RAG development that ships, and stays honest.
Retrieval-augmented generation built for production: grounded answers, enforced citations, and an eval harness that catches regressions before your users do.
Most RAG demos look great and fall apart in production. We build the version that survives contact with real users and measure it on every change.
The full retrieval stack, not just a clever prompt.
Document ingestion and chunking pipelines
Tuned to your content and query patterns, not a default 512-token split.
Vector store setup and retrieval tuning
Pinecone, pgvector, or AWS Bedrock, chosen for your scale and budget.
Reranking and citation enforcement
Every answer traces back to a source, or it doesn't ship.
Hallucination guards and escalation
When the system isn't sure, it says so and hands off cleanly.
The same playbook on every retrieval system we build.
Eval harness before the bot
We build a golden dataset before a production prompt. Every chunking change, model swap, and reranker tweak runs through it.
Soak before scale
The system reads real queries and drafts answers only your team can see. You grade the drafts, and the disagreements drive tuning.
Ship on a canary
Five percent of traffic, then fifty, then full, watching grounding and citation accuracy the whole way.
Built to be queried.
- →Support assistants that answer from your real docs, not the open internet
- →Internal knowledge bots for policies, runbooks, and contracts
- →Product search and Q&A grounded in your catalogue or knowledge base
- →AI chatbot and agent systems that need defensible answers
Model-agnostic, by design.
OpenAI, Anthropic, and Google Gemini for generation. LangChain and LlamaIndex for orchestration. Pinecone, pgvector, or AWS Bedrock for retrieval.
Measured grounding before launch.
uncited answers allowed through citation enforcement
RAG guardrailsoak period before full production traffic
Launch patternchanges checked against a golden eval set
Quality gateretrieval layers tuned: chunking, search, reranking
Retrieval stackRelated field notes and proof.
RAG vs fine-tuning
Which your use case actually needs, and when you need neither.
RAG vs fine-tuning
Which approach your use case actually needs, and how to tell them apart.
Customer support automation
Where grounded retrieval powers deflection, triage, and agent assist across your helpdesk.