DATA SYSTEMS & RAG PIPELINES
Unlock the full value of unstructured enterprise data with high-throughput RAG pipelines and AST knowledge graph extraction.
Records every warehouse table, document store and repository the system may read, and who signed each one off.
Reads each source on its own schedule and turns records, documents and code into one structured representation.
Holds the extracted entities and the relationships between them, so a question about one record reaches the records around it.
Serves the graph to an agent as retrieval, returning the passages that bear on a question rather than the documents that contain them.
Traces every answer back to the rows and passages that produced it, so a claim an agent makes can be checked against the record.
How it works
Data Intelligence AI by Workwise transforms raw database records, PDFs, and code repositories into zero-drift Model Context Protocol (MCP) server endpoints.
Most of an organisation’s data sits where the system that wrote it left it: rows in the warehouse, contracts and reports in a document store, behaviour in product logs, decisions in the code itself. Each store answers questions about its own contents. None of them answers a question that crosses two.
Data Intelligence reads all of them on a schedule your team sets and extracts one structured representation — the entities each source describes, and the relationships between them. The customer row in the warehouse, the renewal contract in the document store and the thread in the ticket system resolve to a single entity instead of three unrelated records.
External signal joins the same graph. Market feeds, competitor movement, supplier indicators and public data arrive alongside internal records and resolve against the same entities. That is where questions internal data cannot answer begin to have answers: what changed outside the business that explains what changed inside it.
Agents query the graph through retrieval rather than through a dashboard, and they reach it through Model Context Protocol endpoints rather than a bespoke integration per system. Each endpoint publishes one slice of the graph — a source, an entity type, a retrieval — and the extraction pipeline rebuilds that endpoint’s shape whenever the schema underneath it moves, so an agent never queries a field the warehouse has since renamed. That is what zero drift names here: an endpoint describes its sources as they stand, not as they stood when someone first wired it up. An agent asks a question, the index returns the rows and passages that bear on it, and the lineage ledger records which sources produced the reply. A dashboard shows a chart and leaves the reading to you; retrieval hands an agent the evidence and keeps the trail back to it.
Each part runs on its own. A team that needs retrieval over one document store adopts the extraction pipeline and the index, and leaves the graph and the external feeds until a question arrives that needs them.
Capabilities
What it runs on
Engagement shape
Data Intelligence starts as a scoped engagement, not a licence. We agree which sources go in first, what a good answer looks like on each, and how long the first pass runs. The pod builds the extraction and the index against your own data, hands over the graph and the runbook that operates it, and your team takes it from there. Scope grows one source at a time, and you decide on the next one after seeing what the last one returned.
Tell us which decision this product would have to improve to be worth running. We will scope the engagement against that, and nothing else.