Enterprise KM Chatbot

Enterprise KM Chatbot suits maintenance teams, engineers and knowledge management teams searching large collections of reports, machine manuals and SOPs. Type a question in everyday language; the system retrieves relevant documents and answers with source references, so you do not need to open files one by one.

Knowledge used in factories and power plants is often scattered across maintenance reports, manuals and SOP files, with some held by senior engineers. Teams may need to open many files to answer a single question. Enterprise KM Chatbot organises these documents into a knowledge base that can be searched by asking questions.

Enterprise KM Chatbot users

• Maintenance teams and engineers who regularly search equipment history by Tag No. or consult manuals
• Knowledge Management teams responsible for organisational standards documents
• Maintenance contractors who need to submit well-organised reports to factory owners
• Organisations with a policy of keeping documents on their own servers

Enterprise KM Chatbot works in 4 stages: collect documents, structure and index them, ask through chat, then verify and improve. Answers identify the documents used, allowing users to check the originals before acting on them.

1. 1

   Collect documents

   Import existing maintenance reports, manuals and internal documents, including existing PDFs and new data entered through digital forms on nimitFlow.

2. 2

   Structure and index documents

   The system processes and indexes documents to make them searchable. For reports using a common form, it structures the data by field, such as equipment Tag No.

3. 3

   Ask through chat

   Type a question in everyday language on the website. The system retrieves relevant documents, then the LLM composes an answer identifying the documents used. Conversation history is saved for later review.

4. 4

   Verify and improve

   Users open the documents cited in an answer to verify it before use. After delivery, we improve the system based on user feedback, subject to the quotation's terms.

How do we verify answers?

• Answers identify source documents, such as a report's Tag No. Users should open and review those reports before using an answer to make decisions.
• Questions involving counts across the entire collection need to be designed to use structured database data. We therefore agree on question types with you at the start; see “What we learned” for the reasons.
• Before delivery, we test answers against questions your team actually asks.
• We do not publish a universal accuracy figure because answer quality depends on each organisation's documents and questions.

Enterprise KM Chatbot combines document-based Q&A, digital report forms, Excel exports and dashboards in one system on nimitFlow. Supporting features are designed around each organisation's documents.

• Natural-language Q&A — supports Thai documents and questions
• Source references — answers identify the reports used, such as by Tag No.
• Browser-based chat — includes search and conversation history
• Digital report forms — enter new reports and attach images; submitted reports enter the knowledge base
• Excel export — export reports as Excel files for further use or sharing
• Dashboard — groups reports by production unit

Where are documents and questions stored?

Enterprise KM Chatbot can be installed on your organisation's server (On-Premise) and use an internally hosted LLM (Local LLM). Together, these allow documents and questions to remain within your organisation's network. If you choose an external LLM service, we explain which text will be sent outside before you decide.

Server specifications depend on document volume, user count and the chosen LLM. We assess these before quoting. If you want all AI processing to run internally, we can supply and install an AI server system.

Enterprise KM Chatbot suits finding and summarising content in consistently formatted documents, such as inspection and repair reports using the same form. It is not suitable for making decisions from answers without checking the source documents, because LLMs can be wrong. Answer quality depends on the completeness and consistency of the imported documents.

We developed an AI Knowledge Management system on nimitFlow for refinery control valve maintenance in the energy industry and delivered it in 2026. The inspection and repair history was held in around 2,000 PDF reports organised by Tag No. (each valve's identifier). The system includes digital report forms, Excel report exports, a dashboard grouping reports by production unit, and Enterprise KM Chatbot combining an LLM with RAG.

The project began as a demonstration prototype (POC) and was then developed into an operational system. The client signed a Job Completion Certificate. The AI assistant is designed to answer questions such as “What has been replaced on this valve?” and identify the Tag No. of relevant reports so users can open the actual reports.

What we learned

RAG retrieves only a subset of the most relevant documents, so it suits questions answered by a few documents. Collection-wide counts, such as “How many reports are there for this brand?”, must therefore be designed to use structured database data. Letting the LLM count by itself can produce incorrect figures.

Reports using the same forms, headings and units are easier to structure and help AI answer more precisely. On-site users need to review specialist terms and form headings so they match the reports their team uses.

What does it mean to you?

Your project therefore starts with actual document samples and frequently asked questions from your team. We distinguish document-content questions from numerical questions at the outset, then test answers against that list before delivery. You can begin with a prototype (POC) and a small document set, then expand to all documents.