RAG Development Services for Enterprise Knowledge
We build retrieval-augmented generation (RAG) systems that answer from your own documents and data, with citations your team can check and accuracy measured against benchmarks agreed with you.
Why Generic AI Gets Your Business Wrong
Language models know a lot about the world and nothing about your company.
No company knowledge
General models cannot see your policies, contracts, products or history.
Confident mistakes
Without grounding, models can produce plausible but wrong answers.
Scattered information
Knowledge sits across SharePoint, drives, CRM, ERP and email.
Access rules
Not everyone should see everything, and AI must respect that.
What We Build
RAG pipelines that find the right information, respect permissions and show their sources.
Knowledge assistants
Employees ask in plain language and get answers with links to sources.
Document Q&A
Search and compare contracts, policies and reports.
Agentic RAG
Agents that act on retrieved information, with human approval where needed.
Ingestion pipelines
Connectors that keep content current and remove outdated versions.
Permission-aware retrieval
Answers drawn only from content the user may see.
Evaluation
Test sets and benchmarks that measure answer accuracy over time.
How It Works
Identify questions and sources
Agree the questions to answer and the approved sources.
Prepare content
Clean, chunk and index content with metadata and permissions.
Build retrieval
Tune search, embeddings and reranking on your own data.
Evaluate
Measure accuracy and hallucination rates against agreed benchmarks.
Deploy and monitor
Release in stages and track quality in production.
Where People Stay in Control
Sources always shown
Every answer links to the documents it used.
Content owners decide
Owners approve which sources are included.
Permissions respected
Users only see answers from content they may access.
Low-confidence handling
Uncertain answers are flagged rather than guessed.
What We Measure
Measures are agreed during the readiness diagnostic and reported against your baseline.
Answer accuracy
Measured against a test set agreed with you.
Hallucination rate
Answers not supported by sources.
Time to answer
How long people spend finding information.
Adoption
How often teams use the assistant and rate its answers.
Frequently Asked Questions
What is RAG?
Retrieval-augmented generation: the system searches your content for relevant passages and gives them to the model, so answers are grounded in your sources.
How is it different from fine-tuning?
Fine-tuning changes how a model behaves. RAG gives it a searchable library that can be updated without retraining.
Can it respect our permissions?
Yes. Retrieval can filter content by the permissions of the person asking.
Which vector databases do you use?
We choose per project, for example Pinecone, Qdrant, Weaviate or MongoDB, based on your data, hosting and security needs.
Related
AI Company Knowledge Agent (use case)
An illustrative example of a permission-aware knowledge assistant.
Start With a Free 30-Minute AI Opportunity Review
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