/Spring Boot)\n- Optimized database queries reducing API response times by 30%\n- Tech Stack: React, JavaScript, Java, Spring Boot, MySQL\n\nSKILLS\n\nFrontend: React, Vue.js, TypeScript, JavaScript (ES6+), HTML5, CSS3, D3.js, WebGL, Webpack, Tailwind CSS, Material-UI\nBackend: Node.js, Python (FastAPI), Go, Java (Spring Boot), SQL (PostgreSQL, MySQL), Redis, MongoDB\nDeveloper Tools: Git, Docker, Kubernetes, GitHub Actions, Datadog, New Relic\nSpecializations: Developer Experience, API Design, Real-time Systems, Performance Optimization, UI/UX for Technical Users\n\nEDUCATION\n\nB.S. Computer Science | University of California, Berkeley | 2019\nRelevant Coursework: Data Structures, Algorithms, Systems Design, Databases, Web Development\n\nCERTIFICATIONS & ACHIEVEMENTS\n- AWS Certified Solutions Architect (Associate) - 2021\n- Open Source Contributor: React Query (20+ merged PRs), Electron (5+ merged PRs)\n- Speaker: \"Building Developer-First UI\" at React Conference 2022"
}

Questions

{
  "years_of_experience": {
    "type": "score",
    "instructions": {
      "question": "How many years of professional experience does the candidate have?",
      "today": "2026-09-15"
    },
    "criteria": [
      "None",
      "2 years",
      "4 years",
      "6 years",
      "8 years",
      "10+ years"
    ]
  },
  "ai_architecture_depth": {
    "type": "score",
    "instructions": "Rate demonstrated AI architecture ownership from production work. Evaluate architecture, ownership, complexity, scale and measurable impact. Ignore titles, companies and skill lists. When uncertain, choose lower.",
    "criteria": [
      "No AI architecture experience.",
      "AI prototypes, coursework or experiments without production ownership.",
      "Implemented isolated AI/ML features within an existing architecture.",
      "Designed and shipped production AI components such as LLM applications, RAG, inference services or ML pipelines.",
      "Designed end-to-end AI systems spanning models, data, retrieval, orchestration, APIs, infrastructure and production operations.",
      "Enterprise-scale AI architecture ownership across systems/domains with platform strategy, architecture standards and organization-wide technical leadership."
    ]
  },
  "llm_experience": {
    "type": "noul",
    "instructions": "Has the candidate built production LLM-powered products, services or features?",
    "criteria": {
      "true": "Production LLM product or feature demonstrated.",
      "false": "No production LLM product or feature demonstrated."
    }
  },
  "rag_experience": {
    "type": "noul",
    "instructions": "Has the candidate designed or implemented a production RAG or retrieval system?",
    "criteria": {
      "true": "Production RAG/retrieval system demonstrated.",
      "false": "No production RAG/retrieval system demonstrated."
    }
  },
  "agent_experience": {
    "type": "noul",
    "instructions": "Has the candidate built AI agents or tool-using AI workflows?",
    "criteria": {
      "true": "AI agent or tool-using workflow demonstrated.",
      "false": "No AI agent experience demonstrated."
    }
  },
  "model_engineering": {
    "type": "score",
    "instructions": "Rate experience selecting, evaluating, fine-tuning, serving or optimizing AI/ML/LLM models.",
    "criteria": [
      "No model engineering experience.",
      "Consumed existing AI APIs/models only.",
      "Implemented model inference or basic model integration.",
      "Performed model evaluation, prompt optimization, embeddings, fine-tuning or model serving.",
      "Designed model strategy including selection, evaluation, fine-tuning, inference, cost and latency optimization.",
      "Advanced model/platform engineering including distributed training, quantization, inference optimization or large-scale model serving."
    ]
  },
  "ai_data_architecture": {
    "type": "score",
    "instructions": "Rate AI data architecture experience including ingestion, processing, embeddings, vector stores, knowledge bases, retrieval, data quality and governance.",
    "criteria": [
      "No AI data architecture.",
      "Basic datasets for AI experimentation.",
      "Basic data preparation, embeddings or vector search.",
      "Production AI data or retrieval pipelines.",
      "End-to-end AI data architecture covering ingestion, transformation, embeddings, storage, retrieval and governance.",
      "Large-scale enterprise AI data platform architecture with multimodal data, lineage, quality and high-volume retrieval."
    ]
  },
  "cloud_ai_architecture": {
    "type": "score",
    "instructions": "Rate production AI infrastructure experience across cloud, containers, orchestration, compute, GPU, storage, deployment and observability.",
    "criteria": [
      "No AI infrastructure experience.",
      "Basic cloud deployment.",
      "Deployed AI services using cloud or containers.",
      "Designed production AI services with cloud, APIs and monitoring.",
      "Designed scalable AI infrastructure including compute/GPU, orchestration, networking, storage, CI/CD and observability.",
      "Enterprise AI infrastructure across environments with scalability, resilience, security, cost and performance optimization."
    ]
  },
  "ai_platform_experience": {
    "type": "noul",
    "instructions": "Has the candidate built reusable AI platforms, shared AI services, model gateways, AI APIs or internal AI infrastructure?",
    "criteria": {
      "true": "Reusable AI platform or shared AI infrastructure demonstrated.",
      "false": "No reusable AI platform experience demonstrated."
    }
  },
  "ai_security_governance": {
    "type": "score",
    "instructions": "Rate AI security and governance experience including privacy, access control, model security, prompt security, auditability, responsible AI and compliance.",
    "criteria": [
      "No AI security or governance evidence.",
      "General application or cloud security only.",
      "Basic AI access control or data protection.",
      "Production AI security, privacy or governance controls.",
      "AI security and governance architecture covering data, models, prompts, access, auditing and compliance.",
      "Enterprise AI security and governance standards across multiple AI systems."
    ]
  },
  "ai_observability": {
    "type": "noul",
    "instructions": "Has the candidate implemented production AI observability covering quality, latency, cost, usage, failures, tracing or drift?",
    "criteria": {
      "true": "Production AI observability demonstrated.",
      "false": "No AI observability demonstrated."
    }
  },
  "ai_evaluation": {
    "type": "noul",
    "instructions": "Has the candidate implemented systematic AI/LLM evaluation?",
    "criteria": {
      "true": "AI/LLM evaluation using datasets, benchmarks, human evaluation or automated metrics demonstrated.",
      "false": "No systematic AI/LLM evaluation demonstrated."
    }
  },
  "ai_cost_optimization": {
    "type": "noul",
    "instructions": "Has the candidate optimized AI/LLM infrastructure or inference cost?",
    "criteria": {
      "true": "AI/LLM cost optimization demonstrated.",
      "false": "No AI/LLM cost optimization demonstrated."
    }
  },
  "architecture_tradeoffs": {
    "type": "score",
    "instructions": "Rate demonstrated AI architecture tradeoffs involving model, accuracy, latency, scalability, cost, reliability, security and maintainability.",
    "criteria": [
      "No architecture tradeoff evidence.",
      "Basic implementation choices only.",
      "Localized technical tradeoffs.",
      "Production architecture tradeoffs across multiple components.",
      "Explicit AI tradeoffs across model, data, infrastructure, cost, latency, security and reliability.",
      "Defines organization-wide AI architecture principles and platform tradeoffs."
    ]
  },
  "technical_leadership": {
    "type": "score",
    "instructions": "Rate technical leadership through architecture ownership, design reviews, mentoring, standards, technical direction and cross-team influence.",
    "criteria": [
      "No technical leadership evidence.",
      "Limited technical collaboration.",
      "Component or feature-level technical ownership.",
      "Leads projects or technical initiatives.",
      "Leads architecture across teams and mentors engineers.",
      "Defines enterprise AI architecture strategy and influences multiple engineering organizations."
    ]
  },
  "mentorship_demonstrated": {
    "type": "noul",
    "instructions": "Does the resume demonstrate mentoring or coaching engineers?",
    "criteria": {
      "true": "Mentoring or coaching demonstrated.",
      "false": "No mentoring or coaching demonstrated."
    }
  },
  "ai_product_experience": {
    "type": "noul",
    "instructions": "Has the candidate built AI capabilities used by customers, users or business operations?",
    "criteria": {
      "true": "Production AI capability with demonstrated users or business usage.",
      "false": "No demonstrated production AI product usage."
    }
  },
  "career_progression": {
    "type": "choice",
    "instructions": "What career progression pattern is demonstrated?",
    "criteria": {
      "steady_growth": "Increasing technical scope, ownership and seniority.",
      "architecture_growth": "Progression toward architecture and technical leadership.",
      "lateral_moves": "Similar responsibility across organizations.",
      "job_hopping": "Frequent moves with short tenure.",
      "unclear": "Progression cannot be determined."
    }
  },
  "primary_talent_profile": {
    "type": "choice",
    "instructions": "Select the primary AI/engineering profile from demonstrated experience. Weight recent production work most heavily. Ignore titles and skill lists.",
    "criteria": {
      "ai_architect": "End-to-end AI architecture; model, data, infrastructure, security, scalability and cost tradeoffs.",
      "ml_ai_engineer": "Model training, fine-tuning, evaluation, inference or ML engineering.",
      "llm_engineer": "LLM applications, RAG, agents, inference, evaluation and orchestration.",
      "ai_platform_engineer": "Reusable AI platforms, model gateways, infrastructure, deployment and observability.",
      "data_ai_engineer": "AI data pipelines, embeddings, vector systems and data platforms.",
      "cloud_architect": "Cloud and distributed-system architecture with limited AI specialization.",
      "software_architect": "Large-scale software architecture without strong AI specialization.",
      "research_engineer": "AI/ML research, experimentation, training and novel algorithms.",
      "other": "Other engineering profile."
    }
  }
}

