RAG Development Development
Retrieval-augmented generation with eval suites and guardrails. Retrieval-augmented generation with evals, citations, and EU hosting options.
Built for teams like yours
Support & ops
Deflect tickets with answers grounded in internal runbooks.
Legal & compliance
Cited answers over policy corpuses with retention controls.
Product teams
In-app search and copilots over your own documentation.
Capabilities included in your build
Chunking & embedding strategy
Domain-tuned chunk sizes and metadata for accurate retrieval.
Hybrid search
Combine vector, keyword, and rerankers for enterprise knowledge bases.
Eval suites & golden sets
Regression tests before every model or index change.
Citation & source grounding
Answers link to approved passages — critical for regulated teams.
EU-hosted vector stores
pgvector, Pinecone EU, or self-hosted options.
Guardrails & PII redaction
Block sensitive leakage in prompts and responses.
How we ship
- 01
Discovery
We start with a structured workshop to map goals, users, constraints, and success metrics.
- 02
Design
Wireframes evolve into interactive prototypes you can test with real users before a line of production code is written.
- 03
Build
Weekly demoable increments, written tests, and code reviews — no surprises at launch.
- 04
Launch
Hardened deployments, observability, and a launch plan covering rollout, comms, and rollback.
- 05
Iterate
Post-launch we track usage, fix friction, and ship improvements on a cadence that fits your roadmap.
Engineered for outcomes, not invoices
- Hallucination rate targets agreed before build starts
- Reindex and rollback without redeploying the whole app
- Compliance-friendly logging of queries and sources
- Works with Confluence, SharePoint, and custom CMS exports
- Optional MLOps retainer for drift and new document types
A modern, proven foundation
We pick boring, battle-tested tools so your platform stays maintainable five years from now.
Common questions
+How long does a RAG pipeline take to build?
8–12 weeks for v1 with evals, admin UI, and one production knowledge source.
+Can RAG run entirely in our VPC?
Yes — embeddings, vector DB, and inference can stay in EU infrastructure you control.
+How do you measure quality?
Golden question sets, precision/recall on retrieval, and human review samples each sprint.
+What document formats do you support?
PDF, HTML, Markdown, tickets, and API-fed content with access control preserved.
+How often should we reindex?
Depends on change rate — typically nightly or webhook-driven on publish events.
Ready to build RAG Development?
Send us a brief — you'll hear back within one business day with next steps.