Enterprise Search & AI Data Access
A retrieval architecture combining enterprise documents, semantic and vector search, and governed structured data so AI assistants can answer using trusted information.
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System overview
This retrieval architecture connected two different kinds of information: enterprise documents and governed structured data. Semantic and vector search provided a path into unstructured content, while data interfaces provided access to operational information that should not be answered from model memory alone.
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My contribution
I worked on ingestion, embeddings, retrieval, structured-data access, API integration, MCP-style tool access, grounding and validation.
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Engineering challenge
Enterprise questions often require both documents and structured business data. Documents are suited to semantic or vector retrieval, while operational data often needs governed APIs or structured queries. Treating every source as the same kind of search problem can obscure the differences in freshness, access boundaries and the evidence needed for an answer.
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Architecture and approach
The document path runs through ingestion, chunking and embeddings into a vector or search index. A separate structured-data path uses governed data APIs, REST, GraphQL or MCP-style tool interfaces. Both paths meet at orchestration, which supplies relevant information to a language model for a grounded response. These are generic paths, not internal data schemas or tool definitions.
Generic flow · illustrative only - Documents → ingestion → chunking / embeddings → vector / search index
- Structured data → governed APIs / REST / GraphQL / MCP
- Both paths → orchestration → language model → grounded response
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Engineering priorities
Retrieval quality is not only about finding similar text. It also involves selecting the right source, validating the returned information and respecting the boundary of each data interface. Tool use should make access explicit rather than turn a language model into an unrestricted database client.
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Lessons and takeaways
Structured and unstructured information complement each other, but they should not lose their distinct access patterns. A grounded response is easier to assess when the retrieval path and the data-access contract are clear. Validation belongs around those interfaces as well as around the final generated answer.