Jev Response

{
  "model": "jev-1.13.0",
  "answers": {
    "years_of_experience": {
      "type": "score",
      "score": 2.98,
      "legend": {
        "0": "None",
        "1": "2 years",
        "2": "4 years",
        "3": "6 years",
        "4": "8 years",
        "5": "10+ years"
      },
      "confidence": 0.98,
      "probabilities": {
        "0": 0,
        "1": 0,
        "2": 0.02,
        "3": 0.97,
        "4": 0.01,
        "5": 0
      },
      "stats": {}
    },
    "ai_architecture_depth": {
      "type": "score",
      "score": 0,
      "legend": {
        "0": "No AI architecture experience.",
        "1": "AI prototypes, coursework or experiments without production ownership.",
        "2": "Implemented isolated AI/ML features within an existing architecture.",
        "3": "Designed and shipped production AI components such as LLM applications, RAG, inference services or ML pipelines.",
        "4": "Designed end-to-end AI systems spanning models, data, retrieval, orchestration, APIs, infrastructure and production operations.",
        "5": "Enterprise-scale AI architecture ownership across systems/domains with platform strategy, architecture standards and organization-wide technical leadership."
      },
      "confidence": 1,
      "probabilities": {
        "0": 1,
        "1": 0,
        "2": 0,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "llm_experience": {
      "type": "noul",
      "noul": 0.04,
      "stats": {}
    },
    "rag_experience": {
      "type": "noul",
      "noul": 0.06,
      "stats": {}
    },
    "agent_experience": {
      "type": "noul",
      "noul": 0.03,
      "stats": {}
    },
    "model_engineering": {
      "type": "score",
      "score": 0,
      "legend": {
        "0": "No model engineering experience.",
        "1": "Consumed existing AI APIs/models only.",
        "2": "Implemented model inference or basic model integration.",
        "3": "Performed model evaluation, prompt optimization, embeddings, fine-tuning or model serving.",
        "4": "Designed model strategy including selection, evaluation, fine-tuning, inference, cost and latency optimization.",
        "5": "Advanced model/platform engineering including distributed training, quantization, inference optimization or large-scale model serving."
      },
      "confidence": 1,
      "probabilities": {
        "0": 1,
        "1": 0,
        "2": 0,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "ai_data_architecture": {
      "type": "score",
      "score": 0,
      "legend": {
        "0": "No AI data architecture.",
        "1": "Basic datasets for AI experimentation.",
        "2": "Basic data preparation, embeddings or vector search.",
        "3": "Production AI data or retrieval pipelines.",
        "4": "End-to-end AI data architecture covering ingestion, transformation, embeddings, storage, retrieval and governance.",
        "5": "Large-scale enterprise AI data platform architecture with multimodal data, lineage, quality and high-volume retrieval."
      },
      "confidence": 1,
      "probabilities": {
        "0": 1,
        "1": 0,
        "2": 0,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "cloud_ai_architecture": {
      "type": "score",
      "score": 0.4,
      "legend": {
        "0": "No AI infrastructure experience.",
        "1": "Basic cloud deployment.",
        "2": "Deployed AI services using cloud or containers.",
        "3": "Designed production AI services with cloud, APIs and monitoring.",
        "4": "Designed scalable AI infrastructure including compute/GPU, orchestration, networking, storage, CI/CD and observability.",
        "5": "Enterprise AI infrastructure across environments with scalability, resilience, security, cost and performance optimization."
      },
      "confidence": 0.73,
      "probabilities": {
        "0": 0.62,
        "1": 0.37,
        "2": 0.01,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "ai_platform_experience": {
      "type": "noul",
      "noul": 0.03,
      "stats": {}
    },
    "ai_security_governance": {
      "type": "score",
      "score": 0.03,
      "legend": {
        "0": "No AI security or governance evidence.",
        "1": "General application or cloud security only.",
        "2": "Basic AI access control or data protection.",
        "3": "Production AI security, privacy or governance controls.",
        "4": "AI security and governance architecture covering data, models, prompts, access, auditing and compliance.",
        "5": "Enterprise AI security and governance standards across multiple AI systems."
      },
      "confidence": 0.98,
      "probabilities": {
        "0": 0.98,
        "1": 0.02,
        "2": 0,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "ai_observability": {
      "type": "noul",
      "noul": 0.03,
      "stats": {}
    },
    "ai_evaluation": {
      "type": "noul",
      "noul": 0.02,
      "stats": {}
    },
    "ai_cost_optimization": {
      "type": "noul",
      "noul": 0.03,
      "stats": {}
    },
    "architecture_tradeoffs": {
      "type": "score",
      "score": 0.01,
      "legend": {
        "0": "No architecture tradeoff evidence.",
        "1": "Basic implementation choices only.",
        "2": "Localized technical tradeoffs.",
        "3": "Production architecture tradeoffs across multiple components.",
        "4": "Explicit AI tradeoffs across model, data, infrastructure, cost, latency, security and reliability.",
        "5": "Defines organization-wide AI architecture principles and platform tradeoffs."
      },
      "confidence": 0.99,
      "probabilities": {
        "0": 1,
        "1": 0,
        "2": 0,
        "3": 0,
        "4": 0,
        "5": 0
      },
      "stats": {}
    },
    "technical_leadership": {
      "type": "score",
      "score": 3.34,
      "legend": {
        "0": "No technical leadership evidence.",
        "1": "Limited technical collaboration.",
        "2": "Component or feature-level technical ownership.",
        "3": "Leads projects or technical initiatives.",
        "4": "Leads architecture across teams and mentors engineers.",
        "5": "Defines enterprise AI architecture strategy and influences multiple engineering organizations."
      },
      "confidence": 0.77,
      "probabilities": {
        "0": 0,
        "1": 0,
        "2": 0,
        "3": 0.66,
        "4": 0.34,
        "5": 0
      },
      "stats": {}
    },
    "mentorship_demonstrated": {
      "type": "noul",
      "noul": 0.98,
      "stats": {}
    },
    "ai_product_experience": {
      "type": "noul",
      "noul": 0.05,
      "stats": {}
    },
    "career_progression": {
      "type": "choice",
      "choice": "architecture_growth",
      "confidence": 0.36,
      "probabilities": {
        "architecture_growth": 0.49,
        "job_hopping": 0.04,
        "unclear": 0,
        "steady_growth": 0.47,
        "lateral_moves": 0
      },
      "stats": {}
    },
    "primary_talent_profile": {
      "type": "choice",
      "choice": "software_architect",
      "confidence": 0.91,
      "probabilities": {
        "other": 0.06,
        "software_architect": 0.92,
        "data_ai_engineer": 0,
        "cloud_architect": 0.02,
        "ai_architect": 0,
        "llm_engineer": 0,
        "research_engineer": 0,
        "ai_platform_engineer": 0,
        "ml_ai_engineer": 0
      },
      "stats": {}
    }
  },
  "usage": {
    "input_tokens": 3087,
    "output_tokens": 496
  },
  "request_id": "playground_102ab50788afc5c4af6b3ce8c598a6cfd06",
  "evaluation_time_ms": 174.11698400246678
}

Clear, Traceable, and Controlled

One of the biggest wins here is transparency. Every score or label is traceable to explicit pieces of evidence like quotes from the resume or extracted facts. This matters for fairness, auditability, and trust. Recruiters and candidates alike can see the “why” behind the evaluation.

