Data Foundations

What it is

Data Foundations gives your business one trustworthy set of data every system can read. Today the same information usually lives in three or four places: a warehouse for analytics, a separate copy for AI experiments, a few spreadsheets nobody owns, and the original systems where the data was created. The copies drift, the numbers disagree, and nobody can say which one is right. We replace that with a single governed substrate and the operating discipline that keeps it fresh, owned, and access-controlled. Once it exists, the next AI use case stops needing its own data pipeline.

Why it matters

AI is only as honest as the data underneath it. An agent that retrieves from a stale document, cites a number that contradicts the dashboard, or reads a table the requesting user cannot see is a data substrate problem, not a model problem. AI pilots most often stall on the way to production because the team has no clean, governed source to point the agent at. The pilot succeeds on a curated demo and breaks on real operations. Data Foundations closes that gap.

The umbrella, and its two sub-services

Data Foundations is the strategic umbrella. Two delivery surfaces sit underneath it. Data Lake & Lakehouse is the storage and ingestion substrate: open-format tables, streaming pipelines, and contracts that keep data honest as it lands. LLM-Ready Knowledge Base is the governed corpus layer: chunked, embedded, permission-aware retrieval that agents read from. A common pattern is to build both. The lakehouse holds structured truth, the knowledge base holds documents, and they share lineage, ownership, and access controls so an agent or analyst points at one substrate.

• Source graph: who owns what, where it flows, who depends on it
• Ingestion contracts, freshness SLOs, named owners
• Column-level lineage and automated impact analysis
• Retrieval-ready chunks, embeddings, and permission propagation
• Two sub-services: Data Lake & Lakehouse, LLM-Ready Knowledge Base

How it works

Each engagement starts with an inventory. We walk the operational systems already in flight (warehouses, OLTP databases, document stores, ticketing, code repos, observability) and produce a source graph: a map of where information lives, who owns it, and which downstream consumers depend on it. From there we collapse duplicate ingestion paths, write source contracts (input shape, freshness target, owner, error budget), and decide which sub-services to stand up first. Quality gates run at ingestion. Failed records go to a dead-letter table with a notification to the source owner. Retrieval-ready transformations sit alongside analytical tables, so one source of truth powers both the BI dashboard and the agent's citation.

What it works with

Data Foundations is the bottom of the stack. The AI Platform points its model gateway, retrieval pipelines, and MCP tool registry at it. Agent Workflows use it to gather context before they act, and write outcomes back. Conversation Intelligence lands captured threads in it so signals stay governed. Closed-Loop Knowledge updates flow back as new source-graph nodes and refreshed indexes. Workflow Evals build gold and regression sets from traces stored here. If any layer above is going to be reliable, this one has to exist first.

Where we draw the line

This is an operating discipline, not a one-time migration: source ownership, freshness contracts, and quality gates we set up and then transfer to your team. Open table formats and a model-agnostic catalog let you swap compute engines without rewriting the substrate (engine swaps still take adaptation work). The same foundations serve finance reporting, operations dashboards, and analyst self-service — the AI use cases are simply the ones that surface failures first.

When you should start

Signals: a number on the executive dashboard regularly disagrees with the underlying system; AI pilots succeed on demo data and stall on production; new AI use cases each require a net-new pipeline because no governed substrate exists; data quality issues are diagnosed by Slack archaeology; a regulator or customer asks who can read which data and the answer is 'we'll check'. Common starting points: stand up the lakehouse sub-service so analytics and AI share a source of truth, build the knowledge-base sub-service so unstructured content joins the same governance, or run a 4-week source-graph diagnostic that maps the current state before any consolidation.