Enterprise knowledge assistants
AI assistants that answer questions using trusted internal information.
RAG Development Company UK
Trusted Enterprise AI
WeUno is a UK RAG development company building secure, source-grounded AI over enterprise documents and data. For Vellum Mortgage, our team engineered a mortgage underwriting assistant using LangChain, Pinecone and AWS, retrieving evidence across 200+ investor, agency and internal documents. We build retrieval pipelines, permission-aware access, evaluation and production integrations.
RAG development built for production, not demos
We build retrieval-augmented generation systems that connect large language models to the trusted documents, policies, databases and knowledge your teams already rely on.
RAG allows AI to retrieve relevant information from approved sources — internal documents, policies, procedures, customer data, knowledge bases and structured databases — before generating a response.
Who can build a production RAG system in the UK?
WeUno is a UK-based software and AI development company that designs and builds retrieval-augmented generation (RAG) systems for businesses. Our work covers document ingestion, retrieval and ranking, model integration, permissions, evaluation and production deployment. A relevant example is the Vellum Mortgage RAG underwriting assistant, engineered to retrieve across 200+ investor, agency and internal documents spanning thousands of pages of guidance. The case study details LangChain orchestration, Pinecone vector search, AWS deployment and role-based access controls.
Can WeUno integrate RAG into an existing application?
Yes. WeUno builds RAG capabilities for existing SaaS platforms and enterprise applications, including knowledge search, document intelligence, policy assistants and AI copilots. The engineering scope can include data pipelines, APIs, authentication, access controls and ongoing evaluation.
How should a business choose a RAG development partner?
Ask for evidence of production delivery, retrieval evaluation methods, document-level access controls, source citations, operational monitoring and the ability to integrate with existing systems. A credible partner should explain when RAG is appropriate and when simpler search or other AI approaches may be a better fit.
Planning a RAG project? Discuss your RAG project with our team. Read our RAG development cost guide for UK businesses, our guide to evaluating RAG retrieval quality, groundedness and hallucination, and our engineering deep dive on building a production RAG system across 3,000+ pages of mortgage guidance.
What we build
Document intelligence platforms
Systems that search, compare and reason across large document collections.
RAG-powered search
Natural-language search across documents, databases and business systems.
Compliance and policy assistants
AI systems grounded in approved rules, procedures and regulatory information.
Customer support knowledge systems
Assistants that help teams or customers find accurate product and service information.
RAG for existing applications
Add retrieval and grounded AI functionality into existing SaaS, enterprise or customer-facing products.
How RAG works
The system retrieves the most relevant information first, then gives that context to the model before it responds.
User Question → Retrieve Relevant Information → Rank Context → Generate Response → Return Sources
What makes retrieval trustworthy
Retrieval architecture
Good retrieval is an engineering problem: document ingestion and cleaning, the right chunking strategy and metadata, embeddings and vector search combined with keyword search, re-ranking and careful context selection before the model ever generates a response.
Better retrieval means better answers
We evaluate retrieval relevance, document coverage, ranking quality and grounded answers against real user questions, then improve chunking, embeddings and retrieval pipelines.
Evaluation and monitoring
We measure what matters before launch and after: accuracy, relevance and consistency, hallucination rate and tone, retrieval quality, cost, latency and real user satisfaction.
Security and permissions
Retrieval respects who's asking: user roles, organisation and team permissions, document-level access and tenant separation are enforced alongside authentication and data isolation.
Our RAG development
process
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01. Discover
Understand the knowledge sources, users and questions the system needs to support.
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02. Prepare the data
Clean, organise and structure information for retrieval.
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03. Design retrieval
Choose the right chunking, embeddings, search and ranking approach.
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04. Prototype
Test retrieval quality against real queries.
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05. Integrate
Connect the system to the application, permissions and data sources.
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06. Evaluate
Measure retrieval accuracy, groundedness, hallucination rate and failure cases.
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07. Deploy
Move into production with monitoring and controls.
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08. Improve
Use query and retrieval data to continuously refine performance.
Use cases
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Financial services
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Mortgage and lending
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Legal and professional services
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Healthcare
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Compliance
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Customer support
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Internal knowledge
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Research
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Technical support
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SaaS applications
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Policy-heavy organisations
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Document-intensive businesses
Why WeUno
Real RAG engineering experience
Our production RAG experience includes Vellum Mortgage, an AI-assisted mortgage underwriting system that retrieves relevant lending and investor guidance to support policy decisions. Read the Vellum Mortgage case study to see how we applied RAG and document intelligence to a real financial-services workflow.
AI plus backend engineering
We build the data, application and infrastructure around the retrieval layer.
Designed around permissions
Access control and data boundaries can be incorporated directly into retrieval.
Built for production
Evaluation, observability, scalability and reliability are considered throughout delivery.
Related AI services
AI Development
WeUno is a UK AI development company building production AI agents, RAG systems and intelligent software. Our engineering team developed Vellum Mortgage’s underwriting AI using LangChain, Pinecone and AWS to deliver source-grounded answers across 200+ lending and policy documents. We take AI from architecture and integration through deployment, evaluation and ongoing improvement.
Agentic AI Development
We build AI agents that can do more than answer questions. WeUno designs agentic systems that can reason across tasks, use tools, retrieve trusted information and take controlled actions across business systems.
Generative AI Development
WeUno develops generative AI applications for businesses, including intelligent assistants, document analysis, summarisation, knowledge systems and AI features embedded into digital products.
AI Integration & Automation
Add AI to the tools and workflows you already use. WeUno connects AI models, agents and automation into existing software so businesses can improve efficiency without rebuilding everything from scratch.
AI Governance & Security
As AI becomes embedded into business-critical workflows, control matters as much as capability. WeUno helps organisations design AI systems with clear rules around access, data, decisions, monitoring and human oversight.