Cross-industry engineering experience across regulated and high-growth sectors.
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“Through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.” — Gartner
“Two-thirds (67%) of organizations have been unable to transition even half of their GenAI pilots to production.” — Informatica
Maps what your data supports today, what is missing, and what to fix first, against the one AI use case you intend to ship. You receive a gap register rating every issue as blocking, degrading, or acceptable, a verdict on whether your current platform can carry the use case, and an ordered plan the following services execute.
Pipelines and ingestion that land source data at the latency your use case sets, whether that is a nightly batch or a change data capture stream measured in seconds. Every pipeline ships with a contract, a monitored SLO, and a rollback path, so the model consuming it never inherits silent failures from the systems upstream.
Per-field completeness and accuracy measurement, remediation, and the thresholds that keep quality from regressing after the first fix. We profile the tables your use case reads, repair only the gaps that block or degrade it, and leave you with automated checks that fail a build before bad data reaches a model in production.
Ownership, policies, and access control for AI-ready governed data, applied to human and machine consumers. Each critical dataset gets a named owner, a documented policy, and an access record, so a regulated field can be traced to who approved its use, by which system, and for which decision, without a manual audit trail assembled after the fact.
Lineage, anomaly detection, and SLO monitoring, so readiness is watched continuously instead of assumed. Source systems change, schemas drift, and volumes spike, and every one of those events can degrade a model quietly. Observability catches the breach at the contract, alerts the dataset owner, and pauses the consumer before a wrong decision leaves the building.
Labeled outcomes and annotated records that give supervised models the ground truth they train against. We define labeling guidelines with your domain experts, build the workflows and quality checks that keep labels consistent across annotators, and version the resulting datasets, so each retraining cycle starts from a known baseline rather than a spreadsheet of unknown origin.
AI-ready data analytics: agreed metric definitions and reporting on the same datasets your models use. When dashboards and models read from one curated layer, finance, operations, and data science stop reconciling three versions of last quarter. Analysts get a semantic layer with definitions they can query, and models get inputs that match what leadership already sees.
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TRAI-ready data is data prepared so a specific AI use case can consume it reliably: complete and accurate enough for the decision it supports, described by metadata a model can interpret, refreshed at the latency the use case requires, and traceable to its source. The test is always the use case, never an abstract standard.
Six checkable attributes: fit for the specific use case, completeness and accuracy above the threshold the decision needs, metadata and business semantics that make fields interpretable without tribal knowledge, freshness matched to decision speed, governance and lineage that establish ownership, and machine-actionable access through APIs rather than exported reports. Every attribute has a test, not an adjective.
An AI data readiness assessment inventories the data your use case needs, profiles what actually exists, and rates each gap as blocking, degrading, or acceptable for now. The output is an ordered map of what is usable today and what to fix first, not a single score.
Work backwards from one use case: define the data and confidence it needs, inventory and profile what exists, close only the gaps that block or degrade it, set freshness, completeness, and accuracy thresholds with a named owner per dataset, and expose the result as data products with contracts. Then monitor, because readiness decays as source systems change.
No, though quality is part of it: data quality asks whether the data is correct, while AI readiness asks whether it is sufficient for the decision a model will make. Correct data can still lack the patterns, semantics, freshness, or lineage that a particular use case depends on.
Unstructured content becomes AI-ready through a different path than tables, but the requirements rhyme: it must be discoverable and deduplicated, tagged with source, date, ownership, and sensitivity, chunked in a way that preserves meaning, and access-governed so retrieval never surfaces what a user should not see. In practice, the failure mode is usually access control, not embedding quality.
Usually not, because most readiness gaps are semantic and organizational: missing definitions, unclear ownership, unmeasured thresholds, and no contract between producer and consumer, all fixable on the platform you already run. A platform change is justified only when the current one cannot meet the latency or scale the use case requires, which the assessment establishes before anyone signs a license.
Agent-ready data is the step past AI-ready: data an autonomous system can not only read but act on, which adds callable interfaces instead of files, contracts guaranteeing shape and availability, permission models for non-human callers, and audit trails linking data to actions. Most organizations reach AI-ready first and agent-ready later.
