Enterprise R&DCase study · Multiple markets

A modular RAG framework for enterprise knowledge systems

Duration: 10 months · Team size: 7–10 specialists

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The challenge

Every new engagement required building a custom retrieval-augmented generation implementation from scratch, taking weeks of engineering. Bespoke evaluation methodologies were difficult to defend, knowledge rarely transferred between projects, and senior engineers spent their time on repeat plumbing rather than genuinely novel problems.

What we did

Built a reusable, auditable RAG framework that ingests multiple file formats, adapts across industries, and carries a defensible evaluation methodology built in from the start.

The outcome

Reduced new client onboarding from weeks to hours, provided enterprise auditability through an integrated evaluation layer, and formed the baseline for all subsequent RAG deployments across industries.

Turned weeks of bespoke RAG engineering per engagement into an hours-long, auditable, reusable framework.

Databricks products used
Vector SearchMLflow EvaluationUnity CatalogAI Gateway
Capabilities applied
Reusable RAG frameworkDefensible evaluation methodologyMulti-format ingestionCross-industry adaptability
Technical depth

The framework standardised ingestion, chunking, retrieval and evaluation into configurable modules, so onboarding a new document set or industry became a configuration exercise rather than a rebuild.