Clear, owned outputs.
- RAG architecture
- Ingestion pipelines with refresh schedules
- Search index and retrieval configuration
- Assistant interface or API
- Evaluation set and results
Retrieval-augmented generation lets a language model answer questions using your documents and data. The system retrieves the most relevant passages and asks the model to answer from them, with citations. Getting it right depends on ingestion, chunking, retrieval quality, permissions and evaluation — which is where we focus.
The problems Retrieval-Augmented Generation solves.
Ingestion, vector and hybrid search, permission-aware answers with citations.
Answers cite the wrong document or invent details.
Users can see content they shouldn't have access to.
Retrieval misses relevant documents because of wording differences.
Content changes, but the assistant's knowledge doesn't.
What Retrieval-Augmented Generation covers.
Content ingestion
Loading documents, pages and records with metadata.
Chunking and embeddings
Splitting and indexing content for accurate retrieval.
Hybrid retrieval
Combining semantic and keyword search, with re-ranking.
Permission awareness
Respecting existing access controls on content.
Citations
Showing sources so users can verify answers.
Evaluation
Measuring retrieval and answer quality on real questions.
How a RAG system answers a question.
Retrieval-augmented generation lets a language model answer from your own documents, with citations. Answer quality depends mostly on the retrieval steps, so that's where we spend most of the effort.
- 01
Ingest
Documents are collected from SharePoint, Confluence, drives or databases, cleaned and kept in sync, with permissions carried across.
- 02
Chunk and embed
Text is split into passages that keep their meaning — by section, not fixed length — and turned into vector embeddings.
- 03
Retrieve
The question is matched against passages using hybrid search (vectors plus keywords), then the best candidates are re-ranked.
- 04
Generate
The model writes an answer using only the retrieved passages, with citations, and says so when the documents don't cover the question.
- 05
Evaluate
A test set of real questions checks retrieval hit rate, answer faithfulness and usefulness after every change.
Why it's worth it.
Verifiable answers
Citations let people check the source.
Current knowledge
Content updates flow into answers.
Respect for access
Users only see what they're allowed to.
When Retrieval-Augmented Generation is the right move.
- An internal knowledge assistant for policies and procedures.
- A support assistant using product documentation.
- Research across contracts, reports or case files.
Technologies & platforms
- Azure AI Search
- Elasticsearch
- OpenSearch
- pgvector
- Pinecone
- Weaviate
- LlamaIndex
- LangChain
- SharePoint
- Confluence
How Retrieval-Augmented Generation work runs.
- 01
Scope
The task, users, content sources, risks and how success will be judged.
- 02
Prototype
A working prototype on real examples, reviewed by the people who will use it.
- 03
Evaluate
Test sets for accuracy, safety and edge cases, with human review.
- 04
Harden
Security, permissions, guardrails, logging and cost controls.
- 05
Launch and improve
Phased rollout, feedback loops and regular evaluation.
Retrieval-Augmented Generation questions.
Why not just put all our documents into the model?
Context windows are limited and costly, and models answer more reliably from a few relevant passages. Retrieval also allows permissions and citations.
Does RAG work with SharePoint and Confluence?
Yes. Connectors can ingest content from SharePoint, Confluence, file shares, databases and websites, with metadata and permissions.
How do you measure if RAG is good?
With test questions and expected sources, measuring whether the right passages are retrieved and whether answers are correct and faithful to them.
Want an assistant that answers from your documents?
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.