Blog
Field notes for people who work with AI.
Practical writing about selected memory across projects and sessions, explicit capture controls, and the specialist Analytics/CRM trust path.
Editorial Focus
Practical writing about continuity, control and evidence.
The ClariLayer blog is organized around work that should remain useful beyond one conversation. We cover what deserves to be durable, how selected history becomes a reviewed proposal, and how correction, forget and exclusions keep memory honest.
Analytics remains a deep editorial lane: warehouse and HubSpot contracts, evidence boundaries and caveat-aware reconciliation. Any 36/38 versus 26/38 result belongs to that internal paired Analytics evaluation, not to general memory.
Context that survives the next session
How confirmed facts, preferences, decisions, rules and lessons stay useful across projects and sessions when their source, scope and applicability remain attached.
Read about how recall and remember workReconciling source rules that drift
The gap between a saved contract and what the source evidence shows. That includes warehouse results and bounded, row-free HubSpot property observations: your agent keeps the credentials, and a mismatch surfaces as a caveat.
Read about the reconcile momentSelected history and bounded capture
How selected supported local history becomes an exact preview before import, why provider qualification is separate from source support, and how later capture gets its own configuration and exclusions.
Read about the quickstartStart Here
Go from an idea to the agent you already work in.
Each path points back to a substantive product page so you can move from editorial framing to actually connecting ClariLayer: features for what the context layer does, use cases for the moments you recognize, and the quickstart for installing it into your agent.
New to ClariLayer
Start with the feature overview: selected-space recall, durable memory, correction, scoped forget and optional bounded capture, with Analytics kept as a specialist path.
Open the ClariLayer feature overviewRecognize the moment
Read the use cases when a project decision keeps disappearing, old guidance needs correction, selected history needs review, or two Analytics numbers will not reconcile.
Open the use casesConnect your AI
Ready to connect? The quickstart covers compatible clients, the selected-space boundary, and the choices that remain separate from connection.
Open the quickstartSemantic Recall: Helping Your AI Find What It Saved
ClariLayer semantic recall helps your AI find saved context beyond exact keywords, with automatic index updates and explicit consent from your organization.
We checked every public dbt project we could find for docs-vs-warehouse drift
The documentation was correct. The code was wrong. We checked every public dbt project we could find, and nine of twelve production projects document columns their warehouse does not have.
How a Context Layer Differs From Notes and AI Memory
A folder of notes or agent memory remembers what you wrote. It can't check itself against your warehouse or tell you when a definition has gone stale. The honest difference.
Anthropic and OpenAI both said context is the bottleneck for data agents. Here's what they didn't say.
Anthropic and OpenAI both concluded the bottleneck for data agents is context, not SQL generation. Field notes from building past the failure modes they describe — for the analyst with no data team.
Your AI Agent Used a Retired Metric Definition. Did It Tell You?
Across 9,000 single-turn SQL questions, ClariLayer's governed envelope produced canonical-with-rejection on 297/360 Drift calls (82.5%) vs 0-1 across the four non-governed baselines.
The ClariLayer Trust Benchmark v1: A 2,136-Call Study of AI Accuracy
AI agents writing SQL against your warehouse get definitional questions wrong 91-99% of the time. We built an 89-question benchmark to measure it.
The Context Gap: Why Warehouses and Semantic Layers Aren't Enough
Your warehouse computes numbers. Your semantic layer queries them. But who governs what metrics mean? Meet the context layer — the missing third layer.
What ClariLayer Does (And What It Does Not)
ClariLayer is not a warehouse, not a semantic layer, and not a wiki. It is the context layer — the missing piece that captures meaning, ownership, and trust for business metrics.
Why AI Agents Need a Context Layer
AI agents are making autonomous decisions based on metric definitions. But no tool captures the business context they need to act responsibly. This is the context layer gap.
Why Your Metrics Need a Context Layer
Data warehouses tell you how a number is computed. But your AI agents need to know what it means, who owns it, and whether they should trust it.