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. The retrieval goes fuzzy, the model invents policy, and nobody notices until a customer screenshots a wrong answer. We build the version that survives contact with real users — and we measure it on every change so it stays that way.
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.
An evaluation harness
A golden dataset that grades resolution, safety, tone, and grounding on every change.
The same playbook on every retrieval system we build.
Eval harness before the bot
We build a golden dataset before a single production prompt. Every chunking change, model swap, and reranker tweak runs through it. Regressions block the merge.
Soak before scale
The system reads real queries and drafts answers only your team can see — for two weeks. You grade the drafts. The disagreements drive the last round of tuning.
Ship on a canary
Five percent of traffic, then fifty, then full — watching grounding and citation accuracy the whole way.
It's the same discipline we wrote up in RAG without regret — the public version of the checklist we run internally.
Built to be queried.
- →Support assistants that answer from your real docs, not the open internet.
- →Internal knowledge bots that let staff query policies, runbooks, and contracts in plain language.
- →Product search and Q&A grounded in your catalogue or knowledge base.
Model-agnostic, by design.
OpenAI, Anthropic, and Google Gemini for generation. LangChain and LlamaIndex for orchestration. Pinecone, pgvector, or AWS Bedrock for retrieval. Postgres and Next.js underneath. We pick per problem and build in fallbacks — we're not tied to one vendor.