Retrieval-Augmented Generation

RAG Development Services

PixoBots builds RAG systems that let AI answer from your own documents, wikis and databases - accurately and with citations - instead of guessing. Enterprise knowledge assistants, internal search and customer support that stay grounded in your data.

Our RAG development capabilities

  • check_circle Document ingestion, parsing & chunking
  • check_circle Vector search & hybrid retrieval
  • check_circle Grounded answers with source citations
  • check_circle Access control & data privacy
  • check_circle Evaluation & hallucination checks
  • check_circle Claude, GPT & open-model support
  • check_circle Integration with your data sources
  • check_circle Monitoring, feedback & continuous tuning

What we deliver

Knowledge assistants

Staff and customers get answers from your own content, instantly.

Grounded search

Semantic search across docs, tickets and databases - not just keywords.

Citations & trust

Every answer links back to its source, so people can verify it.

Secure & private

Role-based access and private deployment keep sensitive data protected.

Explore related services

Ground your AI in your data

Tell us about your goals and we'll get back to you within 24 hours.

Frequently asked questions

What is RAG (retrieval-augmented generation)? expand_more
RAG is a technique where an AI model retrieves relevant passages from your own documents and data at answer time, then generates a response grounded in them - so answers are accurate, current and cited instead of made up.
Why choose RAG over fine-tuning? expand_more
RAG is faster, cheaper and easier to keep current - you update the knowledge base, not the model. Fine-tuning changes style or behaviour; RAG supplies facts. Many systems use both, and we advise on the right mix.
Does RAG keep our data private? expand_more
Yes. We deploy with access controls, encryption and private or self-hosted options so your documents never leak, and users only retrieve content they are permitted to see.
How do you prevent hallucinations? expand_more
We combine strong retrieval, citation of sources, answer-grounding checks, evaluation datasets and fallbacks so the assistant says 'I don't know' rather than inventing - and we monitor quality over time.