And importantly, once Jev has made its evaluation, the final LLM tasked with writing the report cannot rewrite or inflate scores. It simply explains what's there. The human recruiter then makes the final call.

Why Ownership Matters More Than Exposure

Anyone can say they “worked with AWS.” But did they architect a system? Did they lead the design? Keywords often blur this distinction. Our model focuses on evidence of ownership verbs like “designed,” “implemented,” “led,” and “optimized.” These words signal responsibility and impact, not just presence.

Building an Evidence Matrix, Not a Magic Score

Instead of a mysterious “Candidate Score: 72,” the system produces a matrix showing strengths and gaps:

Dimension	Evidence	Result
Software Architecture	Strong	High
AI Architecture	Limited	Low
RAG	Not Found	Not Demonstrated

This helps hiring managers see exactly what's supported by the resume and what isn't.

Beyond Resumes: A Universal Pattern

This approach isn't limited to recruiting. Anywhere you have unstructured data and need fair, explainable evaluation clinical documents, contracts, policies this layered architecture applies:

LLM extracts structured evidence
Jev evaluates against explicit criteria
Human makes the final decision

Final Thoughts: Empowering, Not Replacing Humans

The temptation to automate hiring decisions entirely is big. But resumes only tell part of the story. Interviews, references, communication skills, motivation all matter too. This system doesn't replace recruiters; it empowers them with clearer, more trustworthy evidence.

Think of it like a courtroom: The LLM gathers and organizes facts, Jev evaluates those facts against the law, and the judge (the human) makes the final judgment. That separation is what makes the whole process fairer, more transparent, and ultimately more effective.

The Introvert with AI Intelligence: How AI Can Turn Deep Thinking into Lightning-Fast Innovation

Ranjan Dailata — Sun, 16 Aug 2026 09:16:55 +0000

Introduction

Deep thought meets fast innovation.

Introverts have always been the quiet architects of progress thinkers who thrive in solitude, dissect complexity, and uncover insights that others overlook. Their strength lies not in speaking the loudest, but in thinking the deepest. Yet in a world obsessed with speed, their deliberate pace can feel out of sync with the demands of rapid innovation.

This is where AI becomes a powerful ally. By pairing the reflective depth of introverted minds with the lightning-fast execution of intelligent machines, it unlocks a new paradigm: ideas born from patience and contemplation can now be transformed into breakthroughs at unprecedented speed. AI doesn't replace the introvert's gift of deep thinking; it amplifies it, turning quiet wisdom into bold innovation that can reshape industries overnight.

The Introvert and the Information Problem

Introverts are often misunderstood. Being an introvert doesn't mean someone dislikes people or avoids collaboration. It usually means they prefer thoughtful conversations over constant chatter, and they often need time to process before responding.

Many introverts naturally lean toward independent exploration. When faced with something unfamiliar, their instinct is often: "Let me figure this out first." They read, they reflect, they experiment quietly, and only after building some understandings do they reach out to others.

From the outside, this can sometimes look slower because they aren't immediately asking for help. But in reality, they're diving deeper. They're exploring the problem from multiple angles, building a foundation of knowledge before stepping into the conversation.

This is where AI becomes a powerful ally. Instead of spending hours sifting through endless articles or resources, introverts can engage in a dialogue with AI. They can start with a simple question, then gradually build on it, layer by layer, until they've uncovered a complete picture.

It's not just about consuming information anymore. It's about interacting with information asking, challenging, exploring, and learning in a way that feels natural to them.

For introverts, this means they don't have to be the loudest voice in the room to make an impact. They can walk in with clarity, confidence, and a well‑thought‑out perspective. And with AI amplifying their deep thinking, their quiet strength becomes lightning‑fast innovation.

The Future Isn't Human vs. AI

The most interesting future isn't about humans competing against AI. It's about humans who use AI compared to those who don't.

The person who learns how to work with AI will naturally have an advantage. Not because AI will magically do everything for them, but because it will help them explore more ideas, test more possibilities, learn faster, and move from concept to reality more quickly.

That advantage grows over time. One idea becomes a prototype. The prototype becomes a product. The product generates feedback. The feedback sparks another idea. And the cycle continues, faster and faster.

This isn't a story of replacement. It's a story of amplification. AI doesn't take away human creativity or judgment, it multiplies it. The future belongs to those who know how to combine their own curiosity and insight with the speed and scale that AI makes possible.

Conclusion

The future belongs to those who figure it out with AI by their side.

There was a time when solving complex problems felt out of reach, not because of a lack of ability or determination, but because the right knowledge was inaccessible. Today, that barrier is shrinking. What once seemed impossible can now unfold step by step: a technical challenge becomes a research dialogue, a vague idea evolves into an architecture, a prototype transforms into a product, and that product reaches customers faster than ever before.

For introverts, this shift is especially empowering. You no longer need to have every answer or dominate the room to begin. You can start quietly, ask questions, explore, and build. By the time you step forward, you may not be the loudest voice, but you’ll be the one who has examined the problem from every angle.

This is the true promise of AI: not to replace thinking, but to deepen it to help us explore further and move faster. The future of software development will belong to those who combine three forces seamlessly: human curiosity, engineering judgment, and AI intelligence.

Perhaps the most powerful mindset for the next generation of builders is not "I already know how to do this" but "I don't know how to do this yet, but I can figure it out." With AI at our fingertips, that statement has never carried more potential.

Content Credits - This blog-post contents were formatted with ChatGPT to make it more professional and produce a polished content for the targeted audience.

Train your AI to engineer with empathy, not just efficiency

Ranjan Dailata — Fri, 07 Aug 2026 08:26:12 +0000

Background

Artificial Intelligence has rapidly transformed the way software is built. Modern coding agents can scan entire repositories, refactor thousands of lines of code, and even coordinate multiple specialized agents in parallel. With larger context windows, they can "remember" architecture, documentation, and business logic across vast projects something that once required weeks of human effort.

Introduction

We're living in a moment where AI can churn out code faster than any human ever could. But speed isn't the same as wisdom. Great engineering has never been about typing lines of code; it's about asking the hard questions, anticipating risks, and knowing when not to build.

Yet most AI tools today resemble eager junior developers: they recognize patterns, generate code, and quickly move on; but they rarely pause to question the ‘why’ behind their work. What they lack is the judgment, empathy, and tribal knowledge that seasoned engineers bring to every decision. If we want AI to be more than a code factory, we need to teach it to think like best engineers; cautious, curious, and deeply human in its approach to problem-solving.

Memorization vs. Generation in AI

Think of AI like a student who has read millions of books:

Memorization is when the student repeats a passage word‑for‑word because they’ve seen it so many times.

Generation is when the student uses what they’ve learned patterns of language, logic, and style to write something new that sounds right, even if they’ve never seen that exact sentence before.

Coding agents powered by Large Language Models (LLMs) typically generate code by predicting the next token based on the surrounding context. However, when handling common algorithms, boilerplate patterns, or widely circulated code snippets, they can sometimes reproduce memorized content instead of synthesizing a novel solution.

Understanding vs Memorizing

Modern AI tools (like GitHub Copilot or ChatGPT) can read your entire project in seconds. That’s like giving someone a library card to every document in your company.

But ask yourself: If you read every medical textbook, does that make you an experienced surgeon?

No! Because information is not the same as experience. The AI knows what your code looks like. But it doesn't know why a messy piece of code was written that way to stop a server crash three years ago.

Engineering Judgment

Many people think a programmer's job is just typing code into a computer all day.

In reality, experienced engineers spend most of their time asking questions before they type anything:

Why are we building this?
What happens if this breaks?
Is there a simpler way to solve this without writing new code?

Writing code is easy. Knowing what not to write is the hard part.

The Rules AI Doesn't Know

Every team has unspoken rules learned from past mistakes. We call this tribal knowledge:

Don't touch the billing system at the end of the month!
Leave that weird bug alone our biggest customer actually relies on it!
We tried that 'clean' idea two years ago and it crashed the website.

Right now, AI doesn't know these stories. If you ask AI to "clean up" old code, it might accidentally delete a safety check that was put there for a very good reason.

What a "Smart AI Partner" Would Look Like

Imagine if AI didn't just follow orders blindly but acted like a helpful senior coworker who watches your back. Here are few examples.

Deleting Code

Basic AI: "Done! I deleted that code for you."
Smart AI Partner: "Wait! I checked the history and that code was added after a bug where customers were charged twice. Are you sure you want to delete it?"

Cleaning Up Old Code

Basic AI: "I rewrote 500 lines of code to make it look modern and neat!"
Smart AI Partner: "Wait! This code hasn't broken in 2 years, and we have a big product launch next week. Should we leave it alone for now, so we don't risk breaking anything?"

