RAG Assistant
A RAG Assistant Over Your Internal Knowledge Base
Your team already wrote the answers. They are buried across Confluence, Google Drive, Notion, Slack threads and a dozen PDFs nobody can find. We build a RAG assistant that retrieves the right passage, cites the source, and respects who is allowed to see what.
The problem: answers exist, retrieval does not
Knowledge is spread across tools that do not talk to each other, and keyword search returns 40 documents instead of one answer. New hires ask the same questions in Slack, support agents re-derive policies that already exist, and engineers ping each other instead of reading the runbook. The cost is not missing information. It is the 20 to 30 minutes per lookup, multiplied across every employee, every day.
How our RAG assistant solves it
We connect to your existing sources, chunk and embed the content, and ground every answer in retrieved passages so the model quotes your documents instead of inventing facts. Each response shows its citations with links back to the original page, so staff can verify in one click. Permissions are enforced at retrieval time: the assistant only surfaces what the asking user is already allowed to read.
How it works under the hood
We sync sources on a schedule or via webhooks, run a chunking and embedding pipeline into a vector store (pgvector, Pinecone or your existing Postgres), and use hybrid search (semantic plus keyword) with a reranking step to push the most relevant passages to the top. The LLM answers only from retrieved context, refuses when confidence is low, and logs every query so you can see gaps and stale docs. We deploy it where your data lives, including your own cloud.
Why teams choose Nerai Labs
This is not a wrapper over a generic chatbot. Our founder built and shipped production systems at Pomelo, MercadoLibre, Mercado Pago and Scale AI, and our team has run pipelines handling 50K+ daily executions. We treat retrieval quality as an engineering problem: we measure it, we tune it, and we hand you an evaluation set so accuracy does not silently drift.
How it works
- 01
Connect sources
We map your knowledge base, integrate Confluence, Drive, Notion, Slack, PDFs and tickets, and mirror their existing access permissions.
- 02
Build pipeline
We chunk, embed and index your content, then tune hybrid retrieval and reranking against a real question set from your team.
- 03
Ground and cite
The assistant answers only from retrieved passages, cites every source, and abstains when the documents do not contain the answer.
- 04
Measure and ship
We deploy in your environment, wire up query logging and an eval suite, and keep the index fresh as your docs change.
What you get
- Answers in seconds instead of 20 to 30 minute manual searches across tools
- Every response cited with one-click links to the source document
- Permission-aware retrieval so staff only see what they are cleared to read
- Fewer repeat questions in Slack and fewer internal support tickets
- Faster onboarding: new hires self-serve from day one
- A query log that surfaces missing, stale or contradictory documentation
Questions
Which knowledge sources can you connect?
We integrate the tools you already use: Confluence, Google Drive, Notion, SharePoint, Slack, Zendesk and raw PDFs or Markdown. If a source has an API or export, we can sync it. We keep the index current with scheduled syncs or webhooks so answers reflect the latest version.
How do you stop the assistant from hallucinating?
Every answer is grounded in retrieved passages from your own documents, and the model is instructed to answer only from that context and cite it. When retrieval confidence is low, it abstains rather than guessing. We also ship an evaluation set so you can measure answer accuracy over time, not just trust it.
Does it respect our access permissions?
Yes. Permissions are enforced at retrieval time, so the assistant only returns content the asking user is already authorized to see. A document outside someone's access never enters their answer context. This is configured against your existing identity and source-level permissions.
Where does our data live and is it secure?
We deploy in your cloud or a dedicated environment so your documents and embeddings stay under your control. We do not train third-party models on your data. You choose the LLM provider, including private or self-hosted models if data residency requires it.
How long does it take to launch?
A focused first version over a defined set of sources typically takes a few weeks, depending on connector count and access complexity. We start with your highest-traffic knowledge area, prove retrieval quality against a real question set, then expand. You get a working, measurable assistant early rather than a long build with no checkpoints.
