RAG chatbot over your docs to order
Answers grounded in your docs, with sources, not guesses
A RAG chatbot to order answers from your documents in your tone, with a source link, and escalates only the hard cases to a human. It is retrieval-augmented: a vector search over your knowledge base feeds the model, so the answer is grounded in your content, not invented. I ground it on PDFs, Confluence, Notion, or your help center, tune the tone, and add a human hand-off when the docs do not cover a case. It runs on your site, Telegram, or Slack from one backend. The LLM pipelines I ship in production prove the grounding part is well-trodden ground.
For whom
- Support: the answer is buried in 50 docs nobody reads
- Sales: prospects ask the same thing on every demo
- Teams: an internal Q&A that actually knows your wiki
Included
- Answers only from your docs, with a source link
- Vector search tuned to your content and language
- Speaks in your tone via tuned prompts
- Human hand-off when the answer is not in the docs
How we work
- 01
Brief
We fix the docs, the tone, and the escalation rules.
- 02
Build
I ground the agent on your docs and wire the hand-off.
- 03
Launch
I test on real queries and hand it over.
Timeline: from 1 week·Price: on request, quote after the brief
Stack and integrations
- TelegramSlackNotionPostgreSQL
Related work
Questions
›Will it hallucinate?
It answers only from your docs and cites the source. When the answer is not there, it says so and routes to a human instead of guessing.
›Which docs can it read?
PDF, Confluence, Notion, your help center, or a folder of files. I load and chunk them, then ground the model on top.
Answers grounded in your docs, with sources, not guesses
Discuss your project