The problem
The AI roadmap is blocked on data: brittle pipelines, no lineage, no reproducibility, and a retrieval layer that nobody trusts. Every model improvement is gated on plumbing.
The approach
Architect and build the data foundation AI workloads actually need: ingestion, transformation, lineage, and retrieval, with reproducibility and governance designed in rather than bolted on.
Engagement
Architecture engagement, optionally extended into buildout.
What's delivered
- Pipeline architecture with lineage, idempotency, and reproducible builds
- Retrieval/feature layer designed for evaluation and auditability
- Data governance appropriate to your regulatory posture
- Cost-and-scale model so the platform does not surprise you at volume
The outcome
A data platform that stops being the bottleneck on every AI initiative.
In practice
What this looks like.
Data-Platform Readiness for AI Workloads
SampleA short bar for whether a data platform is ready to carry AI workloads rather than only dashboards and analytics.
- Lineage Every record traces to its source, the transform that touched it, and the run that produced it; column-level where it feeds a model. Without it, you cannot explain or reproduce a retrieval result, and 'where did this come from' has no answer.
- Idempotency Re-running an ingest or a backfill converges to the same state instead of duplicating or drifting. Silent duplication turns into skewed embeddings and double-counted signals that are hard to spot downstream.
- Reproducible builds Pinned dependencies, versioned transforms, and a content-addressed dataset so a given index can be rebuilt to the same bytes. Required to debug a regression, defend a decision, or roll an index back cleanly.
- Retrieval evaluation A labeled eval set and an offline harness that scores retrieval quality on every change, separate from the generation model. Without it, a chunking or embedding tweak ships blind and quietly degrades answers no one is measuring.
- Governance Access controls, PII handling, and retention wired into the pipeline and matched to your regulatory posture, not bolted on at the edge. For regulated data, ungoverned ingestion is a breach waiting to surface in retrieval logs.
- Cost/scale model A model of storage, embedding, and query cost as data and traffic grow, with the dominant drivers named up front. Keeps the platform from becoming unaffordable as usage climbs.
Situation. A carrier wants a retrieval-augmented assistant over policy wordings and claims notes, but the data layer underneath it is brittle. Pipelines fail silently, nothing is traceable to source, and the team does not trust what retrieval returns. Every proposed model improvement waits on the plumbing.
Path
- 01 Map the existing pipelines and name the gaps: where lineage breaks, where a re-run duplicates, what cannot be rebuilt.
- 02 Make ingestion idempotent and builds reproducible, so a given index ties back to a known source state and a version.
- 03 Stand up a retrieval eval harness against a labeled set that scores quality on every change before it ships.
- 04 Wire access controls, PII handling, and retention into the pipeline to match the carrier's regulatory posture.
- 05 Build a cost-and-scale model that names the dominant drivers as data volume and query traffic grow.
Shape of outcome. Retrieval results become reproducible and traceable to source, model work stops being gated on the plumbing, and the cost of scaling is known before it lands rather than discovered after.
Representative: illustrates the method, not a specific client.
Think this is your situation?
Request an audit. You'll hear back from the person who'd do the work.