RAG & Company Memory for Enterprise Teams

The Problem

Company knowledge is distributed across shared drives, wikis, tickets, CRMs, ERPs, and email threads. Classical search returns documents — it does not synthesise, contextualise, or reason across sources.

LLMs without a governance layer produce plausible-sounding answers that may be wrong, outdated, or drawn from data the requesting user is not authorised to access. In regulated environments, or when outputs feed into decisions, neither of those outcomes is acceptable.

What a Production-Ready Knowledge System Requires

Source ingestion and document pipeline Structured extraction from internal sources: documents, databases, wikis, and APIs. Chunking strategies tuned to the content type — not one-size-fits-all. Metadata and access classifications captured at ingest time.

Retrieval and ranking Semantic search, hybrid retrieval, and re-ranking. The retrieval step determines which context reaches the LLM — this is where most quality and consistency problems originate.

Access-aware context assembly Permissions are checked before context is assembled — not delegated to the model. Users retrieve only content they are authorised to see. Access control and data boundary architecture is a first-class concern, not an afterthought.

Source-grounded answers Responses are bounded by retrieved context. The model is instructed not to reason beyond the provided sources. Citations and references are included in the output where the use case requires it.

Human review for high-stakes outputs For outputs that feed into decisions, approvals, or regulated processes, a human review step is part of the architecture. The system surfaces uncertainty rather than suppressing it.

Observability, evals, and cost control Every query, retrieval event, and output is observable. Evaluation harnesses measure answer quality against curated test cases. Cost per query is tracked and steerable. LLMOps architecture is considered from sprint one, not bolted on later.

Who This Is For

Teams where internal knowledge drives decisions but is currently hard to access consistently and reliably:

• Finance and controlling teams querying reports, variances, and policies
• Legal and compliance teams working across contracts, regulations, and internal guidance
• Sales teams preparing proposals and needing fast access to product, pricing, and case knowledge
• Support and operations teams resolving issues against internal documentation
• Executive teams querying structured summaries of internal performance data
• Product and project teams navigating decisions, specifications, and retrospectives

Architecture Principles

Every knowledge system engagement is built on these:

• Retrieval before generation: the LLM reasons only over retrieved context — not over training-data memory
• Access-aware context assembly: document-level permission filtering before any LLM call is made
• Source-grounded answers: responses reference their sources; uncertain answers are flagged, not smoothed over
• Human approval where decisions matter: high-stakes or regulated outputs route through a human step
• Monitoring and evals from sprint one: quality is measured from the first prototype, not retrofitted later
• Prompt-injection-aware design: inputs are validated; retrieved content is treated as untrusted. See AI security and hardening

What This Is Not

• Not a general-purpose chatbot: a knowledge system has a defined corpus, an explicit access model, and quality gates — it is not a free-ranging assistant
• Not just a vector database: the retrieval layer is one component; the system includes ingestion, access control, evaluation harnesses, and operational tooling
• Not “put all data into the LLM”: context-window limits and cost make wholesale ingestion impractical; selective, ranked retrieval is the right approach
• Not an autonomous agent: retrieval-based knowledge systems operate within tight, defined boundaries — not open-ended agent loops

Delivery Model

Discovery and knowledge audit Inventory of internal sources, the access model, user workflows, and quality expectations. Scope is defined and risks are surfaced before any build begins.

Source and access model Data extraction pipeline, chunking strategy, and permission architecture. The foundation every other layer depends on.

Prototype with a limited corpus A working retrieval system against a bounded, representative data set. Quality is measurable before scale is attempted.

Evaluation and review loop Curated test cases, retrieval quality metrics, and answer evaluation. A closed feedback loop between outputs and the retrieval layer.

Production hardening Error handling, rate limits, cost controls, audit logging, and integration with existing authentication infrastructure.

Monitoring and iteration Ongoing observability, regular eval runs, and a clear process for corpus updates and model changes. Aligned with LLM engineering standards.

Related Reference

For the access control architecture underlying permission-aware retrieval, see the data access governance use case.

For design considerations around retrieval and access control, see RAG access control and permission-aware retrieval.

Common Questions

Can this work with our existing document management system? In most cases, yes. Ingestion pipelines can extract content from SharePoint, Confluence, Notion, internal databases, and most systems that expose an API. The complexity depends on the volume, access model, and format diversity of the corpus.

How is it ensured that users only see data they are authorised to access? Permission filtering is applied at the retrieval layer — before any document reaches the LLM context. This is a technical control, not a model-level instruction. It relies on access classifications being maintained and accurate at ingest time.

What happens when the system cannot answer a question? A well-designed knowledge system returns a fallback response when retrieval confidence is low or no relevant context is found. It does not guess or fill gaps with general model knowledge.

How are answer quality and reliability measured? An evaluation harness runs against a set of curated question-answer pairs. Retrieval recall, answer faithfulness, and citation accuracy are tracked as ongoing metrics. Quality benchmarks are agreed before production.

How long does it take to reach a production-ready system? A first working prototype against a bounded corpus can typically be reached in three to four weeks. Production hardening — including access controls, evaluation harnesses, and monitoring — extends the timeline depending on source complexity and compliance requirements. A scoping conversation is the right first step.

Is this tied to a specific LLM provider? No. The architecture is provider-agnostic. Routing between providers, or switching providers, is a supported and planned-for pattern. See LLMOps and cost control for how this is managed operationally.