There Is No "Perfect" Code

In school, there is usually one right answer. In software engineering, everything is a trade-off:

Sometimes simple and repetitive code is better than fancy, complicated code.
Sometimes a team writes quick and messy code on purpose to launch a product on time.

An AI that only knows textbook rules will try to make everything "perfect." A true engineering AI understands real-world trade-offs.

Conclusion

AI's future in software development won't be defined by how quickly it can generate code, but by how wisely it can help us decide what code should be written and when it shouldn't. True engineering is about judgment, trade-offs, and empathy for the people who rely on the systems.

To make AI a genuine partner, you must teach it to pause, ask "why" and respect the messy realities of deadlines, risks, and tribal knowledge that shape real-world engineering. Progress lies not in replacing human judgment but in amplifying it.

Key Takeaways for Developers:

Encourage AI tools to act like senior engineers questioning assumptions, flagging risks, and suggesting restraint when needed.

Recognize that "perfect" code is a myth; engineering is about making the best trade-offs for the situation.

Use AI to enhance decision-making, not just accelerate typing.

Content Credits - This blog-post contents were formatted with ChatGPT to make it more professional and produce a polished content for the targeted audience.

A Beginner's Guide to Smarter Web Automation with NodeMaven Residential Proxies

Ranjan Dailata — Tue, 14 Jul 2026 02:34:54 +0000

Introduction

If you've ever built a web scraper, automated browser workflow, or AI agent, you've probably encountered one of these errors:

HTTP 403 Forbidden
HTTP 429 Too Many Requests
Endless CAPTCHAs
Temporary IP bans

For many beginners, it feels like websites are actively working against automation.

The reality is that modern websites use sophisticated anti-bot systems that monitor IP addresses, request frequency, browser fingerprints, and user behavior. If all your requests come from a single IP address, you'll likely hit rate limits very quickly.

This is where residential proxies come in.

In this blog post, you will be guided and provided help you understand what residential proxies are, why they're useful, and how NodeMaven Residential Proxies can help you build more reliable automation, AI agents, and data collection workflows.

What is a Residential Proxy?

A residential proxy routes your internet requests through a real IP address assigned by an Internet Service Provider (ISP). Instead of websites seeing your computer's IP address, they see a legitimate residential IP from another location.

Unlike datacenter proxies, residential proxies appear much more like normal user traffic, making them well suited for applications such as:

Web scraping
AI agents
Market research
SEO monitoring
Ad verification
Price tracking
Geo-targeted testing

NodeMaven provides residential proxies with a global pool of over 30 million residential IPs, supporting targeting by country, city, ZIP code, and ISP, along with both rotating and sticky sessions.

Why Choose NodeMaven?

There are many proxy providers available today, but NodeMaven focuses on IP quality and session stability rather than simply advertising a large IP pool.

Some notable features include:

30M+ residential IPs
Coverage across 190+ countries
HTTP and SOCKS5 support
Rotating or sticky sessions
Country, city, ISP, and ZIP-level targeting
Unlimited concurrent sessions
Real-time IP quality filtering
AI automation and scraping friendly infrastructure

Real-World Use Cases

1. AI Company Research

Imagine building an AI assistant that researches companies.

Your workflow could:

Visit company websites
Read blogs
Analyze press releases
Collect leadership information
Summarize findings using an LLM

Residential proxies help maintain reliable access across many websites without relying on a single IP address.

2. SEO Rank Tracking

Need to know how your website ranks in Germany?

Or how Google results appear in India?

NodeMaven lets you route requests through specific countries or cities so you can collect location-specific search results.

3. Competitor Monitoring

Many companies automatically monitor competitors for:

Pricing changes
New products
Documentation updates
Blog posts
Careers pages

Instead of checking manually every day, automation can do the work.

4. AI Knowledge Collection

If you're building a Retrieval-Augmented Generation (RAG) application, you'll need fresh data.

Residential proxies make it easier to crawl public documentation, knowledge bases, and product pages at scale, helping keep your AI assistant's knowledge current.

5. Price Monitoring

E-commerce prices often vary based on region.

Using geo-targeted residential proxies allows you to compare pricing as users in different locations would see it.

Sticky vs Rotating Sessions

One feature beginner often overlook is the difference between sticky and rotating sessions.

Rotating Sessions

A new IP is used for every request.

Best for:

Web scraping
Search monitoring
Market research

Sticky Sessions

The same IP is maintained for a period of time.

Best for:

Logged-in sessions
Browser automation
Long-running AI agents
Account management

NodeMaven allows you to configure either mode depending on your workload.

Getting Started

Setting up NodeMaven is straightforward:

Create a NodeMaven account.
Retrieve your proxy username and password from the dashboard.
Choose your target location and session type.
Select HTTP or SOCKS5.
Configure your application or automation tool with the generated proxy endpoint.

NodeMaven's documentation includes examples for cURL, browser automation tools, and custom applications.

Best Practices

To get the most out of residential proxies:

Rotate IPs for high-volume scraping.
Use sticky sessions for authenticated workflows.
Respect website terms of service and robots.txt where applicable.
Add reasonable delays between requests.
Retry failed requests with exponential backoff.
Cache responses when possible to reduce unnecessary traffic.

Good automation is efficient and respectful not just fast.

Conclusion

Residential proxies are no longer just for large enterprises. Whether you're building your first web scraper, experimenting with AI agents, monitoring SEO rankings, or collecting public data for a RAG application, they can make your automation more reliable and location aware.

NodeMaven combines a large residential IP network with features like geo-targeting, sticky sessions, HTTP/SOCKS5 support, and IP quality filtering, making it a practical option for developers who want to build resilient automation without spending time constantly dealing with blocked requests.

How Modern Platforms Like PawfectNotes Help Veterinarians Spend More Time with Patients

Ranjan Dailata — Sat, 11 Jul 2026 04:59:15 +0000

Introduction

When people think about artificial intelligence in healthcare, they often imagine AI diagnosing diseases or replacing doctors. In reality, some of the most impactful AI applications are solving a much simpler but incredibly important problem: documentation.

Veterinarians spend a significant portion of their day documenting consultations. Every appointment generates patient histories, clinical observations, assessments, treatment plans, discharge instructions, referral letters, and updates to the clinic's Practice Management System (PMS). While documentation is essential for continuity of care, it also consumes valuable time that could otherwise be spent interacting with patients and their owners.

This is where AI-powered documentation assistants such as "PawfectNotes" come into the picture. Rather than replacing clinical expertise, they act as intelligent assistants that automate repetitive administrative work while keeping veterinarians in complete control of the final medical record.

In this blog post, you will be guided on how these platforms work from both a clinical and engineering perspective, making it easy for beginners to understand the technologies behind them.

Why Documentation Is a Challenge in Veterinary Medicine

Unlike many industries, veterinary consultations are highly conversational.

A pet owner may begin by describing subtle behavioral changes, eating habits, sleeping patterns, or symptoms that have evolved over several days. The veterinarian asks follow-up questions, performs a physical examination, discusses possible diagnoses, recommends treatment options, and provides instructions for ongoing care.

By the time the consultation ends, an enormous amount of information has been exchanged.

The challenge is that every clinically relevant detail needs to be translated into structured documentation. That includes:

Patient history
Presenting complaints
Clinical observations
Physical examination findings
Assessment
Treatment recommendations
Prescribed medications
Follow-up instructions
Referral information
Billing notes

Completing this documentation manually for every consultation quickly becomes one of the largest administrative burdens in a veterinary practice.

How AI Fits into the Picture

One of the biggest misconceptions about AI documentation systems is that they "write medical records". They don't.

Instead, they perform a series of smaller tasks that together transform an ordinary conversation into structured clinical documentation.

The veterinarian continues to conduct the consultation exactly as they always have. The AI simply observes the conversation, extracts clinically relevant information, organizes it into familiar medical formats, and prepares a draft for review.

This distinction is important because the veterinarian remains responsible for reviewing, editing, and approving every generated document before it becomes part of the patient's permanent medical record.

In other words, AI assists it does not replace clinical judgment.

The Journey from Conversation to Clinical Notes

Every AI documentation platform begins with the consultation itself.

As the veterinarian and pet owner speak naturally, the conversation is captured as audio. Modern speech recognition models then convert this audio into a written transcript with remarkable accuracy.

However, a transcript alone has limited value.

The sentence:

"Bella hasn't been eating much over the past three days and started vomiting yesterday". Is simply text.

