DEV Community: Vozzo AI

Why 97% of Contact-Center Calls Never Get Reviewed — and What It Actually Takes to Fix That

Vozzo AI — Mon, 07 Sep 2026 07:25:33 +0000

Most contact centers running compliance-heavy call floors — collections, lending, insurance, healthcare — have the same quiet problem: they're recording every call, but reviewing almost none of them. Manual QA sampling typically covers 2–5% of total call volume. That means the overwhelming majority of what happens on a call — a mis-stated interest rate, a skipped disclosure, a coercive line from a stressed agent — is only discovered after it becomes a complaint, a regulator escalation, or lost revenue, not before.

I wanted to understand what it actually takes to close that gap technically — not "review more calls with more people," but build a pipeline that can score 100% of call volume automatically. Here's the architecture, the specific problems that make this hard, and where an off-the-shelf platform (I used Vozzo Call Intelligence as the reference implementation) saves you from building all of it yourself.

The core problem, in numbers

A QA team of ten people can realistically listen to a few hundred calls a month. A mid-sized BFSI or BPO floor generates that many calls in hours. Two consequences follow directly from that math:

Sampling can only tell you a problem exists somewhere** — not how often, on which agents, or on which products. It's not a defensible audit position anymore, especially for regulated call floors where auditors expect evidence across the population of calls, not a hand-picked subset.
Call volumes have grown faster than QA headcount ever will.** Outbound dialers, omnichannel campaigns, and multilingual floors have multiplied recorded minutes several times over — and hiring reviewers in proportion isn't affordable, and isn't even consistent (two human auditors rarely score the same call identically).

So the real engineering question is: what would it take to score every call, automatically, against a compliance rubric — accurately enough that a supervisor can trust the output and act on it same-day instead of weeks later?

The pipeline, stage by stage

Ingestion

The first requirement is boring but non-negotiable: pull recordings from wherever they already live — your dialer, your CRM, or storage — rather than requiring agents to change their workflow. A pipeline that needs a new recording step bolted on top of an existing dialer setup will never get 100% coverage; it'll just create a second sampling problem.

Transcription and translation

Call floors in India in particular are rarely monolingual — a single collections queue can run in Hindi, English, and code-mixed Hinglish inside the same shift. Transcription has to handle that mix accurately, and translation has to happen without losing the specific phrasing that compliance scoring depends on (more on this below).

Scoring against your own rubric

This is where "AI reads the transcript" stops being enough on its own — you need scoring against a checklist that maps to your compliance requirements, not a generic sentiment score. For a BFSI collections call, that rubric typically checks things like:

Mandatory disclosure read
Customer identity verified
No coercive language used
Repayment terms stated correctly
Grievance redressal channel shared

A real scorecard output looks something like this — QA score, sentiment, and a compliance flag count, with each individual rubric item scored independently:

CALL ID VZ-84120 · COLLECTIONS
Agent scorecard — Hindi / English

QA SCORE: 84%      SENTIMENT: Neutral      COMPLIANCE: 1 flag

Mandatory disclosure read ........... 100
Customer identity verified .......... 100
No coercive language ................. 72
Repayment terms stated correctly ..... 90
Grievance channel shared .............. 0

AI note (04:12): Agent skipped the grievance redressal
disclosure and used pressure phrasing ("we will have to
escalate today itself"). PAN digits auto-redacted from transcript.

Notice the last line does two things at once: it flags the specific failure with a timestamp (not a vague monthly average), and it redacts sensitive data before a human ever sees the transcript. Both of those are architectural decisions, not afterthoughts — you have to design for them from the start, not bolt them on later.

Redaction before human review

If you're building this yourself, this is the stage that's easy to underestimate. Any transcript a supervisor reviews needs PAN numbers, account digits, and other PII automatically masked before it reaches a human reviewer — not scrubbed after the fact. That's a hard requirement if you're operating under DPDP or RBI-aligned data handling, not a nice-to-have.

Routing and coaching

Flags need to route to the right supervisor queue within minutes of the call ending, not surface in a weekly report. The reason this matters technically: coaching only changes agent behavior when the feedback points to a specific timestamp and phrase, close to when the call happened — not a monthly aggregate score that arrives too late to correct anything.

