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Data · Institutional Memory LayerYour documents, systems and tribal know-how, made retrieval-ready with RAG and knowledge graphs: one governed memory layer that people and agents query alike, compounding with every project and surviving every departure.
An institutional memory layer is a governed, retrieval-ready store of your organisation’s knowledge (documents, data, decisions and tribal know-how) that AI systems query using Retrieval-Augmented Generation (RAG).
Built properly, it rests on four disciplines: governed retrieval that respects your permissions model; structure-aware indexing along real semantics with entities linked in a knowledge graph; citation-first answers where every claim references its source; and continuous evals that measure retrieval quality on real user questions. It is the substrate the other three pillars stand on: agents draw context from it, chatbots ground answers in it, and ambient BI cites it.
How the memory layer is built
Map the corpus and its permissions: what exists, who may see it, what matters most.
Structure-aware chunking with entities linked in a knowledge graph.
Permission-aware retrieval enforcing the same access rules as your systems.
Citation-first responses: every claim references its source passage.
Faithfulness and coverage measured on real questions as the corpus grows.
A retrieval pipeline over your highest-value corpus that enforces the same permissions as your systems of record. Designed in, not bolted on.
Documents chunked along their real semantics (clauses, procedures, tables) with entities linked so the system knows your assets by every name they carry.
Every response carries references back to source passages, with an eval harness measuring faithfulness and coverage as the corpus grows.
One Forward Deployed Engineer: a senior engineer embedded in your team, accountable for a memory layer you can evaluate within weeks.
The first weeks follow a consistent arc: audit the corpus and its permissions, stand up governed retrieval over the highest-value slice, wire citation-first answering, and put evals around the loop before widening. Databricks Lakehouse, Medallion architecture and Unity Catalog governance sit at the core, deployable in cloud or fully air-gapped environments.
No bench. No handoffs. One Forward Deployed Engineer: in your team, on the hook for the outcome.
Let’s TalkRead more: RAG done right: building your institutional memory layer →