How to start a data assessment for your company
We use the assessment to blueprint a native AI data platform—one estate where analytics, operational reporting, and agents share the same curated data, lineage, access rules, and retrieval surfaces—without a fragile RAG add-on divorced from your governed warehouse or lake.
Below is what we will do with you to turn that intent into an actionable platform roadmap: contracts your models and people can both trust, and operations your security team can sign off on.
What we will do to make it a native AI data platform
Each step produces concrete artifacts—use-case boundaries, data contracts, and a phased build plan—so engineering can execute without re-litigating strategy every Friday.
Assessment process
Anchor on AI outcomes and trust
We workshop the agentic and analytic experiences you want—RAG over internal docs, SQL copilots, workflow automation, or operational alerts—and define non-negotiables: PII handling, residency, human-in-the-loop, and evaluation signals. That becomes the north star for the platform shape.
Map the data plane for humans and machines
We inventory sources, batch and streaming paths, lake and warehouse zones, catalogs, and IAM. We pressure-test whether today’s layout can serve both BI and embeddings—where duplication blocks lineage, and where a single governed path can feed dashboards and retrieval.
Design semantic and retrieval contracts
We specify logical entities, metric definitions, chunking boundaries, and embedding refresh policies tied to your gold-layer truth—not ad hoc copies. You get a first cut of what “approved context” means for agents: tool boundaries, citation surfaces, and refresh SLAs.
Close gaps toward platform-native AI
We rank gaps: data quality gates, metadata sprawl, vector storage next to the lake, latency for online features, observability for prompts and pipelines, and MLOps or evaluation hooks. Each item ties to a business outcome and a realistic sequence so security and platform teams can sequence work.
Roadmap the native platform build
You receive a 30–90 day execution plan—quick wins (catalog, critical pipelines, pilot vector path) and foundation work (policy-as-code access, lineage for AI outputs, runbooks for embedding jobs). We align this with public or private cloud and lakehouse choices you already committed to or are evaluating.
What we need from you
- Executive sponsor and one coordinator for workshops and follow-ups.
- Owners for analytics, data engineering, security, and at least one prioritized AI use case.
- Existing architecture notes or inventories—rough diagrams are enough to start.
- Clarity on compliance boundaries and what can be reviewed under NDA (often read-only).
What your company receives
- A native AI data platform snapshot: current flows, trust boundaries, and where agents would pull context today.
- Prioritized backlog linking data work to AI outcomes (not a generic maturity PDF).
- Reference cuts for lake/warehouse/vector alignment, catalog, and evaluation observability.
- Phased roadmap and executive readout; formal SOW only if you choose implementation.
Share your industry, regions, and one AI scenario you care about—we'll propose a focused assessment window and kickoff agenda.
Schedule assessment kickoff