# 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 agent to distill the durable facts from a working session into your Inbox; explicit-request only, you approve each, and the transcript is never sent to ClariLayer (only the distilled candidate facts cross the boundary).
- Each explicit reconcile checks one compatible saved definition through one shipped adapter. Warehouse definitions accept agent-supplied actual_sample evidence: columns, an optional row count, and optional preview rows. HubSpot CRM definitions accept bounded crm_evidence: property metadata and aggregate value distributions, with CRM rows recursively forbidden.
- HubSpot reconcile is generally available to authenticated organizations behind the global emergency kill switch. Salesforce CRM contracts can be stored and recalled, but Salesforce reconcile is disabled.
- In the personal MCP reconciliation path, ClariLayer never holds source credentials, executes SQL, or calls HubSpot; the agent is the connector. Optional preview rows are permitted only in warehouse actual_sample evidence, while CRM evidence is row-free. Separately configured team connectors belong to the gated Governed Context Edge surface and are not part of this personal path.
- context_checkpoint durably and idempotently persists the agent's managed-protocol context_updated or no_update_required declaration. It validates ownership and eligibility of referenced ClariLayer objects; it does not independently prove an entry changed or external work happened. Proposal-only work and update_failed cannot complete the loop, and the receipt never certifies code, deployment, query output, provider state, or semantic correctness.
- Statuses are asserted and caveat. The stronger verified status is not live and remains gated off.
- The context you build compounds across sessions and is portable across claude.ai, Claude Code, Cursor, and Codex. The Governed Context Edge — shared team canon, Diff-to-team, and conflict adjudication — is built and in a hand-run private pilot; teams request access at clarilayer.com/for-teams.

## Docs
- [ClariLayer Docs](https://clarilayer.com/docs)
- [MCP Quickstart](https://clarilayer.com/docs/getting-started)
- [bootstrap verb](https://clarilayer.com/docs/quickstart/bootstrap)
- [Use ClariLayer with Databricks (semantic_model import)](https://clarilayer.com/docs/quickstart/databricks)
- [harvest & propose workflow (propose_batch)](https://clarilayer.com/docs/quickstart/harvest)
- [recall verb (get_analysis_context)](https://clarilayer.com/docs/quickstart/recall)
- [remember verb](https://clarilayer.com/docs/quickstart/remember)
- [reconcile verb](https://clarilayer.com/docs/quickstart/reconcile)
- [Verified vs Asserted](https://clarilayer.com/docs/concepts/verified-vs-asserted)
- [The Context Layer](https://clarilayer.com/docs/concepts/context-layer)
- [The Reasoning Trail](https://clarilayer.com/docs/concepts/reasoning-trail)
- [AI Agent Context Guide](https://clarilayer.com/docs/guides/ai-agent-context)
- [dbt-check CLI Guide](https://clarilayer.com/docs/guides/dbt-check)
- [SaaS Metrics Definition Library](https://clarilayer.com/docs/reference/saas-metrics)
- [Annual Recurring Revenue Definition](https://clarilayer.com/docs/reference/saas-metrics/arr)
- [Monthly Recurring Revenue Definition](https://clarilayer.com/docs/reference/saas-metrics/mrr)
- [Net Revenue Retention Definition](https://clarilayer.com/docs/reference/saas-metrics/nrr)
- [Churn Definition](https://clarilayer.com/docs/reference/saas-metrics/churn)
- [Pipeline Coverage Definition](https://clarilayer.com/docs/reference/saas-metrics/pipeline-coverage)
- [Gross Revenue Retention Definition](https://clarilayer.com/docs/reference/saas-metrics/gross-revenue-retention)
- [Expansion MRR Definition](https://clarilayer.com/docs/reference/saas-metrics/expansion-mrr)
- [Contraction MRR Definition](https://clarilayer.com/docs/reference/saas-metrics/contraction-mrr)
- [Customer Churn Rate Definition](https://clarilayer.com/docs/reference/saas-metrics/customer-churn-rate)
- [Logo Retention Definition](https://clarilayer.com/docs/reference/saas-metrics/logo-retention)
- [Average Revenue per Account Definition](https://clarilayer.com/docs/reference/saas-metrics/arpa)
- [Annual Contract Value Definition](https://clarilayer.com/docs/reference/saas-metrics/acv)
- [ARR Growth Rate Definition](https://clarilayer.com/docs/reference/saas-metrics/arr-growth-rate)
- [Sales Cycle Length Definition](https://clarilayer.com/docs/reference/saas-metrics/sales-cycle-length)
- [Win Rate Definition](https://clarilayer.com/docs/reference/saas-metrics/win-rate)
- [Forecast Accuracy Definition](https://clarilayer.com/docs/reference/saas-metrics/forecast-accuracy)
- [API v1 Overview](https://clarilayer.com/docs/api/v1)
- [API v1 Authentication](https://clarilayer.com/docs/api/v1/authentication)
- [GET /api/v1/metrics and GET /api/v1/metrics/{id}](https://clarilayer.com/docs/api/v1/metrics)
- [GET /api/v1/metrics/{id}/contract](https://clarilayer.com/docs/api/v1/metrics-contract)

## Blog
- [Semantic Recall: Helping Your AI Find What It Saved](https://clarilayer.com/blog/semantic-recall-find-saved-context)
- [We checked every public dbt project we could find for docs-vs-warehouse drift](https://clarilayer.com/blog/dbt-docs-warehouse-drift-survey)
- [How a Context Layer Differs From Notes and AI Memory](https://clarilayer.com/blog/context-layer-vs-notes-and-ai-memory)
- [Anthropic and OpenAI both said context is the bottleneck for data agents. Here's what they didn't say.](https://clarilayer.com/blog/what-anthropic-and-openai-didnt-say-about-context-for-data-agents)
- [Your AI Agent Used a Retired Metric Definition. Did It Tell You?](https://clarilayer.com/blog/post-trust-benchmark-v2-1)
- [The ClariLayer Trust Benchmark v1: A 2,136-Call Study of AI Accuracy](https://clarilayer.com/blog/post-trust-benchmark-v1)
- [The Context Gap: Why Warehouses and Semantic Layers Aren't Enough](https://clarilayer.com/blog/context-gap-warehouses-semantic-layers)
- [What ClariLayer Does (And What It Does Not)](https://clarilayer.com/blog/what-clarilayer-does)
- [Why AI Agents Need a Context Layer](https://clarilayer.com/blog/why-ai-agents-need-context)
- [Why Your Metrics Need a Context Layer](https://clarilayer.com/blog/why-metrics-need-context)