# ClariLayer

> ClariLayer is the context layer that checks itself — 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 that checks itself — 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
- [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)