§ How it works

Wrong answers don't announce themselves. Nodal makes them surface.

Nodal is the evaluation layer for AI analytics: it watches what your organization asks and regression-tests the context your agents can access. Keep the context layer your team already trusts. If you do not have governed business context yet, Nodal Context is the interview-built, open-source starting point. Read the docs ↗

Context-layer independent

Bring your existing context—or build it with Nodal Context. Nodal measures the same agent with and without the context it can access, adding evaluation and observability without requiring you to migrate definitions first.

See how Nodal fits alongside your agent, catalog, semantic layer, and data observability →
Pillar I — Observability

See what your organization actually asks.

Non-technical users don't ask fully-specified questions. Nodal makes the gaps visible before SQL runs — so widening access doesn't scale plausible-but-wrong answers. And every interaction is captured: your data team sees who asked what, when, and how it was interpreted — attributed and traceable.

  • Question reframed with defaults from your documentation; assumptions in brackets the user can change.
  • Confidence score from auditable signals — entity resolution, schema grounding, doc coverage, context freshness.
  • You approve the interpretation, not the SQL.
  • Every question, every answer, every escalation — logged to your data team's Slack channel with full attribution.
  • Every under-specified question becomes a signal — and a candidate test case for the eval suite.

Observability in the docs ↗

Claude
You

How does length of stay compare across facilities in the Northeast?

Claude
Your question, disambiguated

What is the [mean inpatient days] per facility for [all facility types] in [the Northeast region] over [trailing 12 months — assumed]?

Defaults pulled from your documentation. Change any [bracket] before I run it.

Where each default came from
  • "mean inpatient days" — dbt model fct_encounters: discharge_date - admission_date (Confluence confirmed)
  • "Northeast" — dim_facilities.region = 'Northeast' (found in 12 dashboards)
  • "all facility types" — no filter specified; you may want acute care only
  • "trailing 12 months" — assumed; not stated in your question
Confidence score 71/100
  • Core metric well-documented across sources
  • Join path validated in dbt lineage
  • Time window assumed
  • Facility scope may be broader than intended

Should I run this, or would you like to change any of the defaults?

You

Narrow to acute care only. Run it.

Coverage map — questions asked vs. validated coverage; out-of-distribution flagged for review The Nodal admin console Questions map: every question business users asked plotted against the validated eval corpus; one out-of-distribution question flagged in red for analyst review.
Observability — the full loop in action
Pillar II — Evaluation · CI for analytics

Test context like code — when the business changes, nothing should break.

Every code change gets a pull request. A number gets none — it just ships. Nodal gives analytics its CI: every dbt commit, doc edit, prompt change, or model swap triggers a re-run, drift gets attributed to the specific change that caused it, and accuracy gets measured per piece of the system — not assumed.

  • Re-run on every change — schema migrations, dbt commits, doc edits, prompt changes, model swaps. Failures get pinned to the commit that caused them, with affected questions, SQL diffs, and result deltas.
  • Ablation tests on each context source — drop a data dictionary, a Notion page, a glossary entry; measure the answer-quality delta against the token-cost delta.
  • Model trade-off tests — swap Claude for a cheaper model, Codex for Gemini; read off pass rate vs. cost per run.
  • Cost optimization stops being a guess — every piece of the system is benchmarked against the trust it actually delivers.

Evaluation & drift detection in the docs ↗

Benchmark Run — April 8, 2026

Trigger: dbt model change (commit a3f8c2d)

92 questions evaluated
88 passed
4 drifted
0 failed
Affected

dim_patients → enrollment_status

Drifted questions
  1. "Active Medicare patients by region" — result changed
  2. "Enrollment trend by quarter" — confidence score dropped -12
  3. "Payer mix for active patients" — SQL changed
  4. "Patient count by enrollment status" — result changed
View full benchmark report View dbt diff
Documentation health report
67% of answered questions relied on dbt column descriptions
23% used Confluence documentation — but 40% of those pages hadn't been updated in over a year
15% lower consistency on questions grounded in stale docs
Pillar III — Governed context

Your governed context, every agent.

Bring the agent and context you already chose — swap models without swapping truth. Nodal evaluates the context your agent can access and the hosted MCP endpoint can serve governed context to Claude, Codex, Gemini, Cursor, or whatever comes next. Data skills stay grouped and owned by the data team; other teams build on known-good context. Where Nodal fits →

Nodal does not require Nodal Context. Ablation tests run the same agent with and without whichever context sources it can access, measuring answer-quality delta against token-cost delta per source. If you need a governed business-context layer, Nodal Context is free and open source.

Context
GitHub Notion Snowflake Cortex Atlan
Agents
Claude Code Codex Gemini Cursor Claude
Warehouse
Snowflake BigQuery Redshift
BI
Sigma Tableau Hex Looker
analyst@nodal — answered
What's our revenue last quarter?
$4.2M
Q4 2025 · ↑ 12% vs Q3 2025
source
fct_revenue (dbt) · finance domain
fresh
3h ago
trust
94 / 100 · context match
Answered in 0.8s — no escalation
Proof — same question, Claude Code and Codex, same governed answer
Walkthrough — Nodal on a live stack
End of dispatch

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