# FitNova Call Intelligence

> Scores every sales call on five coachable dimensions, and refuses to report any flag it can't quote verbatim from the transcript.

- Status: LIVE
- Period: 2026
- Stack: Python, FastAPI, AssemblyAI, Claude tool-use, spaCy, SQLAlchemy, Streamlit
- Live: https://fitnova-dashboard.onrender.com/
- Source: https://github.com/Blacksujit/FitNova-Call-Intelligence
- HTML: https://www.sujit.top/missions/fitnova

## Objective

FitNova sells programs through advisors who take calls all day. Per call, a manager needs to know: did the advisor find the customer's goals, handle objections without overpromising, and stay compliant? Constraint: the calls are heavily **Hinglish**, and full of Aadhaar numbers, PANs and phone numbers.

## Baseline

A team lead samples a handful of calls a week. Most calls are never reviewed, and which ones are becomes politics.

## System

Four stages, plus a human loop on top:

1. **Ingestion** behind a `CallSource` abstract class. Telephony or CRM adapters are one file each; the pipeline never changes.
2. **Transcription + diarisation** with AssemblyAI, language auto-detect for Hinglish. Speakers mapped advisor/customer by a first-speaker heuristic (calls are outbound).
3. **Two-layer PII redaction.** AssemblyAI's server-side redaction, then a local pass with spaCy NER plus regex for Indian identifiers (Aadhaar, PAN, PIN codes), on the transcript *and* on the model's output. Each layer has different blind spots; a leak in either is a leak.
4. **Analysis** with Claude tool-use: a closed set of 7 issue tags with severities, and 5 scores (needs discovery, product knowledge, objection handling, compliance, next-step booking).

Audio is SHA-256 hashed under a unique constraint, so retries can never double-count a call. 12 FastAPI endpoints with role-scoped auth: directors see the org, team leads their team, advisors their own calls.

## Eval

_Data pending: Agreement between model scores and a human reviewer on a labelled set of calls; precision of each issue tag; cost and latency per call._

What exists today is a hard contract instead of a metric: **no tag ships unless its quote is in the transcript**, in both the LLM path and the rule-based fallback.

## Anomalies

**A-1 · Plausible quotes that were never said.** Ask a model for "the line where the advisor overpromised" and it returns a line in exactly that shape, whether or not anyone said it. Fix:

```python
for tag in response.tags:
    if tag.quoted_line not in transcript:
        log.dropped(tag, reason="quote_not_verbatim")
        continue
```

That turns every claim into a fact a human can check in seconds: the property a compliance tool actually needs.

**A-2 · Diarisation fails silently on bad audio.** On mono or low-quality audio, every segment is now tagged `unknown` and the call is flagged `diarization_quality="failed"`. It degrades loudly instead of scoring garbage confidently.

**A-3 · Known limits, documented in the README:**
- SQLite + an in-process queue collapse past ~50 calls/hour, and queued tasks are lost on restart → Celery + Postgres.
- Inbound calls break the first-speaker heuristic → voice-print or the CRM caller field.
- Hindi-dominant segments can slip past English-tuned prompts → bilingual few-shot prompts.
- The TTL cache is unbounded → capped LRU or Redis.

## Outcome

The pipeline runs end to end and the dashboard is live (demo credentials in the repo README). Advisors can contest a flag and a team lead upholds or dismisses it, and every disagreement between model and human is stored, so systematic model errors show up in the data.

The pattern I reuse everywhere now: **if an LLM claim can't be checked against its source by a dumb, deterministic test, it's a rumour, not a feature.**