RAG SYSTEMS
Retrieval-augmented generation that's accurate, current, and cited. Hybrid search, re-ranking, freshness guarantees, and evaluation pipelines — so your AI answers from your data, not its imagination.
WHAT WE BUILD
Dense vector retrieval combined with BM25 keyword search for recall and precision across structured and unstructured data.
Cross-encoder rerankers surface the most relevant passages, dramatically improving answer quality over naive similarity search.
Every answer is grounded in retrieved sources with inline citations — so users can verify and trust the output.
Incremental ingestion from your docs, databases, and apps with freshness guarantees and change detection.
Smart chunking strategies and embedding pipelines tuned to your content for maximum retrieval quality.
Automated accuracy, relevance, and hallucination evals so you can measure and improve, not guess.
CAPABILITIES
Grounding, guardrails, and answer-verification reduce confident wrong answers to a minimum.
Permission-aware search so users only ever see answers from data they're allowed to access.
Caching, pre-computation, and streaming responses keep the experience fast even over large corpora.
Connectors for Confluence, Notion, Google Drive, S3, databases, and custom internal systems.
Context-window management and model routing keep per-query costs predictable at scale.
Query logs, retrieval traces, and feedback loops so you can see exactly why an answer was given.
PROCESS
Define goals, success metrics, data sources, and constraints. A fixed-price sprint delivers a spec and roadmap you can take anywhere.
WK 1–2System design, evaluation strategy, and infrastructure plan — built for production scale and observability from day one.
WK 2–4Build the ingestion, embedding, retrieval, re-ranking, and generation pipeline, then tune it against a real evaluation set drawn from your data.
WK 4–10Benchmarking, load testing, error handling, monitoring, and cost controls before anything ships to users.
WK 10–12Milestone-gated go-live with monitoring and alerting, then continuous evaluation and improvement as usage grows.
WK 12+TECHNOLOGY STACK
Vector DB
Search
Rerank
LLMs
Frameworks
Eval
USE CASES
Let employees ask questions across wikis, docs, and tickets and get cited answers instantly.
Ground support answers in your help center and product docs to deflect tickets and speed resolution.
Answer regulatory and policy questions with traceable citations to the governing documents.
Synthesise insight across large document sets — contracts, filings, papers — with source grounding.
Turn sprawling docs into a conversational assistant for users and developers.
Specialist assistants for legal, medical, or financial teams grounded in vetted sources.
WHY CHOOSE US
We measure accuracy and hallucination rates — and optimise against them, not vibes.
We've shipped retrieval systems over messy, real-world enterprise data at scale.
Permission-aware retrieval and data handling built for sensitive corpora.
Production latency and uptime, not a fragile demo notebook.
Documented, testable pipelines your team can own and extend.
We keep tuning retrieval and evals as your data and usage evolve.
FAQ
RESOURCES
The model layer that powers retrieval-augmented generation, with cost controls and evals.
Explore service →Agents that combine RAG with tool use to take action, not just answer.
Explore service →A deep dive into building production retrieval-augmented generation systems.
Read the guide →READY TO START?
Tell us what you're building. We'll scope it and give you a fixed price and timeline.