Industrial AI
AI applied to industrial data, not generic answers.
Link applies industrial AI over reliable automation, traceable data and process context. The goal is not to replace engineering, but to increase analysis and decision capacity.
Who it is for
AI for operations that already have process, data and technical responsibility.
Industrial AI makes sense when the plant has reliable data, clear process context and teams prepared to validate the results.
Plants with reliable automation
Industrial AI works best when the plant already has stable control, historical data and clear operational routines.
Production and quality teams
Agents can help investigate deviations, compare batches, read reports and organize process information.
Engineering and maintenance
Industrial data can support diagnostics, recurring alarm analysis and investigation of equipment behavior.
What agents can support
Agents prepared to analyze industrial data with operational context.
The proposal is not to create generic answers. Agents should work over histories, events, alarms, batches, reports and technical information from the operation.
Data reading with process context
The goal is not generic answers, but analysis based on the plant history, reports, alarms, batches and technical documents.
Operational investigation
Agents can help point where to look first, reducing manual effort in deviation and performance analysis.
Decision support
AI supports engineers and specialists with summaries, comparisons and recommendations that must always be technically validated.
Solution layers
Before AI, the plant needs structured data with meaning.
Link organizes the solution in layers: industrial data base, process context and, finally, agents able to support analysis and decision-making.
Industrial data base
SQL databases, historians, reports, events, alarms and electronic records organized with traceability.
Process context
Understanding recipes, units, phases, critical variables, deviations and operating rules.
AI agents
Agents applied over reliable information to support analysis, diagnostics and operational recommendations.
Industrial AI Diagnostic
Evaluate your operation’s readiness for industrial AI.
This is the initial diagnostic logic: practical questions connected to what really determines whether AI can be applied safely to industrial processes. Open each item to view the question and select the options that represent your operation.
What type of process does your plant have?
The first step is to understand the industrial operation that will be connected, monitored and analyzed.
Select one or more options
What is the current automation level of the operation?
AI readiness depends on the existing control base, the connection between equipment and how data reaches industrial systems.
Select one or more options
Do you have historians or industrial data recording systems?
Historians, industrial databases and proprietary systems are important sources for process analysis, traceability, Batch Analytics and local AI agents.
Select one or more options
Are process data recorded automatically?
The more automatic and structured the data collection is, the greater the ability to generate reliable analysis and reduce manual effort.
Select one or more options
Is there reliable history of batches, events or alarms?
This answer indicates whether the plant already has enough context for Batch Analytics, batch comparison and AI agents with industrial context.
Select one or more options
Does the team use dashboards or digital reports?
The way the team consumes data today helps define the path between operational visualization, batch analysis and applied AI.
Select one or more options
What is the main objective with industrial AI?
AI must be connected to a real operational need, not only to a technology trend.
Select one or more options
Does your operation already have enough data for batch analysis?
This question helps identify whether the next step is to organize the data base, apply analytics or evolve to AI agents.
Select one or more options
Agent result
Diagnostic not started
Select some answers to generate an initial reading.
The diagnostic runs locally in the browser and uses only the answers selected on this page.
Select answers to calculate the path
Link recommendations
Practical applications
Industrial AI applied to real production, quality and engineering problems.
The application must start from the operation need: reduce investigation effort, compare data, find patterns and transform industrial history into usable information.
Responsible AI
Artificial intelligence does not replace a well-defined process.
In industrial environments, AI must be applied with governance, traceability and clarity about its limits.
AI does not replace process knowledge
In regulated environments, the technical team remains responsible for validation and decisions.
Data must be reliable
Poor data quality creates poor recommendations. The base must be reviewed before critical use.
Governance is part of the solution
Access, traceability, limits and review criteria must be defined from the start.
Next step
Request an industrial AI diagnostic.
We evaluate existing automation, data sources, histories, batches, events and real opportunities to apply analytics and AI agents safely.