← Back to the fleet FTE-06 · AI Conversation & Quality Analyst

Vocalytic

Understand every conversation — in any language, at any scale. Vocalytic listens to a call, a meeting or a brainstorm in mixed Urdu + English, works out who said what, and turns it into a clean transcript, a translation, minutes and a build-ready spec — the always-on ear a quality team and a scribe can't be.

MVP · real-audio verified Speech + Voice ID faster-whisper + pyannote AI-native 4/5 FTE-fit 4/5

Languages

UR+ENCode-switch aware

Transcribe

~14s42s clip · RTX 4050

Privacy

LocalAudio never leaves the box

Pipeline

6Stages, end-to-end

Why it matters

Your biggest dataset is the one nobody logs.

🎧

2% gets heard

A QA team spot-checks a handful of calls a week. The other 98% — every angry customer, every missed compliance line — is never reviewed.

📝

Minutes go missing

Decisions and action items live in someone's memory. By next week nobody agrees on what was actually agreed.

🌐

Language barrier

Real conversations are code-switched — Urdu and English in one breath. Off-the-shelf tools garble exactly the moments that matter.

💡

Ideas evaporate

The best product idea of the quarter was said out loud in a brainstorm — and lost the moment the call ended.

See it working today · the session workspace

Session · "Water storage management" 2 speakers · diarized

TranscriptTranslationSummaryMinutesLab verdict

ShehzadMATCHED · 0.7100:04

So the core idea is a smart tank controller — pani ka level automatically monitor kare, aur logging bhi ho.

HamzaMATCHED · 0.6800:12

ہاں، اور جب لیول لو ہو تو پمپ خود on کر دے، اور WhatsApp پر الرٹ بھیجے۔

ShehzadMATCHED · 0.7100:20

Exactly. And we predict shortages before they happen — usage history se.

UnknownBELOW THRESHOLD00:27

Never guesses a speaker — an unmatched voiceprint stays Unknown.

Auto-summary · what was decided

A smart water-tank controller that monitors level, auto-runs the pump on a low threshold, sends a WhatsApp alert, and logs usage to forecast shortages.

Build-ready spec · extracted

Problem · manual tank checks, dry-runs.
MVP · level sensor + pump relay + alert.
Later · usage forecast, multi-tank.
Open Q · sensor hardware?

This is the shipped pipeline, run on a real recorded conversation. Voiceprints are enrolled per speaker, audio is transcribed and diarized on a local GPU, code-switched English is restored to Latin script while Urdu stays Urdu, and the LLM chain translates, summarizes, polishes and evaluates — every artifact kept side by side for traceability.

The pipeline · audio in, understanding out

Six stages, one conversation.

Speech and voice recognition run locally on the GPU — audio never leaves the machine; only the transcript text goes to the provider-agnostic LLM layer.

Enroll

Voiceprint per speaker (pyannote embedding)

live

Transcribe

faster-whisper large-v3 + diarization → Name: line

live

Translate

Urdu → English, speaker order preserved

live

Summarize

Faithful recap — no invented conclusions

live

Polish

Reshape into a build-ready brief

live

Evaluate

8-section Lab verdict + AI/FTE scores

live

What runs today vs. the roadmap

Running today

Read and confirmed in source · verified end-to-end on a real recording.

✦Speaker enrollment + diarized transcription — pyannote voiceprints match each turn to a name; faster-whisper large-v3 transcribes mixed Urdu + English in ~14s on a laptop GPU. Uncertain voices stay Unknown.
✦Code-switch restore — English words Whisper wrote in Urdu script are put back into Latin; Urdu stays Urdu. Text-only, graceful with no LLM.
✦Translate → Summarize → Polish → Evaluate — a provider-agnostic LLM chain (OpenAI / Anthropic, set via .env) turns the transcript into an English version, a faithful summary, a build-ready brief and an 8-section Lab verdict.
✦Audio-local / text-cloud privacy split — STT and voice ID run on the GPU; only transcript text ever hits the cloud. Any upload (webm / opus / m4a) is ffmpeg-normalized to 16 kHz mono.
✦Next.js workspace + live recording — enroll, upload or record in-browser (MediaRecorder), watch the diarized transcript, RTL-Urdu bubbles, artifact tabs and score gauges fill in. Stage is derived from the artifacts that exist, so it never desyncs.

On the roadmap

The analysis & observability layer — not shipped, never demoed as live.

