Your model is fine. What breaks is undocumented tables, conflicting definitions, missing lineage, and permissions that stop applying three hops downstream. SchemaVita measures whether your data estate can carry what you're shipping.
Not a hunch — the most consistently reported finding in enterprise AI deployment, and measurable before you spend another quarter on a pilot.
Both are real deliverables, priced on their own merits and never 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 — scorecard, findings register, and a dependency-ordered roadmap.
Your team adopted Claude Code, Cursor, or Copilot under pressure and nobody knows what's entering the codebase. Standards drift, secret handling, review gates, 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 every artifact handed over so your team can re-run the numbers.
Building what the assessment says needs building. Fixed-fee against a defined scope, as a focused sprint or a full program.
For teams with the systems but not the specialist. Governance ownership, standards enforcement, and design review before new AI features ship.
The framework derives from public sources, so you can check the work. Each dimension scores on a five-level maturity scale, weighted for AI readiness rather than general data health.
Accuracy, completeness, consistency, timeliness, validity, uniqueness — profiled and measured, not asserted. Completeness and consistency carry extra weight.
Whether your estate is legible to a retrieval system. Accurate numbers aren't AI-ready if nothing can discover them or their provenance is unknown.
Whether any value traces to its origin and forward to its consumers. Required for reproducibility, staleness detection, and GDPR, HIPAA, and EU AI Act obligations.
Whether governance controls survive the trip downstream. Classification has to happen before indexing — repairing it at the vector store is too late.
Whether the same concept looks the same everywhere. Canonical fields, stable identifiers, one agreed definition per metric.
Whether the data arrives on time, and whether anyone finds out when it doesn't. Freshness, test pass rates, alerting coverage.
Cross-cutting. System inventory, ownership, risk categorization, evaluation practice, and incident response for model failure.
Most assessments are interviews with a scorecard attached. This one instruments your estate first, so the conversations interpret evidence rather than collect opinions.
A short call to fix the AI use case. RAG, agentic, and predictive systems have different failure modes, and the weighting changes accordingly.
Scripted instrumentation against your warehouse and transformation layer — coverage, quality, lineage, entitlement, freshness.
Interviews with owners, consumers, and whoever drives the initiative — to explain the numbers and score what artifacts can't show.
Scorecard, findings, and a dependency-ordered roadmap, walked through with your decision-maker. No pitch.
Read-only access to the warehouse and transformation layer, plus catalog access where one exists — no write permissions at any point. Covered by a mutual confidentiality agreement before any credential changes hands, and revoked at delivery.
No, and that's deliberate. Pricing the assessment on its own merits means I have no financial stake in what the findings say — including when the honest answer is that your data is in better shape than you feared.
The measurement is instrumented rather than interview-led, so findings are reproducible — you get the artifacts and can re-run them 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 sets out what to do about it in sequence. The point of measuring is that the answer can go either way — and knowing early is far cheaper than another two quarters of pilot work.
Work with them, always. The deliverable is written for your engineers to act on, and the roadmap assigns work by role so it can be picked up internally.
I take a small number of engagements at a time rather than stacking them, and I'll tell you 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, entitlement monitoring, security guardrails, and the standards infrastructure that lets agentic systems act on organizational data without producing confident nonsense.
I do that work inside a Fortune 500 technology company, under real compliance pressure. Before that, three years as a senior consultant in AI and data engineering at a Big Four firm, and seven years in federal and municipal oversight.
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.