CallLens

Production-grade AI engineer portfolio project.
Multi-tenant B2B call analytics platform that exposes cross-call intelligence via an MCP server — so any LLM client (Claude Desktop, Codex, Copilot) can answer identity-aware questions about your customer calls.

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What it does

100 real B2B SaaS call transcripts → LangGraph analysis pipeline → Postgres (with RLS) → MCP server → role-aware answers in any LLM.

Ask Claude Desktop "Which accounts are at churn risk this quarter?" as a sales manager and get a different answer than a support lead asking the same question — same data, different framing, different authorization.

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Key engineering features

Feature	Implementation
Multi-tenancy	Postgres Row-Level Security; SET app.tenant_id per connection
Identity-aware MCP	JWT claims (role + account_names) thread through ContextVar into every tool
LangGraph pipeline	6-node graph with 2 HITL interrupt gates; AsyncPostgresSaver checkpointing
Idempotent ingestion	SHA-256 content hash per call folder; single bulk hash query; skip-on-match
Persistent memory	remember_context / recall_context / forget_context tools per JWT sub
Eval harness	Rule-based oracle + LLM-as-judge; regression guard with baseline JSON
Observability	OTEL tracing (FastAPI + pipeline nodes); Langfuse LLM call logging; structlog JSON
Live dashboard	/dashboard — Chart.js charts pulling real-time data from Postgres

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Architecture

┌─────────────────┐   ┌──────────────────────┐   ┌─────────────────────┐
│  ① INGESTION    │   │   ② ANALYSIS PLANE   │   │  ③ MCP SERVING      │
│                 │   │                      │   │                     │
│  100 transcripts│──▶│  LangGraph pipeline  │──▶│  FastAPI + JWT      │
│  Parser (SHA256)│   │  ├─ classify_batch   │   │  MCP Tools (SSE)    │
│  Idempotent     │   │  ├─ 🛑 HITL gate     │   │  Role-scoped tools  │
│  upsert         │   │  ├─ analyze_topics   │   │  Postgres RLS       │
│                 │   │  ├─ detect_risks     │   │  /dashboard         │
│                 │   │  ├─ 🛑 HITL gate     │   │                     │
└─────────────────┘   │  └─ write_insights   │   └──────────┬──────────┘
                      └──────────────────────┘              │ SSE/MCP
                                                            ▼
                                               Claude Desktop · Codex · Copilot

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MCP tools

Tool	Roles	What it returns
get_my_insights	all	Persona-specific insights (support, sales, product, eng)
get_topic_trends	all	Top topics ranked by frequency + avg sentiment
get_account_health	all except eng_lead	Account stats; financials redacted unless sales_manager
get_churn_risks	sales_manager only	High-risk accounts from the AI analysis
search_calls	all	Keyword search across summaries; call types filtered by role
remember_context	all	Save a memory keyed to your JWT sub
recall_context	all	Retrieve your last N memories across sessions
forget_context	all	Delete a specific memory (own only)

Role permission matrix

                     get_my  topic  account  churn  search  memory
support_lead           ✓      ✓       ✓        ✗      ✓       ✓
sales_manager          ✓      ✓       ✓        ✓      ✓       ✓
product_manager        ✓      ✓       ✓        ✗      ✓       ✓
eng_lead               ✓      ✓       ✗        ✗      ✓       ✓

get_account_health additionally redacts contract_value, renewal_date, arr, csm_owner for non-sales roles.

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Observability dashboard

Live at http://localhost:8001/dashboard when the MCP container is running.

Metrics endpoint: GET /api/metrics — JSON, no auth required.

Charts included:

• Sentiment distribution (donut) — 6-level taxonomy: very-negative → very-positive
• Call type breakdown (donut) — external / support / internal
• Top topics (horizontal bar) — ranked by call frequency
• Insights by persona (bar) — 4 personas
• Account health table — avg sentiment score per account with risk badges

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Eval harness

make eval           # accuracy + coverage gate — free, DB queries only
make eval-judge     # LLM-as-judge quality gate — uses API credits
make eval-reset     # clear baseline for a fresh run

Current baseline metrics (Aegis Cloud dataset, 100 calls):

Metric	Score	Threshold
Classification accuracy	100%	≥ 75%
Sentiment direction accuracy	64%	≥ 60%
Insight type coverage	100%	100%

The baseline is saved to tests/evals/baseline_metrics.json on first run. Subsequent runs fail if any metric regresses more than 5%.

