RAG & Knowledge Systems
Retrieval-augmented AI grounded in your documents
Why this matters
RAG and Knowledge Systems is the work of turning your scattered documentation into an accurate, queryable answer engine. We design retrieval-augmented generation pipelines that ingest your documents, build the right indexing and chunking strategy, and serve cited answers through chat, API, or embedded experiences.
Our engineers build on vector databases like Pinecone, Weaviate, and pgvector, hybrid search with BM25 and re-rankers, and foundation models from Claude and OpenAI. We design for accuracy, citation quality, and freshness, not just first-token speed. You get a system that returns answers your team trusts, with the source documents linked, and a maintenance plan for keeping the knowledge current.
Key benefits
Answers grounded in your sources
Every response is built from your documents and links to the exact passages that support it.
Tuned for accuracy, not speed alone
We design chunking, retrieval, and re-ranking to surface the right passage before the model writes.
Freshness without rebuilds
Incremental ingestion keeps the index current as your documentation changes day to day.
Production-ready architecture
Caching, evaluation, monitoring, and access controls included from the start, not bolted on later.
Services in RAG & Knowledge Systems
7 services available in this group.
Custom RAG System
Retrieval augmented generation systems on your internal data.
We design and build custom RAG systems that let teams query internal documents, policies, and product data through accurate, source-cited AI answers.
Learn moreVector Database Setup (Pinecone, Weaviate, Qdrant, Chroma)
Vector database setup on Pinecone, Weaviate, Qdrant, and Chroma.
We design, deploy, and tune vector databases on Pinecone, Weaviate, Qdrant, and Chroma so AI systems retrieve the right data fast.
Learn moreAI Knowledge Base for Support Teams
An AI-powered knowledge base that helps support agents find accurate answers in seconds.
We build AI knowledge bases that index your support content and surface trusted answers for agents and customers in real time.
Learn moreInternal AI Search & Q&A
A private AI search layer that lets your team ask questions across all internal systems.
We build internal AI search and Q&A systems that unify SharePoint, Drive, Notion, and Slack into one secure natural language interface.
Learn moreDocument Q&A System
A document Q&A system that turns long PDFs and reports into instant answers with citations.
We build document Q&A systems that let teams query contracts, reports, and manuals in plain language and get cited answers in seconds.
Learn moreMultimodal RAG (text + image + video)
A multimodal RAG system that retrieves answers from text, images, and video together.
We build multimodal RAG systems that index text, diagrams, screenshots, and video so users get richer, source-linked answers in one query.
Learn moreGraph RAG Implementation
A graph RAG implementation that connects entities and relationships for deeper, structured answers.
We implement graph RAG systems that combine knowledge graphs with vector retrieval to answer complex, multi-hop questions across connected data.
Learn moreOur approach
Knowledge audit
We inventory your sources, assess quality, and recommend what to ingest, clean up, or retire first.
Pipeline design
We pick the vector store, embedding model, chunking strategy, and retrieval architecture for your data.
Build and evaluate
Engineers build the pipeline and we run evals on real questions to measure accuracy and citation quality.
Deploy and maintain
The system ships behind your auth, with monitoring, freshness jobs, and a process for adding sources.
Frequently asked questions
How is RAG different from fine-tuning a model?
Which vector database should we use?
How do you measure RAG accuracy?
How do you handle sensitive documents?
Want help with RAG & Knowledge Systems?
Book a 30-minute call. We will scope the right path for your goals.