Services
RAG systems that make scattered knowledge usable.
Your team is asking the same questions twice a day because the answer lives in a PDF, a Notion page, and a Slack thread. We build the retrieval system that finds it once, cites the source, and stops the repeats.
Talk through the buildThe system
What we build
Document ingestion and chunking strategy
Vector search, hybrid retrieval, and reranking
Source-grounded answers with citations
Permission-aware retrieval for private business data
RAG quality evaluation and monitoring
Built for the work
Why it matters
A useful RAG system is not a chatbot wrapper. It needs clean ingestion, search quality, citations, permissioning, evals, and feedback loops. We build the whole pipeline.
- Find answers across scattered knowledge
- Give teams faster access to policies, contracts, procedures
- Cut repetitive internal questions
A few useful details
Questions about rag systems
What is a RAG system?
A pipeline that connects a language model to your private data so answers are grounded in real documents, not model memory.
Can RAG handle messy documents?
Yes, but quality depends on ingestion, cleaning, metadata, retrieval strategy, and evals. We design the full pipeline.
Open tools
Try it free before you hire us
These free builds from our AI build library show exactly how we approach rag systems. Run one yourself this weekend — when you want it wired into your real stack, that is the engagement.
Next step
Ready to build this?
Book a call and we will map the first high-leverage workflow.
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