RAG knowledge assistants, built for you and watched in production.
We handle RAG development end to end — building an AI knowledge base assistant that uses retrieval-augmented generation to answer from your own documents, wikis, and data, with citations instead of guesses — and wire every assistant into Obsivara so you see cost, retrieval failures, and health from the first query. Answers grounded in your knowledge, without the hallucinations or the black box.
- Scoping call + a clear fixed plan and timeline before any work starts
- Assistant built on your documents, models, and accounts
- Read-only Obsivara connection — per-query cost, retrieval failures, and health
- Citations, access controls, and re-indexing wired in
- Full handoff — you own it; we maintain or hand over entirely
RAG & Knowledge Assistants, done for you.
Internal knowledge assistants
Assistants that answer staff questions from your wikis, policies, and tickets, with citations and access controls so the right people see the right sources.
Customer-facing knowledge bases
Retrieval-grounded assistants over your docs and help center that answer accurately and link to the source, so customers get real answers, not guesses.
Fixes, retrieval tuning & hardening
Audit an existing RAG setup for bad chunks, stale indexes, and hallucinations: tune retrieval and re-ranking so answers stay grounded as your docs change.
vector DB · embeddings · LangGraph · OpenAI / Claude · re-ranking · your docs & wikis
Build → connect → monitor.
Scope & build
On a short call we scope exactly what you need, then design and ship it around your stack, data, and guardrails — with a fixed plan and timeline before any work starts.
Connect to Obsivara
Every system we build is wired into Obsivara read-only from day one — via native n8n, SDK, OpenTelemetry, or webhook. No proxy in your request path, so it never adds latency or a failure point.
Monitor & improve
You get cost intelligence, health scoring, silent-failure alerts, and a weekly audit — and we keep it reliable and tune it as your usage grows, or hand it over entirely.
Built by us. Watched by Obsivara.
Because we build the AI observability platform these assistants run on, monitoring isn't an afterthought — every knowledge assistant ships connected to Obsivara via one read-only API key, with per-query cost attribution, retrieval-failure and hallucination detection, and health scoring live from day one. Nothing sits in your users' request path, so nothing we add can slow an answer down.
RAG & Knowledge Assistants — common questions
How is this different from a chatbot?
A RAG assistant is built to answer from your own knowledge — it retrieves the relevant documents first and answers with citations, instead of relying on the model's memory. We connect Obsivara read-only so retrieval failures and cost surface per query.
How much does RAG development cost?
It's scoped and quoted per project — a single-source FAQ assistant and a multi-source knowledge base with access controls are very different builds. You get a fixed quote and timeline on the scoping call before any work starts. The Obsivara monitoring has a free tier, so observability isn't an extra line item to begin.
How do you keep answers accurate as our docs change?
We wire in re-indexing so new and updated documents flow into the assistant, tune retrieval and re-ranking to cut bad matches, and connect Obsivara so hallucinations and retrieval failures surface as alerts. You own the assistant and the data it runs on.
Ready to build your rag assistants?
Tell us what you want built. We'll scope it, quote it, and ship it observable — with a clear plan before any work starts.