◆Call-center QA — score every agent against a rubric (communication, empathy, resolution, compliance), gauge customer sentiment and escalation risk, flag missed disclosures.
◆Automatic minutes & action items — decisions, owners and follow-ups extracted from any meeting and pushed to the tools you already use.
◆Observability dashboard — 100% coverage: CSAT, first-call-resolution, handle time, talk/listen ratio, topic and sentiment trends, a QA leaderboard across the whole team.
◆Autonomous & real-time — a silence/keyword "finished" trigger for hands-free runs, live streaming captions, auto-follow-ups, alerts, and CRM / helpdesk / calendar integrations.

Honesty note: today Vocalytic transcribes, translates and summarizes — genuinely, on real audio. The scoring, sentiment, CSAT and dashboards below are the vision layer: treat every number in them as a synthetic target, not a shipped metric.

The roadmap · the analyst crew

Four AI FTEs, on every conversation.

Each agent owns a slice of the work a quality team and a scribe do by hand — labeled roadmap, human-in-command before anything is scored or sent.

🖋

Scribe FTE

CAPTURE · MINUTE

Transcribes and diarizes every call and meeting, then drafts the minutes — decisions, action items and owners — before anyone leaves the room.

🎯

Auditor FTE

SCORE · COACH

Rates every agent call against your rubric, spots the coachable moment, and writes the feedback tip — quality review at 100% coverage, not 2%.

💓

Pulse FTE

SENSE · FLAG

Gauges customer sentiment and escalation risk turn by turn, and raises a real-time flag on anger, churn cues or a compliance breach.

⚙

Forge FTE

IDEATE · SPEC

Turns a spoken brainstorm into a build-ready spec — problem, MVP, nice-to-haves, open questions — ready to drop into the Lab.

Roadmap preview · the real-time workspace & observability

vocalytic://live-session — real-time experiencedesign preview · roadmap

Vocalytic real-time workspace design preview — a live meeting transcript with four detected speakers in mixed languages, tabs for transcript / translation / summary / insights / minutes, and a live AI-insights panel showing top topics, sentiment and action items.

Team dashboard synthetic preview

Calls analyzed

1,248

100% coverage

Avg CSAT

4.6/5

▲ 5.1%

First-call res.

68%

▲ 7.3%

Billing issues35%

Refunds28%

Product info18%

Tech support12%

Agent scorecard synthetic preview

4.3

Agent · sample

142 calls · this month

Communication4.6

Empathy4.2

Resolution4.4

Compliance4.1

Hold time4.0

⚠ Roadmap preview — every figure here is synthetic sample data, not a shipped Vocalytic metric.

Where it goes · the magic

Not a tool — an AI co-pilot for every conversation.

🛡

Autonomous QA

Reviews 100% of interactions and flags risk & opportunity.

🎓

Smart Coaching

Agents get personalized feedback and tips, per call.

🔔

Real-time Alerts

Compliance breaches, anger and escalations, instantly.

📣

Voice of Customer

Surface trends, pain points and product feedback at scale.

🔀

Process Mining

Find the bottlenecks and optimize the flow of a call.

📈

FTE Productivity

Measure utilization, quality, occupancy and efficiency.

⚡

Speed of Service

Track response time, resolution time and SLA performance.

🔒

Risk & Compliance

Detect PII, sensitive data and policy violations.

🔄

Auto Follow-ups

Draft the emails, case notes and next tasks automatically.

🌍

Global Ready

Multi-language, multi-accent, multi-channel by design.

The vision · one platform

Every conversation type, every language.

vocalytic://platform — understand every conversation, in any language, at any scale

One platform across group discussions, call-centre calls, meetings, webinars and support chats. The shipped slice is the transcribe → translate → summarize → spec spine; the scoring, sentiment and dashboards are the roadmap.

The role it replaces

A QA analyst — and the meeting scribe.

The analyst who can only sample a fraction of calls, and the note-taker who misses half the room. Vocalytic hears every second of every conversation, in the language it was actually spoken, and never files it late — human-in-command before anything is scored or sent.

100%of calls heard · vs a ~2% human sample

The business case

Conversations are the largest un-instrumented dataset a company owns. Every call, meeting and brainstorm carries the truth about your customers, your agents and your next product — and almost none of it is logged, scored or searchable. Vocalytic makes it observable: it starts today as a working voice→spec pipeline that already handles the hard part — mixed-language audio, on-device, with speakers named — and grows into the analyst that reviews every conversation for quality, sentiment, compliance and speed of service.