Retrieval-augmented AI you can actually ship.
Production RAG systems that retrieve from your data, cite sources and admit when they don't know. Built on LangChain, LlamaIndex or RAGFlow — whichever fits the problem.
- Citations by default
- Evaluated, not guessed
- Privacy-aware deployments
- Cost-aware architectures
Concrete outcomes, not buzzwords
Document ingestion
PDFs, web pages, SaaS sources, support tickets, code — normalised, cleaned, version-aware.
Semantic chunking & embeddings
Chunking that respects document structure — sections, tables, code — not fixed-size cuts.
Hybrid retrieval & re-ranking
Vector + keyword + cross-encoder re-ranking, with metadata filters for precision.
Cited answers
Every claim links back to its source so experts can verify in one click and trust the system.
Evaluation harness
Real question sets, known-good answers and regression tracking on every prompt or model change.
Abstention behaviour
When retrieval can't support an answer, the system says so — and that's the feature that earns trust.
What we work with
Frameworks
Models
Vector stores
Re-rankers & embeddings
Deployment
A deliberate sequence
Corpus audit
We map what content you have, what's authoritative, what's stale — before any embedding work.
Pipeline & retrieval
Ingestion, chunking and retrieval, validated against a real evaluation set early.
Answer layer
Grounded synthesis with inline citations and explicit abstention when confidence is low.
Evaluate, observe, improve
Every change runs against the eval set. Drift is caught before users see it.
Honest about cost and scope
A focused RAG MVP is usually 4–8 weeks; production hardening adds time depending on corpus size and compliance scope. We send a written, costed proposal after a corpus audit.
A taste of what this looks like in production
Production RAG Assistant Over a 12-Year Knowledge Base
Deflected 42% of tier-1 support tickets with cited, accurate answers.
Read case studyQuestions buyers usually ask us
LangChain vs LlamaIndex vs RAGFlow — which do you use?
All three, depending on the problem. LangChain when orchestration and tool use dominate; LlamaIndex when retrieval ergonomics matter most; RAGFlow when document understanding (tables, layouts, OCR-heavy PDFs) is the hard part. We pick on the problem, not on preference.
How do you handle stale or contradictory content?
We classify sources by authority and recency at ingest, exclude superseded material explicitly, and filter by version at retrieval. Letting embeddings 'sort it out' is how RAG systems quietly start lying.
Can it run on-prem for sensitive data?
Yes. We've deployed RAG with open-source models (Llama, Mistral, Qwen) on customer infrastructure, inside VPCs, or with region-pinned managed services. Privacy posture should drive deployment choice, not the other way round.
How do you measure quality?
Separate retrieval and generation scores against a curated evaluation set — so when a change degrades quality we can see exactly where. Without that, RAG quality drifts silently.
What about cost and latency?
Aggressive caching of retrievals, embedding-only updates instead of full re-indexing, model routing for cheap vs expensive paths, and re-ranker pruning. Production RAG we've shipped runs at a few cents per query, with sub-second answers.
Can you integrate with our existing chatbot or product?
Yes — most of our RAG work plugs into existing surfaces (in-app chat, help centre, agent console). We build the engine; you keep the UX.
Ready to start?
Tell us about your project. We respond within one business day.