The AI must understand that:

Bella is the patient.
Reduced appetite is a symptom.
Vomiting is another symptom.
Symptoms have persisted for three days.
The owner is reporting the information.
These observations belong in the Subjective section of a SOAP note.

This process moves beyond transcription and enters the domain of Natural Language Processing (NLP), where the model identifies medical entities, relationships, timelines, and clinical context.

Only after these pieces are understood can a Large Language Model generate meaningful documentation.

Why Large Language Models Are So Effective

Large Language Models excel at organizing unstructured information.

Clinical conversations rarely follow a neat structure. Veterinarians frequently move between symptoms, examination findings, owner questions, treatment options, and future recommendations.

An experienced clinician can mentally organize this information.

A properly prompted LLM attempts to do the same.

Instead of simply summarizing the conversation, it reorganizes the information into familiar clinical formats such as:

SOAP Notes
Referral Letters
Progress Notes
Surgery Reports
Dental Records
Discharge Instructions

The impressive part is that most of these documents are derived from the same consultation rather than being written independently.

Understanding SOAP Notes

One of the most common outputs produced by AI documentation platforms is the SOAP note.

SOAP stands for:

Subjective - Information provided by the owner, including symptoms and concerns.
Objective - Findings observed during the physical examination or diagnostic tests.
Assessment - The veterinarian's clinical interpretation of the findings.
Plan - Recommended treatment, medications, diagnostics, and follow-up care.

This format has been widely used across healthcare because it presents clinical information in a consistent, easy-to-review structure.

Instead of typing each section manually, the AI prepares an initial draft that the veterinarian can quickly validate and modify.

Beyond SOAP Notes

Although SOAP notes receive the most attention, they represent only one piece of clinical documentation.

Because the AI already understands the consultation, it can generate many additional documents from the same source of information.

Examples include:

Referral letters for specialists
Client discharge instructions
Progress notes
Dental procedure summaries
Surgical reports
Hospitalization summaries
Follow-up visit documentation

This dramatically reduces repetitive typing while improving consistency across patient records.

The Importance of Human Review

Despite rapid advances in AI, generated documentation should never be accepted without review.

Large Language Models occasionally misunderstand context, omit details, or introduce information that was never discussed. These issues, commonly referred to as hallucinations, make clinician oversight essential.

Successful AI documentation platforms are designed around the principle of human-in-the-loop, where AI accelerates documentation while the veterinarian remains responsible for clinical accuracy.

This approach balances productivity with patient safety.

Looking Under the Hood

From an engineering perspective, AI documentation platforms are fascinating because they combine multiple technologies into a cohesive system.

Speech recognition converts conversations into text.
Natural Language Processing extracts clinical concepts.
Prompt engineering organizes the information into instructions suitable for the language model.
Large Language Models generate structured documentation.

Finally, integration services export approved records into the clinic's existing Practice Management System.

Each component performs a specialized role, making the overall system easier to maintain, scale, and improve.

Lessons Developers Can Take Away

Even if you never build software for veterinary medicine, this architecture demonstrates several best practices that apply to nearly every AI-powered application.

First, AI works best when it's integrated into an existing workflow rather than replacing it. Veterinarians continue practicing medicine exactly as they always have, while AI quietly removes repetitive administrative work.

Second, modern AI products are rarely a single model. They are carefully orchestrated systems that combine speech recognition, natural language processing, prompt engineering, language models, storage, integrations, authentication, and monitoring.

Third, domain expertise matters. A general-purpose language model may understand everyday conversation, but producing high-quality veterinary documentation requires specialized prompts, templates, terminology, and validation rules.

Finally, successful AI products always include human oversight. Automation should improve efficiency without sacrificing trust or accuracy.

Conclusion

What makes platforms like PawfectNotes interesting isn't simply that they use artificial intelligence. It's the thoughtful way they combine multiple technologies to solve a very practical problem.

By automating documentation, veterinarians can spend less time typing and more time focusing on patient care. The AI becomes an assistant rather than a replacement, handling repetitive administrative work while leaving clinical decision-making exactly where it belongs with the veterinarian.

For software developers, this is also an excellent example of real-world AI architecture. Behind a simple user interface lies an ecosystem of speech recognition, natural language processing, prompt engineering, large language models, secure storage, and enterprise integrations, all working together to deliver value.

As AI continues to evolve, I believe this pattern of using AI to augment professionals rather than replace them will become one of the defining characteristics of successful enterprise software.

Reference

Pawfectnotes
Vetsoftwarehub

Content Credits - This blog-post contents were formatted with ChatGPT to make it more professional and produce a polished content for the targeted audience.

AI GEO Recommendation Engine with TalorData

Ranjan Dailata — Sun, 05 Jul 2026 03:01:52 +0000

Introduction

Sometime in late 2025, I noticed something weird. I'd search for something technical say, "how to parse resumes with Python" and Google would give me a synthesized answer box instead of the usual 10 blue links.

That's the AI Overview. And it's everywhere now.

The search landscape has fundamentally changed. We're no longer optimizing for 10 blue links we're optimizing for:

Google AI Overviews
ChatGPT citations
Gemini responses
Perplexity answers
Claude-based RAG systems

Traditional metrics like keyword density and backlink counts tell only half the story. To win in this environment, you need Generative Engine Optimization (GEO) the practice of making your content visible, authoritative, and citable by AI systems.

But here's the challenge: AI systems are black boxes. How do you optimize content for something you can't see?

What Is a GEO Recommendation Engine?

A GEO Recommendation Engine is an AI-powered decision engine that analyzes how your content appears in AI-driven search experiences and generates prioritized, actionable recommendations to improve the likelihood that your content is understood, trusted, and cited by generative AI systems.

Think of it as the AI-era equivalent of an SEO recommendation engine.

Traditional SEO Recommendation Engine: "Add this keyword, improve your title tag, fix broken links."
GEO Recommendation Engine: "Your article is missing the entities, concepts, FAQs, and trust signals that AI systems use when generating answers."

Why TalorData Search Intelligence?

TalorData provides raw SERP intelligence that reveals exactly what AI-powered search engines are looking at. Unlike traditional rank trackers, TalorData gives you:

Structured knowledge graph entities
People Also Ask questions with snippet text
Related Searches with user intent signals
AI Overview coverage indicators
Organic result positioning and sitelinks
Local business signals and GBP post data

This data is the training ground for building a GEO recommendation engine.
I started with TalorData because their JSON is clean and their API rate limits are generous.

