Discovery: 8.7/10
Confidence: 0.91

Evidence:
  04:32  Representative asks customer about their current operational bottleneck.
  05:17  Representative asks about business impact.
  07:02  Customer explains delivery delays.

Missing behavior:
  Representative never established urgency or implementation timeframe.

flowchart TD
    A[Audio / Transcript] --> B[Ingestion]
    B --> C[ElevenLabs STT + Diarization]
    C --> D[Transcript Normalization]
    D --> E[LangGraph]
    E --> F[Deterministic Metrics]
    E --> G[Semantic Analysis]
    G --> H[Sentiment]
    G --> I[Topics]
    G --> J[Intents]
    F --> K[Evidence Verification]
    H --> K
    I --> K
    J --> K
    K --> L[Confidence Gate]
    L -->|sufficient| M[Report]
    L -->|insufficient| N[Bounded Re-score]
    N --> K

cp .env.example .env
docker compose up

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Analyze a transcript (offline, deterministic)
calllens analyze sample.txt

# Start the API server
calllens server

# Run the evaluation harness
calllens eval run

cd apps/web
npm install
NEXT_PUBLIC_API_URL=http://localhost:8000 npm run dev

calllens analyze call.mp3
calllens analyze call.mp3 --rubric consultative_sales --output report.json
calllens rubric list
calllens rubric validate ./my_rubric.yaml
calllens eval run
calllens server

import asyncio
from calllens import CallLens


async def main():
    async with CallLens(base_url="http://localhost:8000") as client:
        call = await client.calls.upload("sales-call.mp3")
        await call.analyze(rubric="consultative_sales")
        report = await call.report()
        print(report["overall_score"], report["confidence"])


asyncio.run(main())

POST   /api/v1/calls                     upload a recording or transcript
GET    /api/v1/calls
GET    /api/v1/calls/{id}
POST   /api/v1/calls/{id}/analyze
GET    /api/v1/calls/{id}/analysis       the evidence-backed CallReport
GET    /api/v1/calls/{id}/transcript
DELETE /api/v1/calls/{id}                privacy/retention deletion
POST   /api/v1/rubrics                   register a declarative rubric
GET    /api/v1/rubrics
POST   /api/v1/rubrics/validate
GET    /api/v1/reps/{id}/analytics
POST   /api/v1/evals/run

name: consultative_sales
version: "1.0"
dimensions:
  rapport:
    label: Rapport
    weight: 0.08
  discovery:
    label: Problem Discovery
    weight: 0.16

# .env — speech + reasoning
ELEVENLABS_API_KEY=sk_...
ELEVENLABS_STT_MODEL=scribe_v2

# Any OpenAI-compatible endpoint, e.g. Melious (https://api.melious.ai/v1)
LLM_PROVIDER=compatible
COMPATIBLE_BASE_URL=https://api.melious.ai/v1
COMPATIBLE_API_KEY=sk-mel-...
LLM_MODEL=gpt-oss-120b

# Option A — you have a recording: transcribe + analyze it live
# (Scribe v2 STT → metrics → evidence-backed scoring → coaching)
calllens analyze call.mp3 --rubric consultative_sales --output report.json

# Option B — no recording? Synthesize a two-speaker sample call with ElevenLabs TTS
python examples/generate_sample_call.py   # → sample_call.mp3
calllens analyze sample_call.mp3 --rubric consultative_sales --output report.json

# Both write the evidence-backed report (scores + timestamped evidence + coaching)
# to report.json; omit --output to print it to stdout.

mcpize analyze && mcpize doctor && mcpize deploy

pip install -r requirements.txt
python mcp-server/server.py

| Dimension | MAE | Correlation |
|-----------|-----|-------------|
| Discovery | .61 | .88         |
| Rapport   | .74 | .81         |