Engineering and enablement
Data Foundation: the data your AI answers from, governed and measured
The data layer an AI system's accuracy in production depends on. We build the path from your existing systems to the answers an application gives, and we measure whether those answers hold up. It is engineered on FORGE, under the same standards and the same record as the application that uses it. What anyone can retrieve is bounded by what they are allowed to see, and anything classified as personal is masked outside production. Your engineers and your auditors read one account of where the data came from, under your governance, consent and retention rules.
An application can be up, answering, and quietly wrong. A row in a lookup table is stale, a value crossed a boundary without its unit, or a retrieval returned the wrong document with confidence. Data Foundation builds and tests the data layer against your access rules and the answers your application has to give. BtSavvy's engineers deliver it on FORGE inside your accounts. Or your data team builds it with our forward-deployed engineer beside them, teaching as they build. Your team keeps the capability to maintain it, for the services your business owns and runs. Lineage is produced as the work happens, so an auditor's question is a lookup. Engage it on its own or alongside Build. We agree who maintains the sources, the quality checks and the retrieval evaluations once the system is running.
What the client gets
Retrieval governed by your access rules. Ingestion from your existing sources, or federated access to data that should not move. That data is reached where it lives. Your security and data owners decide who may search each source. We apply those rules to every retrieval path and test for access across customer boundaries. For example, your policy may let support staff search help articles while contracts stay with the teams entitled to them.
Answers you can measure. Vector search, retrieval-augmented generation (RAG) pipelines, and retrieval-quality measurement for each application. Accuracy is checked against known-good answers. Text read from a document is handled as data, not as instructions, which limits what a prompt-injection attempt can do. A lineage graph records where each value came from, with its unit and currency carried across every boundary. Money and safety calculations get an independent check.
Reference data with an owner. Much of what decides whether software gives the right answer is not in the code. It is a rate card, a product catalogue or a lookup table. Each one gets a contract naming its owner, its authoritative source, its validity checks and how fresh it must be. Anomaly checks flag a suspicious row; the named owner decides, and the decision is on the record.
A corpus with a lifecycle. For products that answer from documents, ingest, replace, delete and re-index are each on the record. When a deletion request has to be closed, the evidence of erasure covers the active store, the indexes and anything derived from them. It covers the archived copies within scope. Changing the embedding model re-indexes the whole corpus as a planned change.
Your data terms, per project. You decide which models may read project data, under agreed retention, training-use and region terms. A field is labelled sensitive and its masking rule set in the same step. There is no gap in which it is logged in the clear. We identify personal data in development fixtures, mask it outside production, and test that the deployed system's logs mask it too.
When to engage
Engage Data Foundation when retrieval quality is degrading in production and nobody can say which upstream data choice caused it. When a new agentic workload needs a data layer it can trust, or the data platform is sound for analytics and not yet for agents. And when the data an AI system needs cannot leave the system it lives in.
Accelerators on this service
Data Foundation engagements draw on tooling we built for the work. It covers agentic ingestion, schema discovery, profiling, lineage mapping, governance workflows, metadata enrichment and data-quality automation. Your data and your intellectual property stay yours.
Adjacent services
Data Foundation pairs with Build: the applications Build ships consume the data it prepares. It runs on the platform Cloud builds, under the policy DevSecOps generates into the pipeline. Assurance reads the lineage directly. AI Strategy & Architecture may scope it as a prerequisite.
Talk to us about the data underneath your AI.
Most engagements begin with the FORGE Diagnostic. For data, it takes one workload you select and builds a working slice of the path from your sources to the application's answers. It is built to your standards, inside your security controls, and deployed where you can watch it run. You keep the demonstration, its evidence and a readiness assessment, with a proposed next step.