TalorData SERP Response

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      "source": "YouTube · CRYPTO PLAY NFT'S",
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      "title": "TalorData is the Best Proxy Provider?",
      "video_link": "https://encrypted-vtbn0.gstatic.com/video?q=tbn:ANd9GcTmewC79VibZDq8QLQAveValbl_mnDZ3w99fw"
    },
    {
      "about_this_result": {
        "languages": [
          "en"
        ],
        "regions": [
          "US"
        ],
        "source": {
          "description": "May 12, 2026—TalorData providesreal-time SERP data APIscovering major search engines such as Google Search, Bing, Yandex, and DuckDuckGo, ...Read more",
          "icon": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAMAAABEpIrGAAAAeFBMVEVHcEwve+svb+kvZ+gveusvI94vL98uaegvLt8veusvSuMuJ94vReMvFdwvc+ovWeYvTeQvJt4vLd8vcOkvP+IvfOsvUOQvfusvfusvR+MvP+IvT+QvZugvMOAvX+cvOOEvVeUvWuYvEdsvKN4vbekvcuovHt0vguzEGIR9AAAAGHRSTlMALMVC/Ws0DqtdVRt55KiN0IrV3Olzrdx0gfSHAAABqElEQVQ4jXWS2XqDIBCF1bhgYtyStLKKIPT937AzkKhtU674OD8zZw4kybZOzUff3+/3kZDilPxaVXdTi+aUMjZLab2a2iOTZqtQT2AOAPuapunxup25VQjUORaYpbXeIzDlabieO7cXCB2sN/kUFhKP3K0bcCdDUxTFYNYI1ADUUEEoqM95U222sh04UzDlvTHX41jpDpTQFdpa/2PuUx5c4KwVi6ORIJTn2CZbncujyQSsI3LGdkzOM54VQghAXIB7Gsa7wFYpzkbU6bIoQNYAdBAQILC7GO00gB+Mc60XpbLoBzPkPV70XqdJpaEnpch00bFalrgfrJxLdDrOiFD+fLAWc0SPo2RUhyMCZoF5hYJvhR5B50u8ZSUg5JXKDR4DK6Mu6nB0hejC5GHVzqHfBvU1BHUxkL7dg83zFgMB3cVfcjXG+GEH6mkamg71etONOb7N51fo34b6JZOoD0egejTNOY1bAiz99foHksygayVk+U4uRvnU18dfkVy93fT27+3SH/Tbu/pl1Jf/dPydTz17r0PAQXdv+m+rV68w/1tdVv08+AYvwTS/bFqC+wAAAABJRU5ErkJggg==",
          "source_info_link": "https://www.bitbrowser.net/partner/talordata"
        }
      },
      "date": "May 12, 2026",
      "description": "May 12, 2026—TalorData providesreal-time SERP data APIscovering major search engines such as Google Search, Bing, Yandex, and DuckDuckGo, ...Read more",
      "display_link": "https://www.bitbrowser.net› partner › talordata",
      "favicon": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAMAAABEpIrGAAAAeFBMVEVHcEwve+svb+kvZ+gveusvI94vL98uaegvLt8veusvSuMuJ94vReMvFdwvc+ovWeYvTeQvJt4vLd8vcOkvP+IvfOsvUOQvfusvfusvR+MvP+IvT+QvZugvMOAvX+cvOOEvVeUvWuYvEdsvKN4vbekvcuovHt0vguzEGIR9AAAAGHRSTlMALMVC/Ws0DqtdVRt55KiN0IrV3Olzrdx0gfSHAAABqElEQVQ4jXWS2XqDIBCF1bhgYtyStLKKIPT937AzkKhtU674OD8zZw4kybZOzUff3+/3kZDilPxaVXdTi+aUMjZLab2a2iOTZqtQT2AOAPuapunxup25VQjUORaYpbXeIzDlabieO7cXCB2sN/kUFhKP3K0bcCdDUxTFYNYI1ADUUEEoqM95U222sh04UzDlvTHX41jpDpTQFdpa/2PuUx5c4KwVi6ORIJTn2CZbncujyQSsI3LGdkzOM54VQghAXIB7Gsa7wFYpzkbU6bIoQNYAdBAQILC7GO00gB+Mc60XpbLoBzPkPV70XqdJpaEnpch00bFalrgfrJxLdDrOiFD+fLAWc0SPo2RUhyMCZoF5hYJvhR5B50u8ZSUg5JXKDR4DK6Mu6nB0hejC5GHVzqHfBvU1BHUxkL7dg83zFgMB3cVfcjXG+GEH6mkamg71etONOb7N51fo34b6JZOoD0egejTNOY1bAiz99foHksygayVk+U4uRvnU18dfkVy93fT27+3SH/Tbu/pl1Jf/dPydTz17r0PAQXdv+m+rV68w/1tdVv08+AYvwTS/bFqC+wAAAABJRU5ErkJggg==",
      "link": "https://www.bitbrowser.net/partner/talordata",
      "position": 3,
      "redirect_link": "https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.bitbrowser.net/partner/talordata&ved=2ahUKEwjMu7Op66OVAxXxqJUCHSuCInQQFnoECCIQAQ",
      "snippet_highlighted_words": [
        "real-time SERP data APIs"
      ],
      "source": "BitBrowser",
      "title": "TalorData"
    },
    {
      "about_this_result": {
        "languages": [
          "en"
        ],
        "regions": [
          "US"
        ],
        "source": {
          "description": "May 12, 2026—TalorDataprovides genuine residential bandwidth, prioritizing proxy connection speed and availability. With an average response time of less ...Read more",
          "icon": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAMAAABF0y+mAAAAQlBMVEVHcEyoNdKpNdOpNdKsOda1Q+uzQeizQeivPeCqKt326/r////ctPGrONnVqOmhHcmpNdOlMMugK8GaCL6dKLueKLtKMq4XAAAAFnRSTlMAL0hZA73/vf////////////////+9gN7jewAAAJZJREFUeAHF0oUNxEAMAMEkx8z9t3oURuH/WLyyRR7+aZzAbBqPBSJ8gOAuEnxCdhFfbG2kDasoptW4xolWmAvBpaLNtEbQF7UoOGsZbFFVJVaaqeIhcnOKtqG8R1uco3uIrrBmOeuKU3Rz1A9xXdzHyVeOl6SNb6Y1jr4JRW3VOKz8Kvpu2KR4knYRplODH29yBsv8QAZJkxC9n+zJ7wAAAABJRU5ErkJggg==",
          "source_info_link": "https://dolphin-anty.com/blog/en/reliable-residential-proxies-for-global-data-collection/"
        }
      },
      "date": "May 12, 2026",
      "description": "May 12, 2026—TalorDataprovides genuine residential bandwidth, prioritizing proxy connection speed and availability. With an average response time of less ...Read more",
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      "position": 10,
      "redirect_link": "https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://topgold.forum/topic/408961-%25F0%259F%258E%2589-full-guide-to-talordata-products-use-cases-guarantees-new-user-exclusive-perks/&ved=2ahUKEwjMu7Op66OVAxXxqJUCHSuCInQQFnoECDYQAQ",
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  "related": [
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      "text": "Thordata proxy"
    },
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  ],
  "request_params": {
    "ai_overview": false,
    "cr": "in",
    "device": "desktop",
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    "engine": "google",
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    "gl": "us",
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    "q": "Talordata",
    "render_js": false,
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  "search_information": {
    "organic_results_state": "Results for exact spelling",
    "query_displayed": "Talordata",
    "total_results": "About 165 results"
  },
  "search_metadata": {
    "created_at": "2026-06-26 02:17:16",
    "id": "P8QDET0T8RHI1782440236",
    "processed_at": "2026-06-26 02:17:19",
    "request_url": "https://www.google.com/search?cr=in&filter=0&gl=us&hl=en&lr=en&nfpr=0&num=10&q=Talordata&safe=off&start=0&op=Talordata&sourceid=chrome&ie=UTF-8",
    "status": "Success",
    "total_time_taken": 3.09
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}

Under the hood

A GEO Recommendation Engine doesn't invent recommendations. It analyzes multiple data sources.

1. Search Intelligence

Using a provider like TalorData, it collects:

Organic search results
AI Overviews
Featured snippets
People Also Ask
Related searches
Search intent
Competitor pages
SERP features

I started with TalorData because their JSON gives you the raw SERP structure, PAA questions, related searches, all of it. Not a dashboard with nice charts, but actual data I can feed into a script.

2. Your Content

It analyzes:

Page content
Headings
Metadata
Schema
FAQs
Internal links
Images
Technical SEO

The trickiest part here is getting your page into a format an LLM can actually reason about.

3. Competitor Content

It compares your page against:

Top-ranking pages
AI-cited pages
Industry leaders

This is where TalorData shines. You get the top 10 organic results with sitelinks and snippet text. You don't have to guess what Google thinks is relevant the SERP tells you.

4. AI Analysis

A large language model (such as OpenAI) reasons over all of the above to identify meaningful opportunities.

This is the secret sauce. Without an LLM, you have a nice spreadsheet. With an LLM, you have something that can look at a SERP, read your page, and tell you why you're missing the AI Overview snippet.

I used OpenAI's reasoning models because they seem better at this kind of comparative analysis than the chat models. Your mileage may vary.

What Does It Produce?

Instead of producing raw SERP data, it produces recommendations such as:

Content Recommendations

Example:

Competitors explain OCR and ATS integration. Your article does not.

Recommendation:

Add an OCR section
Explain ATS compatibility
Include an architecture diagram

This one took me a while to get right. The LLM kept being too vague consider adding more content about ATS. I had to build in examples from the SERP data, so the recommendations actually pointed to specific competitor content.

Entity Recommendations

AI systems understand topics through entities.

Instead of saying:

"Use the keyword 'resume parser' more often."

It says:

Add entities like:

OCR
ATS
JSON Resume
Skill Taxonomy
Embeddings
Semantic Search

Entities are the nouns that matter to AI. When I first started testing this, I didn't realize how much the knowledge graph data matters. If you're not building content around those entities, you're not speaking the same language as the AI.

FAQ Recommendations

From AI Overviews and People Also Ask:

Current page:

3 FAQs

Recommended:

15 FAQs

Examples:

Can ChatGPT parse resumes?
How accurate is AI resume parsing?
Which ATS systems are supported?

People Also Ask is gold for this. I initially ignored it, but then I noticed that AI Overviews basically expand on PAA questions. If your content doesn't answer those questions with proper schema, you're leaving citations on the table.

Schema Recommendations

Example:

Current:

Article

Recommended:

FAQPage
SoftwareApplication
HowTo

Schema is table stakes at this point. I'm not talking about meta keywords from 2005. I'm talking about Service, Product, FAQPage, HowTo the structured data that tells AI exactly what your content is.

