RAG & knowledge search services.
Answers from your data, with sources.
Retrieval systems that answer questions from your documents and databases, and show where every answer came from.
The problems we solve first.
Scattered knowledge
Policies, manuals and tickets spread across tools.
Wrong answers
Models invent facts when retrieval is weak.
Permissions
Not every user should see every document.
What you get.
Every engagement includes design, engineering, evaluation, security review and handover from one senior team.
Ingestion pipeline
PDFs, sites, drives and databases indexed.
Chunking & embeddings
Tuned for your content type.
Hybrid search
Keyword and vector search combined.
Cited answers
Every answer links to its source.
Access-aware retrieval
Results filtered by user permissions.
Quality evals
Measured answer accuracy before launch.
RAG & knowledge search, by industry.
How we ship AI that holds up.
Evaluation comes before scale. You see it working on your data before you commit to the full build.
Discover
Pick the task, define what “good” means and collect sample data.
Prototype
A working version on your real data, shown to real users.
Evaluate
Automated tests for accuracy, latency, cost and safety.
Productionise
Security, scaling, integrations and admin controls.
Monitor
Quality, drift and spend tracked; prompts and models improved.
Three ways to start.
AI sprint
One use case, prototyped on your data and tested with users.
You leave with · Prototype + go/no-goPilot
A production-grade version for one team, with evals and monitoring.
You leave with · Live pilot + ROI dataAI team
A dedicated squad that ships and improves AI across your products.
You leave with · Roadmap deliveredQuestions, answered.
Most clients start with a two-week AI sprint. Pilots and production builds are priced after the sprint, once we know what works on your data.
Ready to try rag & knowledge search? Start with a sprint.
Tell us the task. An AI architect replies within one business day with a sprint plan. NDA on request.