Work / AI use case
Find the answer.
Verify the source.
Transform static enterprise documents into conversational, source-grounded knowledge using retrieval-augmented generation.
- Practice
- Applied AI
- Pattern
- Enterprise RAG
- Deployment
- Cloud / On-premise
The challenge
Knowledge exists. Finding it still takes too long.
Business users can spend significant time searching business requirements, functional specifications, procedures and policy documents. Important knowledge remains distributed across unstructured files, slowing decisions and increasing dependence on subject-matter experts for repeat questions.
Solution pattern
Conversational access.
Grounded in enterprise context.
The platform allows authorised users to upload or connect approved documents, ask natural-language questions, generate summaries and receive concise responses grounded in retrieved source material.
- 01
Ingest
Accept approved PDF, Word, text and other enterprise document formats.
- 02
Structure
Parse, clean and divide content into useful, traceable knowledge units.
- 03
Represent
Create semantic embeddings that preserve meaning beyond exact keywords.
- 04
Retrieve
Find the most relevant source passages for each natural-language question.
- 05
Generate
Produce a concise response constrained by the retrieved enterprise context.
- 06
Verify
Return source references so people can review evidence before acting.
Designed for trust
Retrieval before generation.
The system finds relevant enterprise passages first, then constrains the model response to that context. Source references keep the answer reviewable and allow users to return to the underlying document.
Access, document scope, model selection, retention and deployment boundaries are defined around the organisation's security requirements.
Reference architecture
Composable technology.
Controlled as one system.
The source implementation uses a modular architecture. Components can be selected or substituted to fit enterprise standards without changing the core retrieval pattern.
- Language models
- Groq, OpenAI or Azure OpenAI selected around deployment, governance and performance requirements.
- Orchestration
- LangChain or an equivalent governed orchestration layer for ingestion, retrieval and response workflows.
- Embeddings
- Hugging Face or approved enterprise embedding models appropriate to the language and document domain.
- Vector search
- FAISS for focused deployments, with alternative managed stores available where scale and controls require them.
- Application layer
- Python services with an enterprise web interface, access controls, monitoring and integration points.
Operational value
Make enterprise knowledge
easier to use responsibly.
Faster retrieval
Reduce the effort required to locate relevant information across large document collections.
Consistent onboarding
Give new team members a governed route into policies, procedures and established knowledge.
Grounded responses
Connect each answer to retrieved source material and reduce unsupported model output.
Less SME dependency
Resolve repeat questions through approved knowledge while preserving escalation to accountable experts.
Key differentiators
Enterprise knowledge without the black box.
- Support for PDF, Word, text and additional approved formats
- Multilingual semantic retrieval across enterprise knowledge
- Source references that make answers reviewable
- Secure RAG architecture shaped around access and data boundaries
- Cloud, private-cloud or on-premise deployment patterns
- Human escalation for questions that require accountable expertise
Explore the broader AI, XR and integrated XRAI delivery portfolio.
Return to all workDocument intelligence
Turn static files into
usable knowledge.
Bring a representative document set, user group and security context. We will define the retrieval, governance and deployment pattern required for a credible first release.
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