RAG fails on messy retrieval. Brainforge builds knowledge systems over your docs, transcripts, tickets, and data — vector search, context engineering, citations, and evals — so agents answer from approved context instead of guessing.
In plain terms
Where teams get stuck
Our AI gives generic answers because retrieval returns junk.
Nobody can verify where an answer came from.
Docs, transcripts, and tickets are scattered and unsearchable.
We can't measure whether RAG quality is improving or drifting.
What we deliver
Vector search, hybrid retrieval, and knowledge indexing over your sources, on the stack that fits your data — including Turbopuffer and pgvector.
Structuring approved sources, business rules, and memory so the system knows what it is allowed to use and how to use it.
Retrieval and answer evals, citation checks, and quality review loops that turn failures into improvements.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Vector search and hybrid retrieval over docs, transcripts, tickets, and data, on the vector store that fits your scale.
Structuring approved sources, rules, and memory so the system knows what it can use.
Pipelines that keep retrieval fresh as your docs, tickets, and transcripts change.
Answers carry source trails so teams can verify and trust what AI says.
Retrieval and answer evals that catch drift and rank improvements.
What changes
Proof in production
Our internal assistant searches meeting transcripts, vault docs, and repo context with source trails on every answer.
See the Slack Assistant proof →See how AI copilots used unified campaign context to flag underperforming assets and budget misallocations.
Read the copilot case study →Related ways to engage
Common questions
Engagements start with a scoped build sprint, so you pay for a bounded piece of work rather than an open-ended retainer. Most teams begin with one knowledge domain and one retrieval use case, then expand across sources.
A first retrieval system with ingestion, vector search, citations, and evals typically ships in 3–6 weeks of sprint work, depending on how many sources and how clean the data is.
We pick the store that fits your scale and stack — commonly Turbopuffer for high-volume retrieval or pgvector when you want to stay inside Postgres. The choice is driven by your data, not by a preferred vendor.
Ingestion pipelines refresh as your docs and tickets change, and eval suites measure retrieval and answer quality so drift is caught instead of discovered by users.
Yes. We index structured and unstructured sources — docs, transcripts, tickets, chat, and warehouse data — so the context layer spans how your company actually works, not just the wiki.
How we work
We find where answers live, where they go stale, and what retrieval needs to return first.
We wire ingestion, vector search, and context engineering so the system retrieves the right approved sources.
We set evals, measure quality, and iterate from real queries and failures.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
