Your model is fine. Your retrieval architecture is fine. What breaks is undocumented tables, conflicting definitions, missing lineage, and permissions that quietly stop applying three hops downstream. SchemaVita measures whether your data estate — and the way your engineers build with AI — can actually carry what you are shipping.
This is not a hunch. It is the most consistently reported finding in enterprise AI deployment, and it is measurable before you spend another quarter on a pilot.
Both are real deliverables, priced on their own merits, and neither is credited back against later work — that would make it a sales call rather than an audit.
Your AI pilot has stalled and the data is the suspect. Seven dimensions measured against published standards, delivered as a scorecard, a findings register, and a dependency-ordered remediation roadmap.
Your team adopted Claude Code, Cursor, or Copilot under pressure and nobody knows what is entering the codebase. Standards drift, secret handling, review-gate coverage, and supply-chain exposure — measured, then a governance plan.
A measured verdict on whether your data can carry the AI system you intend to build. Scored on evidence rather than interviews, with all measurement artifacts handed over so your team can re-run the numbers themselves.
Building what the assessment says needs building. Fixed-fee against a defined scope, as a focused sprint or a full programme.
For teams that have the systems but not the specialist. Governance ownership, standards enforcement, reliability, and design review before new AI features ship.
The framework derives from public sources rather than invention, so you can check the work. Each dimension scores on a five-level maturity scale, weighted for AI readiness specifically rather than general data health.
Accuracy, completeness, consistency, timeliness, validity, uniqueness — profiled and measured, not asserted. Completeness and consistency carry extra weight, because missing or conflicting data is what produces unreliable models.
Whether your estate is legible to a retrieval system. A table of accurate numbers is not AI-ready if nothing can discover it, its columns are cryptic, or its provenance is unknown.
Whether any value traces to its origin and forward to its consumers. Required for reproducing model results, for staleness detection, and for GDPR, HIPAA, and EU AI Act obligations.
Whether governance controls survive the trip downstream. Sensitivity classification has to happen before indexing — repairing access control at the vector store is already too late.
Whether the same concept looks the same everywhere. Canonical fields, stable identifiers, normalised units and time zones, one agreed definition per metric across teams.
Whether the data arrives, on time, and whether anyone finds out when it does not. Freshness, test pass rates, alerting coverage, and how often a human intervenes by hand.
Cross-cutting. System inventory, ownership, risk categorisation, evaluation practice, incident response for model failure, and third-party model visibility.
Most assessments are interviews with a scorecard attached. This one instruments your estate before anyone gets asked a question, so the conversations interpret evidence rather than collect opinions.
A short call to fix the AI use case in question. RAG, agentic, and predictive systems have different failure modes, and the weighting changes accordingly.
Scripted instrumentation against your warehouse and transformation layer. Coverage, quality profiling, lineage completeness, entitlement inheritance, freshness.
Interviews with owners, consumers, and whoever drives the initiative — to explain the numbers and score what artifacts cannot show.
Scorecard, findings, and a dependency-ordered roadmap, walked through with your decision-maker. No pitch in that meeting.
Read-only access to the warehouse and transformation layer, plus catalog access where one exists. No write permissions at any point.
Everything is covered by a mutual confidentiality agreement before any credential changes hands, and access is revoked at delivery.
No, and that is deliberate. Pricing the assessment on its own merits means I have no financial stake in what the findings say.
If the honest answer is that your data is in better shape than you feared, I want to be able to tell you that plainly.
Two things. The measurement is instrumented rather than interview-led, so the findings are reproducible — you get the artifacts and can re-run them yourselves in six months.
And the framework derives from published standards, so you can check the reasoning rather than taking the method on trust.
Then it says so, and it sets out what to do about it in sequence. The point of measuring is that the answer can go either way.
Knowing early is considerably cheaper than discovering it after another two quarters of pilot work.
Work with them, always. The deliverable is written for your engineers to act on, and the remediation roadmap assigns work by role so it can be picked up internally.
I take a small number of engagements concurrently rather than stacking them, and I will tell you honestly at the first call when I could realistically start.
SchemaVita is Cordero Perez. I build the governance layer that makes AI systems accurate, reliable, and safe to deploy — documentation quality systems, entitlement monitoring, automated security guardrails feeding into AI build workflows, and the standards infrastructure that lets agentic systems act on organisational data without producing confident nonsense.
I do that work today inside a Fortune 500 technology company's finance organisation, under real compliance pressure. Before that, three years as a senior consultant in AI and data engineering at a Big Four firm, and three years as a data analyst for a municipal oversight and investigations division, where the work was cited in the New York Times.
The name means roughly structure brought to life — which is the job. Design the thing properly, then make it run.
Tell me what you are trying to build and where it is stuck. If an assessment is not the right thing, I will say so — sometimes the answer is one conversation, not an engagement.