RAG Systems
Retrieval-augmented generation that grounds LLMs in your proprietary knowledge - accurate answers from your docs, not hallucinations.
We design ingestion pipelines, chunking strategies, embedding models, and retrieval architectures tuned to your data. Hybrid search, reranking, and citation-backed responses ensure trust.
Who It's For
Is This the Right Fit?
Organizations with proprietary knowledge trapped in documents, wikis, and tickets who need accurate, citation-backed answers.
Deliverables
What You Get
- Document ingestion and chunking pipeline
- Vector store setup (pgvector or managed)
- Hybrid search with reranking
- Citation-backed response UI
- Evaluation framework and accuracy benchmarks
- Cost monitoring and caching layer
Typical Engagement
$25,000 – $60,000
6–10 weeks
Use Cases
Common Applications
- Internal knowledge base search across wikis, tickets, and documents
- Customer-facing product documentation assistants
- Legal and compliance research across document archives
- Sales enablement with instant competitive intelligence
- Multi-tenant SaaS copilots with permission-aware retrieval
FAQ
Frequently Asked Questions
What data sources can you connect?
Wikis, PDFs, tickets, Confluence, SharePoint, databases, and APIs. We design ingestion pipelines tuned to your document types and update frequency.
How do you measure retrieval accuracy?
We build evaluation sets from real queries, benchmark precision and recall, and track citation accuracy before launch. Ongoing monitoring catches drift.
Can RAG respect existing permissions?
Yes. We mirror your access controls at retrieval time so users only see documents they are authorized to access.
Ready to scope rag systems?
Book a discovery call or send a message with your use case.