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ClariLayer

ClariLayer is the context layer you never have to curate — it grows out of the work you are already doing, and every definition is reconciled against your source, not just asserted. Connect it to claude.ai, Claude Code, Cursor, or Codex over MCP. It bootstraps five artifact kinds — SQL, dbt, CLAUDE.md, dictionary/codebook, and semantic model — and reconciles one saved definition at a time through warehouse actual_sample or row-free HubSpot crm_evidence, so your agent stops re-explaining your dat

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# ClariLayer

> ClariLayer is the context layer you never have to curate — it grows out of the work you are already doing, and every definition is reconciled against your source, not just asserted. Connect it to claude.ai, Claude Code, Cursor, or Codex over MCP. It bootstraps five artifact kinds — SQL, dbt, CLAUDE.md, dictionary/codebook, and semantic model — and reconciles one saved definition at a time through warehouse actual_sample or row-free HubSpot crm_evidence, so your agent stops re-explaining your data every session.

## Key Pages
- [Home](https://clarilayer.com)
- [Features](https://clarilayer.com/features)
- [Use Cases](https://clarilayer.com/use-cases)
- [Pricing](https://clarilayer.com/pricing)
- [About](https://clarilayer.com/about)
- [Blog](https://clarilayer.com/blog)
- [Methodology](https://clarilayer.com/methodology)
- [For teams](https://clarilayer.com/for-teams)

## Comparisons & Security
- [Security](https://clarilayer.com/security)
- [Comparisons](https://clarilayer.com/comparisons)
- [ClariLayer vs CLAUDE.md](https://clarilayer.com/comparisons/claude-md)
- [ClariLayer vs Semantic Layers](https://clarilayer.com/comparisons/semantic-layer)

## Product Facts
- ClariLayer is the context layer you never have to curate — it grows out of the work you are already doing, and every definition is reconciled against your source, not just asserted. It is delivered over MCP so an individual analyst or RevOps operator can give their AI durable, checkable context.
- In our internal paired eval — three batteries, 38 pre-registered data questions, same agent with and without the layer — the agent scored 36/38 with ClariLayer connected vs 26/38 without. The cheaper the model, the bigger the lift (the control went confidently wrong under session load).
- It is single-player: one analyst or RevOps operator, their own data, their own agent — no team account or procurement required. The production service supports claude.ai through OAuth; Claude Code, Cursor, and Codex connect over MCP with a context key.
- Connect the clarilayer MCP server at POST /api/mcp/mcp. The four headline verbs are recall (get_analysis_context), remember, bootstrap, and reconcile; context_checkpoint separately records a bounded completion receipt.
- bootstrap bulk-ingests context from artifacts you already have — five source kinds: validated SQL (deterministically structured into tables/joins/grain), a data dictionary / codebook (the agent maps it into structured per-variable schema-notes), a semantic-layer artifact such as a Databricks Metric View or dbt semantic models (the agent normalizes it into structured models, one canonical metric definition each), dbt models, and CLAUDE.md / freeform notes.
- propose and propose_batch suggest context for your review instead of saving it directly: proposals land in your Context Inbox to accept or reject (nothing is recalled until you accept). propose_batch is the bulk path and the vehicle for conversation harvest — ask your agen
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