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 can actually support what you're building — and then fixes what can't.
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.
Start with the assessment. It is a real deliverable, priced on its own merits, and it is never credited back against later work — that would make it a sales call rather than an audit.
A measured verdict on whether your data can carry the AI system you intend to build. Seven dimensions, scored on evidence rather than interviews, delivered as a scorecard, a findings register, and a dependency-ordered remediation roadmap your team can start on Monday.
Building what the assessment says needs building. Scoped as a fixed-fee project against the remediation roadmap, so you are buying a defined outcome rather than an open-ended engagement.
A fixed number of days each month for teams that have the systems but not the specialist. Ongoing governance ownership, standards enforcement, pipeline reliability, and someone accountable for the thing you just built continuing to work.
The framework is derived from public sources rather than invented, so you can check the work. Each dimension is scored on a five-level maturity scale, weighted for AI readiness specifically rather than general data health.
Accuracy, completeness, consistency, timeliness, validity, and 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. Coverage measured at model and column level.
Whether any value can be traced 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, and 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 has to intervene 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 are about interpreting evidence rather than collecting 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 is driving the initiative — to explain the numbers and to score governance and entitlement, which artifacts cannot show.
Scorecard, findings, and a dependency-ordered roadmap, walked through with your decision-maker. No pitch in that meeting.
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.
cperez@schemavita.com