AI agents for companies

Find maintenance knowledge, process documents, query orders and stock

T-NEX builds AI agents for factories, plantations and warehouses: maintenance and knowledge assistants for technicians, processing of inspection records, delivery and freight documents, order and stock queries in natural language, and custom software for operations.

Discuss a manufacturing or logistics use case Explore the details

AI agents for manufacturing, plantations and logistics

01What the agents take on in the factory, on the plantation and in the warehouse
02Which components serve which purpose
03Access, sign-off and pilot on site

Aligned with your processes

In daily work

What the agents take on in the factory, on the plantation and in the warehouse

The agents work with what your business already has: work instructions and maintenance documents, inspection records, delivery notes and freight documents, and the order and stock data in your systems. Which of these areas we start with is agreed in the project.

Answer maintenance questions: Anyone working on equipment asks in their own words which work instruction applies and gets the answer from the released documents, with its source.
Bring maintenance knowledge together: Notes that today sit with individual people or in scattered files become a maintained knowledge base with a source and a named owner. Questions without a suitable answer show what is still missing.
Process inspection records: Details such as the associated asset, the date and the required evidence go into fixed fields. Poorly legible or missing passages are presented to your team for review instead of being filled in silently.
Read delivery and freight documents: Document numbers, line items, quantities and dates are recognised and passed to the next processing step as a reviewable record, for example as a file or through an interface.
Ask order and stock questions in natural language, for example which orders are still open for the week or how much of an item is in stock. The result appears as a table, key figure or chart based on the connected data source.
Develop custom software for an operating workflow, such as mobile data capture on site, order processing or the link between planning and execution, where existing software does not cover the workflow.

Overview

Which components serve which purpose

For maintenance we combine knowledge management and a chatbot. AI knowledge management sets up the selection and upkeep of the sources, and the AI chatbot makes them available in dialogue and shows maintained answers with their recorded source. An additional RAG function can compose answers from retrieved document passages; this stage is developed and tested as a separate scope. Tables, attachments and differing document versions often hold details that a summary leaves out, so the review process provides a path from a hit back to the original.

Document AI processes inspection records and delivery and freight documents. It combines text recognition, classification of the document type and extraction of the required information. A target schema is agreed for each document type. A direct posting or order creation is built only if it expressly belongs to the workflow.

T-NEX Dynamic AI Reporting answers order and stock questions on the connected data source; we develop the connection to your system in the project. Software for operating workflows is built as custom software, early on as a clickable prototype. We design work queues, clarification requests and the log based on the workflow automation concept, whose specific actions we develop for your business.

Overview

Access, sign-off and pilot on site

A maintenance assistant may use only information intended for the audience it serves. Access roles are therefore defined before sources are connected. For missing evidence, a clear response path is set up: insufficient information, a clarifying question or handover to the responsible team.

For documents and analyses, recognising a value is not yet a business sign-off, and a generated dashboard establishes neither that your data is complete nor that an interpretation is correct. We name in advance who checks and releases a result. Which content an AI model may receive is decided for each use case; data locations and access for development and support are described before setup.

A pilot tests the workflow on your own examples, including difficult ones: poorly legible passages, missing values, differing formats and questions without a matching source. For mobile applications we look at the intended devices and the connection conditions on site. Support hours and availability are agreed for the specific application.

Overview

More providers for this task in Malaysia

The register of AI providers in Malaysia lists further providers for this task, including solutions for automation and robotics, quality inspection and machine vision, warehousing, fleet and supply chain, and plantations, agriculture and food. This page describes only what T-NEX builds itself.

All 14 tasks for which T-NEX develops AI agents are listed in the overview.

The workflow

How we introduce the solution.

01
Choose a use case: We begin with a defined, recurring task, such as a document type like the delivery note, a maintenance area with frequent questions or a query that is compiled by hand today, and check whether documents, data and responsible people exist for it.
02
Pilot with a measurable goal: Together with your domain team we define what a correct result is and which errors are not acceptable. Examples from your business, including difficult ones, measure correctly transferred values, missing information, answer quality and the correction effort needed.
03
Set access and sign-off: Audiences, released sources, the response path for missing evidence, the check by your domain team and a log of action, time and person handling it are fixed before the agent goes into operation.
04
Operation and expansion: Only after the pilot do we extend document types, sources, sites or users. Hosting, support and handover are agreed for the specific application.

FAQ

Questions about the solution

Which documents can the agent read, and who signs off the result?

The agent is designed to read inspection records and delivery and freight documents. For each document type we agree a target schema with the required fields and include difficult cases from the start: poorly legible passages, several possible dates, missing or contradictory values. Unclear information is made visible, and your domain team confirms or corrects a proposal against the original before it moves to the next step. Recognising a text does not replace a business sign-off.

Can the agent query orders and stock in our system?

This is intended for questions in natural language put to a connected data source; the result appears as a table, key figure or chart. Each source needs a connection to the respective system, which we develop in the project. Beforehand we define data ownership, field mapping and access rights and decide what a language model may see. We start with one reliable data source and one query, and further ones follow after the pilot. Whether a workflow also writes back to the system is defined separately.

Do the agents control machines, inspect parts by camera or predict failures?

No. The agents described here work with the documents, texts and data of your business: they answer questions, read documents, analyse data and support workflows. Machine control, robotics, camera-based inspection and failure prediction models are not part of what this page describes. The register of AI providers in Malaysia lists further providers for these. Maintenance documents, inspection records and order data around such equipment, however, can be part of a project.

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