Why "just call an LLM on the transcript" isn't the whole solution

If you're picturing this as "transcribe, then ask an LLM to score it," the gap shows up fast in two places:

Consistency. A generic prompt scoring against an ad-hoc rubric will drift between calls the same way human reviewers do — which defeats the entire point of automating this in the first place. You need the rubric encoded as a structured, versioned checklist the model scores against consistently, not a paragraph of vibes.

Auditability. Regulated call floors need every score, override, and export to leave a traceable record — who reviewed what, when, and what changed. That's an audit-log and data-governance problem sitting on top of the ML problem, and it's usually the part that takes longer to build than the scoring model itself.

Calling this programmatically

If you're integrating call scoring into your own stack — a supervisor dashboard, a CRM, an internal audit tool — the useful entry point is a post-call analytics API that returns the structured scorecard rather than raw transcript text you have to parse yourself:

curl -X GET "https://api.vozzo.ai/v1/calls/{call_id}/scorecard" \
  -H "Authorization: Bearer $VOZZO_API_KEY"

const res = await fetch(`https://api.vozzo.ai/v1/calls/${callId}/scorecard`, {
  headers: { Authorization: `Bearer ${process.env.VOZZO_API_KEY}` }
});
const { qa_score, sentiment, compliance_flags, rubric_breakdown } = await res.json();

(Check your account's API docs for the exact schema — this illustrates the shape, not a guaranteed contract.) The point of exposing this as structured JSON rather than a raw transcript dump is that it plugs directly into a CRM record, a supervisor dashboard, or a BI pipeline without you having to build a parsing layer on top of free text.

What you'd need to build this from scratch — and what you wouldn't

If you're weighing "build vs. buy" on this: the transcription and translation layer is table stakes — plenty of providers handle that well. The harder, more specific engineering is everything downstream of it — a configurable rubric engine, PII redaction before human review, an audit trail on every score/override, and integrations into whatever dialer/CRM/core-banking stack your floor already runs on (Salesforce, Zoho, core banking systems, payment gateways).

That's the part that took the most design thought when I looked at how Vozzo's Call Intelligence product handles it — full 100% call coverage instead of a sampled subset, ISO 27001 and SOC 2 Type II certified, DPDP-aligned, RBI-ready practices baked into the redaction and access-control layer rather than added on top. If you're evaluating whether to build this pipeline in-house or adopt something purpose-built for regulated call floors, their sample scorecard is a reasonable way to see what "100% coverage" actually looks like as output before deciding which way to go.

The takeaway

The gap between 2–5% manual sampling and 100% automated coverage isn't a data-availability problem — every one of these calls is already being recorded. It's an engineering problem: consistent rubric-based scoring, redaction before review, routing that's fast enough to matter, and an audit trail regulators will actually accept. Whether you build that stack yourself or adopt one, that's the bar worth designing to.

How to Build an AI Voice Agent in Under 15 Minutes (No-Code, Step-by-Step)

Vozzo AI — Mon, 10 Aug 2026 04:44:44 +0000

How to Build an AI Voice Agent in Under 15 Minutes (No-Code, Step-by-Step)

Building a voice AI agent from scratch usually means stitching together a speech-to-text engine, an LLM, a text-to-speech engine, a prompt that survives real conversations, and some way to feed it your own data so it doesn't hallucinate. That's a multi-week project before you've even had a real test call.

I wanted to see how fast that whole pipeline could be replaced with a UI-driven workflow, so I built a working voice agent end-to-end on Vozzo AI Labs — no infrastructure, no glue code, just configuration. Here's the exact process, step by step, plus where to plug in code if you want to go past the dashboard.

This works whether you're building a customer support bot, an enrollment assistant, an order-tracking line, or something else entirely — the pipeline is the same regardless of industry. I'll point out where your choices will differ depending on your specific use case.

What you'll need

A Vozzo AI Labs account (sign up here)

Some reference material for your agent — a website URL, an FAQ doc, or a spreadsheet

~15 minutes

Step 1: Sign in

Go to platform.vozzo.ai and sign in with email/password or Google SSO. You land on the main dashboard — the left sidebar has everything you'll need: Create New, My Agents, Agent Templates, Actions, Telephony, Voices, and further down, API Docs (useful once you're past manual testing and want to trigger calls or pull data programmatically).