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Quick start

Prerequisites

• Docker + Docker Compose
• OPENAI_API_KEY (or Anthropic key)

1 — Configure

cp .env.example .env
# Edit .env: set OPENAI_API_KEY

2 — Start infrastructure

docker compose up -d postgres redis

3 — Ingest transcripts

docker compose run --rm --entrypoint calllens-ingest app
# Output: New: 100 / Updated: 0 / Skipped: 0 / Errors: 0

4 — Run the analysis pipeline

make pipeline-run
# Runs all 6 LangGraph nodes; pauses at HITL gates if any uncertain calls

5 — Start the MCP server

make mcp-up
# Server at http://localhost:8001
# Dashboard at http://localhost:8001/dashboard

6 — Generate test tokens

make mcp-token
# Prints JWT tokens for all 4 personas

7 — Connect Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "calllens": {
      "url": "http://localhost:8001/mcp/sse",
      "headers": {
        "Authorization": "Bearer <your_sales_manager_token>"
      }
    }
  }
}

Then ask: "Which accounts are at churn risk? What actions should I take this week?"

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Project structure

calllens/
├── src/calllens/
│   ├── ingestion/        # Parser, writer, idempotent CLI
│   ├── agents/           # LangGraph graph, nodes, prompts, state
│   ├── mcp/              # FastMCP server, JWT auth, tools, dashboard
│   ├── eval/             # Metrics oracle, LLM-as-judge
│   ├── observability/    # OTEL setup, structlog
│   └── llm/              # Provider factory with Langfuse callback
├── tests/
│   ├── unit/             # Parser tests
│   ├── integration/      # DB ingestion tests
│   └── evals/            # Accuracy gate, LLM judge, regression guard
├── migrations/           # Postgres schema with RLS policies
├── docs/
│   └── architecture.svg
└── docker-compose.yml

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Makefile reference

make up               # Start Postgres + Redis
make ingest           # Run ingestion CLI
make pipeline-run     # Run the full LangGraph pipeline
make pipeline-review  # Review a specific batch BATCH_ID=...
make mcp-up           # Start MCP server on :8001
make mcp-token        # Generate JWT tokens for all personas
make eval             # Run the accuracy eval suite
make eval-judge       # Run LLM-as-judge quality eval
make smoke            # Quick DB sanity check
make psql             # Open Postgres shell

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Environment variables

Variable	Required	Description
OPENAI_API_KEY	Yes	LLM provider key
JWT_SECRET	Yes (prod)	HS256 signing secret
LANGFUSE_PUBLIC_KEY	No	LLM observability (cloud.langfuse.com)
LANGFUSE_SECRET_KEY	No	LLM observability
OTEL_EXPORTER_OTLP_ENDPOINT	No	OTLP endpoint (e.g. Jaeger)
LLM_PROVIDER	No	openai (default) or anthropic
LLM_MODEL	No	gpt-4o-mini (default)

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Design decisions

Why Postgres + RLS instead of a vector DB?
The 100-call dataset fits entirely in Postgres. Adding a vector DB adds ops cost without meaningful recall improvement at this scale. Semantic search can be layered on with pgvector when needed.

Why LangGraph instead of a simple loop?
HITL (human-in-the-loop) interrupt gates are the hard part. LangGraph's interrupt() + AsyncPostgresSaver gives durable, resumable checkpoints — the pipeline can pause, wait for human corrections, and resume without re-running completed nodes.

Why MCP instead of a REST API?
MCP makes the analytics available to any LLM client without building a custom chat UI. The role-scoped tools and RLS-enforced DB queries mean the LLM gets only what the authenticated user is allowed to see.

Why JWT ContextVar instead of passing claims as arguments?
MCP tools have fixed signatures defined by the server. Threading claims through function arguments would pollute every tool signature. ContextVar gives clean per-request scoping in async code without changing the tool interface.