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Data Foundation

How context reaches the model: the source repositories, the ingestion pipeline that makes them searchable, the vector store, and the knowledge graph that maps the relationships between them.

Architecture diagram of a data foundation: ingestion and ETL pipeline, vector search platform, enterprise knowledge graph, and runtime grounding delivery.

Components

  • Data Lakes & Warehouses

    Grounding is only as good as its sources; fragmented or stale repositories produce confident, wrong answers.

    • Structured transaction databases for audited records — balances, order status
    • Unstructured document stores for PDFs, contracts, transcripts, and decks
    • Consolidated log archives of tickets and history for context

    A single, current source of truth for retrieval, so the rest of the pipeline has something solid to stand on.

  • ETL & Data Pipelines

    Raw PDFs dumped into a vector store return fragmented, useless matches.

    • Document parsing that keeps text, tables, and layout context intact
    • Semantic chunking on meaning rather than a fixed character count
    • Metadata tagging — author, department, date — to enable filtered search

    Documents turned into retrievable, well-tagged context, and kept fresh as sources change.

  • Vector Databases

    Keyword search matches words, not meaning, and misses documents that say the same thing differently.

    • Approximate-nearest-neighbour indexes (HNSW, IVF-PQ) for fast similarity over millions of chunks
    • Metadata pre-filtering to scope a search by department or date before ranking
    • Retrieval kept inside a tight latency budget so the query pipeline stays responsive

    Context retrieved by meaning, which is what decides whether a grounded answer is the right one.

  • Enterprise Knowledge Graph

    Vector search finds isolated passages; multi-hop questions need the relationships between them.

    • Entity-relationship mapping across customers, projects, products, and sites
    • GraphRAG: combine vector similarity with graph queries for relational context
    • Relationship pathways supplied to the model for reasoning over the network

    Answers to connected, multi-hop questions that passage retrieval alone can't assemble.