Step 2: Start from a template, not a blank page

This is the step that saves the most time, regardless of what you're building. Go to Agent Templates. Vozzo ships with pre-built templates spanning multiple industries — Edtech (Academic Advising, AI Tutoring & Support, Campus Information, Student Enrollment Support), Ecommerce (Order Tracking, Service Professional Tracking), Government (Candidate Screening, Grievance Help Desk), and more, with a search bar and an industry filter to narrow things down.

Each card gives you a one-line summary of the agent's persona and purpose. Whatever your use case, look for the closest match first — even an 80%-fit template is faster to edit than a prompt written from a blank page, because the hardest parts of a voice-agent prompt (pacing, tone, edge-case handling) are already worked out.

If nothing matches, Create New starts you from scratch — the configuration steps below are identical either way.

Step 3: Configure the model stack (Persona tab)

Click Create Agent and you land on the Persona tab, where you set the actual pipeline behind the agent. Two architecture options:

Orbit — speech-to-speech only, a single unified model handling the whole pipeline

Quantum — separate STT → LLM → TTS stages, each independently configurable

Quantum is the better default if you want control over each stage — different providers genuinely perform differently depending on your language, accent, and domain vocabulary. A typical config:

Stage Provider Model Speech-to-Text Sarvam AI Saaras:v3 LLM OpenAI GPT-4.1-mini Text-to-Speech Google Gemini-2.5-flash-preview-tts

Each stage has Advanced Settings and a Fallback Configuration. Set a fallback on at least the LLM stage — voice agents fail loudly (dead air, dropped calls) when a provider hiccups mid-call, and a fallback swaps providers quietly instead.

Step 4: Write the conversation logic (Prompts tab)

This tab decides your agent's personality and reliability more than anything else:

Agent Greetings — the literal first line spoken.

Agent Greets First — whether the agent opens the call or waits.

Agent Prompt — the full system prompt.

A few rules worth adding to any voice-agent prompt, regardless of industry:

Language and tone — specify explicitly (formal vs. casual, single language vs. code-mixed) rather than leaving it to the model's default.

Number handling — voice agents mis-speak numbers constantly. Force digit-wise reading for phone numbers, IDs, order numbers, and codes, or your TTS will read a 6-digit ID as one large number.

Punctuation handling — an easy miss that breaks agents in production: TTS engines will sometimes literally say "hyphen" or "dash" out loud if a prompt or knowledge source contains one. Add a rule to treat punctuation as silent spacing, never verbalized.

Pacing — for anything procedural (steps, instructions, options), tell the model to chunk information rather than dumping a paragraph — a caller can't re-read the way a chat user can.

There's an Edit with AI button next to the prompt box to revise it via plain-English instructions, and Prompt History to roll back a change that made things worse.

Step 5: Give it real knowledge (Wisdom tab)

A strong prompt with no real data will just make things up when asked something specific. The Wisdom tab fixes that:

Website URLs to reference — paste your site/docs URL and hit Add; the agent pulls from it live.

Custom Knowledge — free text for anything not already published on a page.

Upload File — for structured detail (catalogs, pricing sheets, policy documents, FAQs), upload directly rather than pasting as text.

General rule regardless of use case: URLs and free text work well for broad context, but anything tabular or fact-heavy (prices, deadlines, codes) is far more reliable as an uploaded file than as pasted text.

Step 6: Save, test, iterate

Hit Save & Next, then start the agent and talk to it directly. This is the step to spend real time on: ask off-scope questions, say numbers the way a real caller would, interrupt it mid-sentence. Every failure here is a one-line prompt fix now instead of a bad call in production.

What I'd do differently next time

Start from a template no matter what you're building — editing beats writing from scratch almost every time.

Write the punctuation/number-handling rules into the prompt on day one, not after a test call embarrasses the agent.

Upload structured data as a file, not pasted text — the model handles tabular facts more reliably that way.

Set an LLM fallback before calling it "done" — a two-minute setting that prevents dead air during a real call.

Wrapping up

Going from an empty dashboard to a working, testable voice agent took about 15 minutes, and none of that time was spent on infrastructure. Whatever the use case, the pipeline is the same: pick a starting template, configure the model stack, write the prompt with voice-specific rules, feed it real knowledge, and test it like a real caller would. Start from the closest match in Agent Templates, and once you're ready to go beyond manual testing, the API docs cover triggering calls and pulling data programmatically.