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    SERVICE / RETRIEVAL & MEMORY

    RETRIEVAL &MEMORY

    RAG done properly. Embeddings, vector search, and knowledge graphs that ground your models in real, current context to provide answers that cite your data instead of hallucinating around it.

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    Where retrieval quietly goes wrong

    Not in the model. In the pipeline: chunks split mid-thought, embeddings that drift from the source language, a graph that returns a plausible passage nobody can trace back to where it came from, an index that goes stale the day after it's built.

    We build the ingestion and retrieval layer to be inspectable first, then tune it for relevance.

    // EVERY ANSWER, TRACED BACK
    1. DOCUMENTS
    2. TICKET LOG
    3. DATABASE
    4. TRANSCRIPTS
    5. HYBRID INDEXdense + keyword, one store
    6. GROUNDED ANSWERcites ticket log, cites transcripts

    EVERY CLAIM WALKS BACK TO A SOURCE

    // WHAT WE BUILDALL SERVICES →
    Ingestion & chunkingRaw documents parsed and split into chunks that preserve meaning instead of cutting sentences in half.
    Metadata & syncMetadata extracted alongside the text and kept in sync as source documents change, so the index never drifts from what's actually true.
    Embeddings & vector storesEmbedding models chosen and tuned for your domain, backed by a vector store built to serve them at query time.
    Hybrid searchDense and keyword search combined, so an exact term match isn't lost to a similarity score.
    Knowledge graphs & provenanceEntities and relations extracted into a graph that combines with vector retrieval for structured reasoning, with every answer traceable back to its source.
    Retrieval qualityRetrieval evaluated on real queries with freshness windows enforced, so answers stay grounded instead of going stale.
    RAGEmbeddingsVector DBKnowledge graphs
    // ANATOMY OF A RETRIEVAL PIPELINE

    From a pile of documents to a grounded, cited answer.

    IngestSOURCES & DOCS
    Parse PDFs, DBs, APIs, transcripts
    Chunk & embedEMBEDDING MODEL
    Smart splitting, vectorized
    IndexVECTOR + GRAPH
    Hybrid store with metadata
    RetrieveQUERY TIME
    Dense + keyword recall
    RerankRELEVANCE MODEL
    Precision over raw recall
    GroundCITED CONTEXT
    Answer tied to sources
    RETRIEVAL STACK
    PGVECTOR / PINECONEVECTOR STORE
    NEO4JKNOWLEDGE GRAPH
    EMBEDDING MODELSHOSTED OR LOCAL
    RERANKERSPRECISION LAYER
    EVAL HARNESSRECALL & PRECISION
    Discovery sprint

    Not sure it's even an agent problem yet?

    Begin with a fixed-scope discovery sprint. You walk away with a real architecture, a build plan, and an honest read on feasibility. The output is yours to keep, whether or not we build it together.

    Book a discovery sprintSee our work
    // RELATED SERVICES
    Agentic SystemsCloud & InfrastructureFull Product Builds

    Tell us what you're
    building.

    Send the problem, the systems it has to touch, and the deadline. We will tell you what it takes to engineer it, or if it's not a fit we will help guide you to a better solution.

    Start a projectCONTACT@GRIF.AI