RAG Knowledge Assistants

Make business knowledge easier to find, with answers grounded in your sources.

A retrieval-augmented generation (RAG) system retrieves relevant passages from approved information sources and provides them as context to a language model. The design should account for document quality, access permissions, source freshness, and how people verify answers.

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Business problem

Start with the workflow, not the model.

Policies, operating procedures, product information, and technical documents can be spread across repositories. Keyword search may not reflect how a person phrases a question, while a general-purpose model does not automatically know private or current company information.

Who it can help

Support, operations, engineering, and internal enablement teams that need to find information across a defined set of business documents.

Practical use cases

  • Search internal policies and procedures using natural-language questions.
  • Help support teams locate relevant product or troubleshooting documentation.
  • Summarize selected documents with links or references to the source material.
  • Provide an internal interface over approved manuals, guides, or knowledge bases.

Implementation approach

A practical path from requirements to a working system.

  1. 01

    Inventory source documents, owners, access rules, update frequency, and sensitive data.

  2. 02

    Build ingestion and parsing steps, then prepare searchable text and metadata.

  3. 03

    Retrieve relevant passages with permissions applied and pass selected context to the model.

  4. 04

    Evaluate retrieval and answers on representative questions; review failures and refine the system.

Architecture and deliverables

  • Connectors and scheduled or event-based ingestion for approved sources.
  • Parsing, chunking, metadata, and embedding or search indexing components.
  • A retrieval layer with access checks, relevance controls, and source references.
  • An application/API layer, model integration, feedback path, and operational monitoring.

Integrations to consider

  • Document repositories such as SharePoint, Google Drive, or Notion, subject to supported APIs and permissions.
  • Internal applications and identity providers where access can be authorized.
  • Vector stores, search services, and model providers selected for the project constraints.

Security and evaluation

  • Preserve source permissions during retrieval and define how revoked access is reflected in indexes.
  • Set retention, encryption, logging, and model data-handling requirements before selecting providers.
  • Measure retrieval relevance and answer grounding with approved test questions and expected sources.

Limitations and dependencies

  • Answers depend on source quality, ingestion freshness, retrieval behavior, and model interpretation.
  • RAG does not guarantee correctness; critical answers need source review and appropriate human decision-making.
  • Complex tables, scanned documents, conflicting sources, and fine-grained permissions may require additional processing and evaluation.

Engineering context

Technology choices follow the requirements.

Review the broader architecture and technology-selection approach before choosing an implementation path.

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Project consultation

Discuss your requirements with our team.

Share the workflow, systems, data constraints, and outcome you are considering. We can discuss a suitable scope and next steps.

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