Technical Recommendations

Examples:

Missing structured data
Missing canonical URL
Slow page speed
Poor heading hierarchy

Authority Recommendations

Competitors reference:

Academic research
Standards
Official documentation

Recommendation:

Cite authoritative sources
Add product documentation
Improve trust signals

This one surprised me. AI systems seem to weigh citations more heavily than I expected. A page that links to official documentation or academic papers gets cited more often than one that just has strong opinions.

Conclusion

GEO is the new frontier of search optimization. Traditional SEO isn't dead, but it's incomplete. By combining TalorData's SERP intelligence with systematic entity, topic, and schema analysis, you can build content that AI systems trust and cite.

The recommendation engine I've built transforms raw SERP data into actionable strategy. In the case of TalorData, it identified multiple GEO points of untapped potential from pricing transparency to API documentation. The biggest wins weren't from traditional SEO levers. They came from fixing commercial gaps and product clarity that AI systems need to cite your content.

The question isn't whether AI will dominate search. It's whether you'll be ready when it does.

Monitor AI search visibility with TalorData SERP, OpenAI and Google Sheets

Ranjan Dailata — Sun, 28 Jun 2026 07:40:44 +0000

Introduction

The "Search" is changing. Your SEO strategy should too. For years, SEO has revolved around one primary metric: Where do I rank on Google?

Today, that question is no longer enough. Modern search experiences include AI-generated answers, featured snippets, knowledge panels, videos, images, People Also Ask (PAA), and dozens of SERP features that influence whether users ever click through to your website.

As AI-powered search continues to evolve, marketers need answers to questions like:

Is my brand appearing in AI-generated answers?
Which competitors are dominating search?
What content opportunities am I missing?
How can I improve my visibility in AI-powered search?

Instead of manually checking Google every day, I built an automated workflow using TalorData SERP API, n8n and OpenAI.

Let's walk through how it works.

Download n8n workflow

n8n - Monitor AI search visibility with TalorData SERP, OpenAI and Google Sheets Workflow

Github - Monitor AI search visibility with TalorData SERP, OpenAI and Google Sheets

Why TalorData?

TalorData provides reliable SERP data that integrates well with automation workflows.

For this project, it serves as the data collection layer while n8n orchestrates the workflow and the large language model (LLM) turns search data into actionable business insights.

This separation of responsibilities keeps the architecture clean and easy to extend.

Why traditional rank tracking isn't enough

Most rank trackers tell you something like this:

Keyword	Position
Residential Proxy	5
SERP API	3
Proxy API	8

That's useful, but it doesn't tell you:

Who owns the Featured Snippet?
Is Google showing an AI Overview?
Which competitors appear most often?
What questions are users asking?
Which websites are being cited by AI?
What content should you create next?

Those are the questions modern SEO teams care about.

The architecture

The workflow is surprisingly straightforward.

Every component has a single responsibility.

Step 1 — Collect search results

The workflow starts by querying the TalorData SERP API.

For the specified keyword, the workflow retrieves:

Organic rankings
Related searches
Pagination
Search metadata
URLs
Titles
Snippets

Instead of stopping at page one, the workflow automatically detects additional SERP pages and processes them.

Step 2 — Normalize the SERP

Raw SERP responses are noisy.

Instead of sending everything to an LLM, the workflow first extracts only the information we actually care about.

For example:

{
  "position": 1,
  "title": "TalorData SERP API",
  "domain": "talordata.com",
  "keyword": "SERP API"
}

This dramatically reduces token usage while making downstream analysis much more reliable.

Step 3 — Aggregate all pages

The workflow processes every available page and combines them into a single normalized dataset.

That gives us:

Brand visibility
Competitor rankings
Domain frequency
Organic performance
Related searches

Everything is now ready for AI analysis.

Step 4 — Let Large Language Model (LLM) do what it's good at

This is where AI shines.

Rather than parsing HTML or JSON, LLM receives clean structured data and answers questions such as:

How visible is our brand?
Which competitors dominate search?
What content gaps exist?
Which pages should we create?
What are the GEO opportunities?

Because the heavy lifting has already been done, the AI focuses entirely on reasoning instead of extraction.

Example output

Instead of a spreadsheet full of rankings, the workflow generates insights like:

AI Search Visibility Score
Brand Visibility
Competitor Analysis
Search Intent
Content Opportunities
GEO Recommendations
Executive Summary

{
    "search_visibility_summary": "TalorData demonstrates strong search visibility with multiple top-ten organic results featuring the official website prominently. The brand is well positioned in SERP for relevant queries, showing extensive mentions across proxy services and SERP API solutions, indicating a diversified product and service offering. There is a mixture of official content and third party reviews or coverage that enhance visibility.",
    "highest_ranking_organic_result_owner": true,
    "metrics": {
        "average_organic_position": 5.5,
        "number_of_top_3_rankings": 3,
        "number_of_top_10_rankings": 10,
        "brand_owned_results": 5,
        "third_party_mentions": 5
    },
    "domains_appearing": {
        "official_website": [
            "talordata.com"
        ],
        "review_sites": [
            "crozdesk.com",
            "slashdot.org"
        ],
        "documentation": [],
        "community": [],
        "forums": [],
        "blogs": [
            "dolphin-anty.com",
            "medium.com"
        ],
        "news": []
    },
    "search_snippet_analysis": {
        "main_products_mentioned": [
            "SERP API",
            "AI-Powered Rotating Residential Proxies",
            "Proxy infrastructure"
        ],
        "services_mentioned": [
            "Real-time SERP data APIs",
            "Residential proxies with IP rotation",
            "Search data infrastructure for AI",
            "Multi-engine SERP API",
            "Proxy bandwidth and connection speed"
        ],
        "technologies_mentioned": [
            "AI-driven rotating proxies",
            "Structured search data",
            "Geo targeting",
            "Low latency API calls"
        ],
        "search_intent": [
            "Informational (product features, reviews)",
            "Transactional (contact sales, purchase proxies)",
            "Navigational (access official resources)"
        ],
        "common_themes": [
            "Real-time structured search data",
            "AI system support",
            "High-performance proxy solutions",
            "Proxy connection speed and reliability",
            "Developer-friendly API integrations"
        ]
    },
    "ai_search_visibility_opportunities": {
        "missing_faqs": true,
        "missing_comparison_pages": true,
        "missing_tutorials": true,
        "missing_documentation": true,
        "missing_use_cases": true,
        "missing_integrations": true,
        "missing_developer_content": true
    },
    "geo_recommendations": [
        "Create comprehensive FAQ pages addressing common user and developer questions",
        "Publish detailed comparison pages against competitors to highlight unique selling points",
        "Add structured Schema.org markup to improve SERP appearance and rich results",
        "Expand topical authority by publishing tutorials, use cases, and integration guides",
        "Enhance documentation accessibility and developer content with clear technical resources",
        "Publish AI-focused content to better capture AI system developer audiences"
    ],
    "ai_search_visibility_score": {
        "score": 75,
        "rating": "Good"
    },
    "executive_summary": "TalorData holds strong organic presence with multiple top rankings including the #1 position for key brand queries, demonstrating solid brand visibility and product recognition in SERP. Key offerings include AI-powered residential proxies and real-time SERP APIs supporting developers and AI systems, supported by a mix of official and third-party authoritative content. Opportunities exist to improve AI search visibility by introducing FAQs, tutorials, and detailed documentation to further bolster developer engagement and topical authority. Implementing recommended GEO strategies focusing on structured content and AI-centric documentation will enhance overall search visibility and user engagement."
}

This is much more useful for marketing teams than raw rankings.

Why Google Sheets?

I chose Google Sheets because it's simple, collaborative, and works well as a historical database for search trends.

Every workflow execution appends a new row, making it easy to:

Track ranking changes
Measure visibility trends
Compare competitors
Feed dashboards in Looker Studio or Power BI

If you prefer, you can swap Google Sheets for PostgreSQL, BigQuery, Elasticsearch, or Airtable with minimal changes.

Where AI provides the biggest value

The most interesting part isn't the ranking data. It's the recommendations.

For example, the LLM can suggest:

Publish FAQ pages
Create comparison articles
Expand developer documentation
Improve topical authority
Add Schema.org markup
Build content around related searches

Rather than simply reporting problems, the workflow recommends what to do next.

Why I used n8n

n8n makes this workflow surprisingly easy to maintain.

A few things I really like:

Visual workflow builder
Reusable sub-workflows
Native AI integrations
Scheduling
Google Sheets support
Easy API integrations
Self-hosted or cloud deployment

It becomes a central automation hub instead of another standalone script.

