Klarova

The trust layer between AI agents and your company's data.

Correct, cited, access-scoped answers — served to any agent over MCP.

The problem

A generic agent — Claude, Cursor, ChatGPT — can already plug into your database or document store. But it doesn't understand your business, so it's confidently wrong: it writes SQL that misreads your schema, answers from whatever it happened to retrieve, cites nothing, and respects no rule about who may see which row or document. That's unshippable anywhere a wrong number or a leaked row has real cost.

Related MCP server: CRMy

The fix

Klarova is a governed context layer that sits between any agent and your data. It does no reasoning of its own — it's the trustworthy foundation an agent stands on. It holds your governed definitions, the grounding that lets an agent query accurately, and governed execution that scopes, runs, and audits every access. It's exposed over MCP, so any agent gets the same correct, cited, scoped answer.

The agent is replaceable; the context layer is the moat.

✨ What it does

• 🧠 Semantic layer — governed metric definitions, so an agent picks a metric by name instead of guessing the maths.

• 🗄️ Text-to-SQL, governed — validated read-only queries, executed and returned with the exact SQL that produced each number.

• 🔍 Governed enterprise search — hybrid (dense + sparse) retrieval over your documents, reranked and multi-hop, every answer citing its source span.

• 🔗 Cross-source synthesis — combine a governed warehouse number and a document fact into one answer that carries both citations.

• 🛡️ Governed access — identity-scoped rows and documents, enforced inside execution and written to an audit trail.

• 🔌 MCP-native — plug in Claude, Cursor, or Klarova's own copilot; all get the same governed result.

• 📊 An evaluation harness — every capability has an objective test (execution accuracy, retrieval recall, citation match), reported as a number.

🧩 How it works

Two things sit between a person and their data, and keeping them cleanly separate is the whole design.

• The context layer (the moat) — passive, reusable, MCP-exposed. A semantic model, grounding & memory, and governed execution. Does no reasoning.

• The agent (the copilot) — the reasoning consumer that turns a question into an answer by consuming the layer: plan → generate → execute → investigate → verify → synthesize → act → remember. Klarova ships its own reference copilot, but it sits in the same position as Claude or Cursor.

Because the layer holds no agent logic, any agent gets the same governed answers — which is exactly why it, not the chat, is the durable asset.

🚀 Quickstart

Requirements: Python 3.12+ and uv.

git clone git@github.com:samson-ailabs/Klarova.git
cd Klarova
uv sync                 # install dependencies into .venv
cp .env.example .env    # then add your OpenRouter + embedder keys
uv run klarova          # the development CLI/REPL

Development workflow:

uv run ruff check .     # lint
uv run ruff format .    # format
uv run mypy src         # type-check (strict)
uv run pytest           # tests

🗺️ Status & roadmap

⚠️ Early stage — the bootstrap is in place; the walking skeleton is next.

The build follows a walking skeleton, then deepen plan: the thinnest slice that runs and is measured first, then one capability at a time, with every step leaving the system running and re-measured.

Vertical 1 grounds the layer over a warehouse (the numbers) and internal documents (the knowledge), and across both. The same engine-core later serves other domains (data-ops, CRM) by swapping connectors, not the core.

Full plan in docs/ROADMAP.md · design in docs/ARCHITECTURE.md · decisions in docs/decisions/.

🛠️ Built with

Python 3.12+ · LangChain / LangGraph v1 (primitives only) · DuckDB + sqlglot (governed SQL) · Qdrant + FastEmbed (hybrid retrieval) · the official mcp SDK · OpenRouter (model gateway) · ruff · mypy · pytest.

📄 License

Apache-2.0. Every source file carries an SPDX header.