Chat with us!We built an internal AI knowledge assistant that lets employees ask questions in plain language and get accurate, cited answers drawn from scattered wikis,...
It is like having a colleague who has read every document we own and answers in seconds.
Head of Internal Operations
We built an internal AI knowledge assistant that lets employees ask questions in plain language and get accurate, cited answers drawn from scattered wikis, docs, and tickets — ending the daily hunt for information.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Enterprise |
| Technologies | LLM, pgvector, LangChain, Next.js, Node.js, OAuth / SSO |
| Delivery Partner | mTouch Labs |
| Primary Outcome | Employees found trustworthy answers in seconds, reclaiming time and easing the load on experts. |
Institutional knowledge was fragmented across wikis, drives, and chat history. Employees wasted hours searching, and the same questions were answered over and over.
We connected the assistant to all content sources, indexed them with semantic embeddings, and used retrieval-augmented generation to answer questions with citations and permission awareness.
Employees get instant, source-cited answers in a chat interface that respects access permissions and improves as content grows.
Answer generation with grounding
Semantic search over content
Retrieval and citation pipeline
Chat interface and admin tools
Connectors and indexing service
Permission-aware access
Catalogued knowledge sources and access models.
Built connectors and semantic indexing with permissions.
Implemented RAG with mandatory citations.
Enforced per-user permission-aware retrieval.
Launched company-wide with gap analytics.
Employees found trustworthy answers in seconds, reclaiming time and easing the load on experts.
A grounded, permission-aware knowledge assistant turned fragmented institutional knowledge into an instant, trustworthy resource for the whole company.
We delivered an AI workflow automation platform that orchestrates multi-step business processes across departments — routing documents, making decisions, a....
View case studyWe built an autonomous AI support agent that resolves customer tickets end to end — understanding intent, retrieving account context, and taking real actio....
View case studyWe developed a generative AI chatbot that holds natural, context-aware conversations across a SaaS product — answering product questions, guiding onboardin....
View case studyWhat businesses ask us most often about this project and how we built it.
It is an internal assistant that answers employees' plain-language questions using retrieval-augmented generation over your wikis, documents, and tickets, with citations to the source.
Yes. Retrieval is permission-aware, so users only get answers from content they are allowed to see.
Every answer is grounded in your real content and cites its sources, and the assistant declines when it lacks supporting material.
It connects to wikis, document drives, ticketing systems, and other internal content via secure connectors.
Yes. An automatic re-indexing pipeline keeps the knowledge base in sync as content changes.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