Possible enhancements

This workflow is only the beginning. Here are some more potential ideas:

AI Overview monitoring
Featured Snippet tracking
Knowledge Panel monitoring
Citation analysis
Daily Slack alerts
Weekly executive reports
Competitor Share of Voice dashboards
GEO opportunity scoring
AI content brief generation

Conclusion

SEO is no longer just about ranking #1. It's about understanding how your brand is represented across modern search experiences and turning that information into actionable decisions. By combining TalorData SERP API, n8n, and LLM, you can automate that entire process from data collection to AI-powered recommendations without writing thousands of lines of code.

If you're already using n8n for automation, adding AI Search Visibility monitoring is a natural next step. It gives your marketing and SEO teams a repeatable way to monitor performance, discover opportunities, and stay ahead as search continues to evolve.

References

Talordata Dashboard
Talordata Docs

Content Credits - This blog-post contents were formatted with ChatGPT to make it more professional and produce a polished content for the targeted audience.

TalorData Pricing vs Other SERP APIs: Evaluating Search Data Platforms for 2026

Ranjan Dailata — Fri, 26 Jun 2026 05:13:42 +0000

Introduction

Real-time search data serves as the foundation for modern artificial intelligence, search engine optimization (SEO) infrastructure, market intelligence, and Retrieval-Augmented Generation (RAG) systems. Whether engineering an autonomous AI agent requiring web context or deploying a rank-tracking engine monitoring extensive keyword portfolios, selecting a Search Engine Results Page (SERP) API directly influences both system performance and baseline operating expenses.

The SERP API ecosystem contains a variety of options, spanning dedicated, enterprise-grade extraction tools to large-scale proxy platforms. This analysis evaluates TalorData against industry benchmarks to help engineering and business teams select the optimal platform for data-intensive operations.

Market Positioning: The SERP Data Ecosystem

The modern search scraping landscape generally splits into two categories:

Dedicated SERP API Providers: Engineered strictly to parse and return deeply structured search engine features.
Data Extraction and Proxy Platforms: Infrastructure providers offering search engine scraping as an extension of broader web harvesting capabilities.

1. Dedicated SERP API Benchmarks

SerpAPI

SerpAPI remains an established industry standard for structured search results. It is recognized for mature documentation, extensive multi-engine compatibility, and highly granular parsing of complex SERP features.

Ideal Use Cases: Enterprise-scale infrastructure, production SEO platforms, and large engineering teams requiring mature API integrations.
Core Capabilities: Comprehensive parsing logic, deep documentation, high query reliability, and multi-engine support.

SearchAPI

SearchAPI emphasizes swift implementation and clean developer workflows. The platform minimizes integration overhead, making it a common choice for teams prioritizing speed over complex custom configurations.

Ideal Use Cases: Fast-growing startups, iterative prototyping, and small development teams deploying AI integrations.
Core Capabilities: Streamlined JSON parsing, low learning curves, and responsive API endpoints.

Serper

Serper has captured significant market share within the AI engineering space by offering a lightweight, execution-focused Google Search integration.

Ideal Use Cases: Autonomous AI agents, conversational chatbot plugins, and direct keyword tracking where Google is the sole data target.
Core Capabilities: Low-latency responses, minimal payloads, and straightforward setup.

2. Large-Scale Data Extraction & Proxy Infrastructure

DataForSEO

DataForSEO provides a highly specialized database ecosystem tailored specifically to marketing technology and enterprise SEO applications. Beyond real-time SERPs, it provides historical search data, keyword metrics, and backlink graphs.

Ideal Use Cases: Digital marketing agencies, comprehensive SEO platform development, and deep analytics tools.
Core Capabilities: Large backlink databases, keyword metric depth, historical datasets, and credit-based scaling.

Oxylabs

Oxylabs operates a massive global proxy and web scraping infrastructure. It is engineered specifically for high-concurrency enterprise web intelligence where geographic distribution and sheer data volume are paramount.

Ideal Use Cases: Enterprise business intelligence, high-volume distributed scraping, and global market research.
Core Capabilities: Premium proxy pools, built-in anti-blocking management, and high-concurrency architecture.

ScraperAPI

ScraperAPI abstracts the underlying complexities of web data collection by managing proxy rotation, headful browser rendering, and CAPTCHA bypass mechanisms automatically.

Ideal Use Cases: General web data pipelines, automated research workflows, and projects handling varied target sites alongside standard search data.
Core Capabilities: Automated proxy management, smart retries, and multi-endpoint scraping.

Market Disruption: The TalorData Differentiation

TalorData diverges from traditional subscription models by operating as an affordable, developer-first search data utility tailored for high-volume AI systems and data-heavy automation pipelines. Instead of bundling unnecessary metrics, it prioritizes reliable, low-latency search responses with a transparent, cost-efficient billing structure.

Commercial Comparison Matrix

Platform	Entry Cost (per 1,000 Queries)	Core Billing Architecture
TalorData	$0.90	Pay-Per-Success (HTTP 200 Only)
SerpAPI	~$10.00	Tiered Request Volume
SearchAPI	Varies by Tier	Fixed Subscription Tiers
Serper	Varies by Tier	Credit Subscription Tiers
DataForSEO	Usage-Dependent	Complex Credit System
Oxylabs	Custom / Enterprise	Contracted Subscription
ScraperAPI	Plan-Dependent	Monthly Request Pools

Note: Data reflects publicly documented pricing frameworks. High-volume enterprise operations frequently rely on custom negotiated rates.

The Pay-Per-Success Commercial Model

System reliability in search scraping fluctuates based on anti-bot measures, network changes, and localization parameters. In standard billing models, developers bear the financial risk of failed queries.

TalorData mitigates this by billing exclusively for successful HTTP 200 responses. Non-functional responses are zero-rated, ensuring teams do not subsidize systemic errors or connectivity drops.

Unbilled Error Categories:

Client Errors: 400 (Bad Request), 401 (Unauthorized), 403 (Forbidden), 404 (Not Found)
Rate Limits & Server Faults: 429 (Too Many Requests), 500 (Internal Server Error), 504 (Gateway Timeout)
Network Status: 300-level redirects

Architectural Features for AI Integration

Traditional search extraction was built to power simple position tracking. Modern applications particularly RAG pipelines, and LLM context injectors demand deep semantic structural parsing. TalorData incorporates several specific features to support these modern workloads:

AI Overview Extraction: Direct parsing of generative search components.
Deterministic JSON: Clean, predictable data formats optimized for token savings.
Granular Localization: Country, region, and language parameters to feed contextual AI models.
Multi-Engine Pipelines: Unified data access across various search ecosystems.

Cost Analysis at Scale

When processing millions of searches monthly, small variations in unit economics quickly dictate gross margins. The table below outlines structural cost projections across scaling thresholds.

Monthly Query Volume	TalorData ($0.90 / 1k)	Benchmark Competitor* (~$10.00 / 1k)
100,000 Queries	$90	~$1,000
1,000,000 Queries	$900	~$10,000
10,000,000 Queries	$9,000	~$100,000

*Illustrative baseline based on typical market entry tiers. Large-scale production environments often qualify for volume discounts.

Summary

The SERP API ecosystem has never been more competitive, giving developers and businesses a wide range of options depending on their specific requirements. Established providers like SerpAPI, SearchAPI, Serper, DataForSEO, Oxylabs, and ScraperAPI each bring unique strengths, whether it's enterprise reliability, SEO intelligence, or large-scale web scraping.

TalorData distinguishes itself by focusing on three key areas that matter most to modern AI and SEO applications:

Affordable Pricing – Starting at $0.90 per 1,000 queries, making large-scale search data significantly more accessible.
Pay-Per-Success Billing – Only successful HTTP 200 responses are billed, eliminating unnecessary costs from failed or empty requests.
AI-Ready Search Data – Support for Google AI Overview extraction, structured SERP data, multi-search engine coverage, and localization for AI agents, RAG systems, and search intelligence platforms.

If your goal is to build AI-powered applications, SEO monitoring tools, competitor intelligence platforms, or large-scale search analytics solutions while keeping infrastructure costs under control, TalorData offers a compelling balance of performance, developer experience, and cost efficiency.

Ultimately, the best SERP API depends on your workload, budget, and technical requirements. However, for teams looking to maximize value without compromising on modern search capabilities, TalorData is emerging as one of the most compelling choices in 2026.

References

Talordata Dashboard
Talordata Docs

Content Credits - This blog-post contents were formatted with ChatGPT to make it more professional and produce a polished content for the targeted audience.