Your AI is only as trustworthy as the metrics it can't see are wrong.
Stratum reads the DAX logic across your entire Power BI portfolio and proves whether the same metric means the same thing — before Copilot and your agents scale a silent inconsistency to everyone.
AI doesn't audit the data it's pointed at. It answers.
A human analyst pauses when two reports disagree. An AI agent reasons from whatever semantic layer it finds, picks a definition, and presents the result as fact — to everyone who asks, at machine speed.
Catches it, questions it
Sees two revenue figures, senses something's off, traces it back, and flags the discrepancy before it reaches a decision.
One wrong number, caught once.
Inherits it, amplifies it
Selects one definition with no transparency, or synthesizes across conflicting ones — then repeats that answer confidently to every user, in every query.
One wrong definition, scaled to everyone.
Six things that turn an unaudited semantic layer into AI-ready ground.
One pipeline, run across your whole portfolio — not a single model at a time, and not a raw list of 300 violations.
Semantic consistency analysis
Reads DAX logic across every dashboard to detect where the same metric diverges — regardless of how it's named. The thing no other tool does.
Canonical measure registry
Establishes the one authoritative definition for each business metric, reconciled across all models — so AI reasons from a single trusted source.
AI-ready documentation
Generates plain-English interpretations of every measure. Feeds directly into RAG pipelines, Copilot descriptions, and agent tool definitions.
Trust scoring
Flags which metrics are safe for AI consumption today and which carry conflicts — a prioritized remediation list, not an all-or-nothing gate.
Semantic versioning & drift
Detects when a definition changes between cycles and flags the AI tools that referenced the old one — before stale context produces wrong answers.
Portfolio-scale coverage
Analyzes 10, 20, or 50+ models simultaneously. AI readiness is only as strong as your least-audited dataset — so nothing gets skipped.
Know exactly which AI use cases your semantic layer can carry.
Stratum maps your portfolio against four stages. Most organizations discover they're deploying AI a stage or two below where they assumed they were.
Unaudited
No cross-dashboard analysis. Definitions unknown. AI outputs unreliable on any shared concept.
Inventoried
Measures catalogued per model, but never compared across them. Conflicts still invisible.
Consistent
Conflicts identified and resolved. Canonical registry in place. Reliable multi-model Copilot and RAG.
Governed
Trust scoring live, versioning active, docs auto-maintained. Full deployment with drift alerting.
Stratum moves you from Stage 1 to Stage 3 in the first cycle — across the line where AI stops being a liability and starts being trustworthy. See the full readiness model →
We never hold your data — because we're built so we can't.
Stratum operates exclusively on structural metadata exported from your model. Not a security promise layered on top — a boundary the tool is built around.
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No tenant onboardingNo connection to your Power BI Service, no Premium capacity, no access to your workspace.
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Structural metadata in, insight outVPAX and data-free PBIP describe the model's shape and logic — never the rows inside it.
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Runs where your data livesAnalysis happens locally, on your terms — the trust story that survives procurement and security review.
The category has documentation and cataloguing. It doesn't have comprehension.
One model at a time
They describe a single model and produce static output that goes stale the moment the model changes.
Breadth, not logic
They govern the platform — lineage, access, glossaries — at a level well above the calculation itself.
Consume, never check
They surface answers from whatever semantic layer they find. Auditing that layer is not their job.
It's the difference between knowing what your dashboards are called and knowing what they actually do.
Value in the first cycle, before any commitment to scale.
Delivered as an expert assessment first — with the option to license the tool for continuous governance once you're ready to own it.
Pilot
A scoped assessment of a target portfolio. Semantic conflicts, trust scores, and an AI-readiness verdict — demonstrable value inside the first documentation cycle.
Deploy
Findings become a remediation roadmap. Conflicts resolved deliberately, a canonical registry established, documentation generated as a byproduct.
Operationalize
Refresh cadence and drift detection go live. Governance becomes continuous — because semantic drift is continuous too.
Consulting-led, with a licensing option. Most teams start with an assessment and the interpretation that comes with it. Teams managing large portfolios with active AI roadmaps graduate to owning Stratum directly.
Prove your semantic layer is ready — before your AI assumes it is.
Start with a pilot assessment of one portfolio. You'll see exactly where your metrics diverge, and what it would take to make them AI-safe.