AI agents for 14 tasks
The 14 tasks for which T-NEX builds AI agents, with the shared components, a pilot with a measurable goal and the rules for running the agents.
Learn moreAI agents for companies
Combine business figures, query them and flag what stands out
T-NEX builds AI reporting and analytics agents: combining figures from ERP, point-of-sale systems and spreadsheets, answering questions on business data in natural language, producing regular reports and flagging unusual trends.
- 01Data analytics use cases
- 02Components for analysis and data connection
- 03Data access, checking and sign-off
Aligned with your processes
In daily work
Data analytics use cases
The agent works with the figures your business already produces: orders and postings in the ERP, sales at the point of sale, planning and list data in spreadsheets. Which source is connected first is agreed in the project.
- Combine figures from ERP, point-of-sale systems and spreadsheets in one analysis instead of reading them off several files.
- Ask questions about business data in your own words, for example revenue per branch for the selected month, and build the next question on the result.
- Produce a recurring report from a clearly defined analysis, such as an overview for management, and pass it on as a PDF.
- Make unusual trends visible and report them, for instance a variance before the next meeting. An annotation keeps it with the analysis it concerns, so follow-up questions stay there.
- Check a variance down to individual entries: the table shows the entries, and an annotated chart supports the investigation of causes.
Overview
Components for analysis and data connection
The analysis itself is handled by T-NEX Dynamic AI Reporting. A business question becomes a dashboard on the connected data; your team develops it further through the conversation or the editor, organises it in folders and shares it by role.
So that figures from ERP, point-of-sale systems and spreadsheets arrive there, we develop the connection as part of the project. Data ownership, field mapping and access rights are defined for each system. Faulty records should stay visible before they affect live operation.
Overview
Data access, checking and sign-off
Which data sources an agent may use is defined for each set of users, and viewing and editing permissions are granted separately by role. In the schema-only approach, the AI model receives structural information such as tables and fields, and the application accesses the records. If a model is to interpret specific amounts or individual records, a different scope of information is needed. We decide with you, for each use case, which content may reach which model.
A generated dashboard establishes neither that your data is complete nor that a business interpretation is correct. That is why acceptance compares generated analyses with known results, including empty datasets, incomplete sources and ambiguous questions. Who interprets a result and releases it is named in advance.
We describe data locations, access for development and support, and the services involved before setup. 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 BI and dashboards, for sales and demand forecasts, and partners of data platforms. 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 start with a recurring, clearly defined analysis that is compiled by hand from several spreadsheets today, and check whether a reliable data source and a named owner for the figures exist.
- 02
Pilot with a measurable goal: Together with your team we define metrics, dimensions, time basis and control totals. The pilot measures whether generated analyses match known results, including empty datasets, incomplete sources and ambiguous questions.
- 03
Set the rules: Data connection, roles and permitted model connections are in place, and responsibility for interpretation and release is named, before the agent goes into operation.
- 04
Operation and extension: Only after the pilot do we add data sources, reports or users. A report goes to further roles once it has been checked against known figures; support and response times are agreed for the specific application.
FAQ
Questions about the solution
Which data sources does the agent combine?
The agent is designed to combine figures from ERP, point-of-sale systems and spreadsheets. Each source needs a connection to the respective system; beforehand we define data ownership, field mapping and access rights. We start with one reliable data source and one report, and further sources follow after the pilot.
Does the AI model see our business figures?
In the schema-only approach, the model receives structural information such as tables and fields, and the application accesses the records. If a model is to interpret specific amounts or individual records, a different scope of information is needed; we agree for each use case which content may reach which model. A schema-only setup is not a blanket commitment for every analysis function.
Does the agent also produce forecasts?
The agents described here combine figures, answer questions about them, produce reports and flag unusual trends. A dedicated forecasting model, for example for sales or demand, is not part of what this page describes. If you need a forecast, we first formulate the business question together and record in the pilot how you will recognise a usable answer. We commit only to what the pilot shows on your data.
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