Comparisons
Two honest comparisons, not a scoreboard. ClariLayer is your individual-analyst context layer, delivered over MCP — and the clearest way to understand it is against the two things analysts reach for first.
When an analyst wants their AI to stop re-explaining the data, two options come up. The first is writing the definitions down by hand in a CLAUDE.md or notes file. The second is reaching for a semantic layer or catalog — dbt, Cube, a metrics store, a data dictionary. Both are reasonable. Neither is the same thing ClariLayer does, and saying so honestly is more useful than pretending we replace them.
The thread through both comparisons is the same distinction. ClariLayer does not compute your numbers and it is not a warehouse of definitions you have to trust on faith. It is the trust layer: it reconciles a saved definition against the result your agent actually computed, shows provenance and status, and surfaces a caveat when the declared definition drifts from the real result — instead of handing your agent a confident, unchecked answer. We describe what is reconciled and what is merely asserted, and we never stamp a claim present-tense “verified.”
vs the free default
Typing your definitions into a notes file is a real, useful start — but nothing checked it against your warehouse. See the same definition as a ClariLayer entry: reconciled, with provenance and a caveat when the declared definition no longer matches the actual result.
Asserted vs reconciledvs the execution layer
A semantic layer (dbt, Cube, and the like) or a data catalog answers how a number is computed and where it lives. ClariLayer answers what your agent should trust — reconciled against your real result, not asserted. They sit at different layers, and they compose.
Execution vs trust layerThe one distinction that runs through both
A definition you typed and a definition that was reconciled against your warehouse are not the same thing — even when the words are identical. The first is asserted: nothing checked it, so when it drifts your agent keeps trusting the stale line, confidently. The second carries its provenance and status, and a declared-vs-actual mismatch shows up as a caveat you can act on.
That is the trust gap ClariLayer exists to close. A hand-written CLAUDE.md never closes it. A semantic layer makes the computation right but does not, on its own, tell your agent which saved definition has quietly diverged from what the warehouse now returns. Both comparisons are really the same story, told against two different starting points.
Connect ClariLayer to Claude Code, Cursor, or Codex. Keep your CLAUDE.md, keep your semantic layer — and add the trust layer that reconciles what your agent should believe against your real result.
Connect your AIWe use privacy-friendly analytics
With your consent we use PostHog and Vercel Analytics to understand how ClariLayer is used so we can improve it. We never sell your data. Errors are always monitored (without analytics) so we can keep the app reliable. You can change your mind anytime.