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.
Runtime
How this service ships
Production gates for this engagement - not a slide-deck architecture.
retrieval_stack
cite · eval harness
01
Ingest
02
Chunk
03
Retrieve
04
Cite
05
Evaluate
- ✓ Permission-aware corpus
- ✓ Citation required
- ✓ Eval before scale
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
deliverables
6 items
- 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
engagement
typical
price_band
$25,000 - $60,000
duration
6-10 weeks
Use Cases
Common Applications
use_cases
common patterns
- 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 scoping call or send a message with your use case.