AI for manufacturing and industry
Ideasforge builds artificial intelligence solutions for industrial and manufacturing companies that need to automate processes, make technical knowledge easier to reach and connect data, documentation and operational systems. AI agents, automations and assistants connected to ERP, MES, databases, technical documentation, in-house software and APIs.
We don’t apply AI in a generic way. We build it in where it cuts time, makes information easier to reach or removes manual work.
Artificial intelligence for complex industrial environments.
When information is spread across several tools, much of the knowledge ends up depending on people who know where to look or how to read each system.
Artificial intelligence can become a layer of access and automation over that existing infrastructure.
- Operational data
- Technical documentation
- Procedures
- Work orders
- Incidents
- Maintenance
- ERP
- MES
- Databases
- In-house systems
- Manuals
- Spreadsheets
What can AI do in an industrial company?
Artificial intelligence can step into technical, operational, document and administrative processes.
Technical documentation lookup
The user asks a question in natural language and the system retrieves the relevant information from the authorized sources.
- Manuals
- Procedures
- Technical data sheets
- Protocols
- Maintenance documentation
- Product documentation
- Internal instructions
Access to operational data
An agent can query information stored in databases, ERP, MES or other industrial applications, without making the user click through several screens.
Which maintenance orders are still open on line 3?
- Which incidents have come up most often this quarter?
Incident management
AI can help interpret an incident, classify it, retrieve related documentation and send it to the right process.
- Create an order
- Record information
- Assign owners
- Raise alerts
- Update statuses
Internal technical support
An assistant can help employees find procedures, past cases and documentation related to a specific problem. Especially useful where part of the knowledge sits with a few very experienced people.
- Procedures
- Past cases
- Related documentation
Administrative process automation
AI can also step into the administrative processes around operations.
- Document handling
- Data entry
- Email classification
- Validations
- Report generation
- Coordination between applications
“Has something like this happened before, and which procedure was followed?”
In industry, knowledge isn’t always organized in a single source. It can be spread across manuals, procedures, manufacturer documentation, incident history, databases, maintenance systems and internal know-how.
An AI system can connect those sources and, when an incident comes up, build an answer based on real documentation and operational information.
AI agents connected to industrial systems.
An agent can become an access layer over different systems. The goal isn’t to replace them.
It’s to make information easier to reach and automate actions between them.
AI agent development →- ERP
- MES
- CMMS
- Databases
- APIs
- In-house software
- Document management systems
- Maintenance systems
- Quality applications
Industrial process automation with AI.
Many industrial processes need to combine AI with conventional rules. AI interprets the part that varies. The software keeps control of rules, permissions and actions.
- Incident received
- Interpretation
- Classification
- Documentation lookup
- Order created
- Assignment
- Document received
- Extraction
- Validation
- ERP
- Filing
- Exception
Where it applies on the plant floor and in the technical office.
Four areas where AI tends to cut the time spent looking for information and moving data between systems.
Maintenance and operational support
Faster access to the information that matters for the first diagnosis.
- Procedures
- History
- Machine documentation
- Previous interventions
- Spare parts
- Similar incidents
It doesn’t replace technical experience. It makes it easier to reach.
Incidents and historical knowledge
Incidents generate valuable information that, stored as free text, is hard to reuse.
- Classify incidents
- Group similar cases
- Retrieve past cases
- Find patterns
- Link documentation
- Summarize history
Accumulated information turned into reusable operational knowledge.
Assistants for the plant floor and technical office
An interface to look up technical information without knowing which system it’s stored in.
- Look up procedures
- Find documentation
- Retrieve data
- Review history
- Product information
- Previous incidents
Conversation as a layer over industrial knowledge.
Quality and documentation
Large volumes of documentation and workflows where documents have to be interpreted before the process continues.
- Find procedures
- Compare versions
- Summarize documentation
- Classify files
- Retrieve standards
- Identify relevant information
Documents interpreted before they enter the flow.
A real case: a plant assistant
Guided troubleshooting for whoever is at the machine
See the plant assistant case →
In an industrial company, plant knowledge lived in dense manuals and in the heads of the most experienced people.
We built an assistant on the company’s own systems. Operators and supervisors query production and fault data, and get step-by-step guided troubleshooting when a machine stops.
Underneath, an orchestrator routes each query to one of half a dozen specialized agents. The accuracy of choosing which agent answers went from 72% to 91%, measured on 118 real cases.
In industry, a wrong action can have serious operational consequences.
That’s why we don’t design agents with unlimited freedom. AI interprets and critical decisions are made by the code, never by the model.
You don’t need to replace your current infrastructure to apply artificial intelligence.
We can build in
- Permissions
- Validations
- Limits on actions
- Business rules
- Human approval
- Logs
- Traceability
- States
- Error recovery
Before integrating we analyze
- Permissions
- Data quality
- Update frequency
- Allowed operations
- Impact of errors
- Need for human validation
Not every industrial process needs AI.
Part of our job is identifying where artificial intelligence really adds value. When a rule can be expressed clearly in software, we usually prefer to solve it that way.
It makes sense when there is
- Natural language
- Unstructured documentation
- Decisions that vary
- Large volumes of information
- A need to retrieve knowledge
- Several sources
Biggest return in processes with
- Plenty of documentation
- Frequent incidents
- Scattered knowledge
- Repetitive administrative tasks
- Several applications
- Large teams
- Long search times
- Dependence on specific people
A good industrial system doesn’t use more AI. It uses the right amount.
How we build AI solutions for industry.
Before building we analyze frequency, current time, volume, errors, number of users, operational impact, complexity and the share that could be automated. The goal is to improve the process, not to add technology.
-
01 We analyze the process
We study how it works today and which systems, people and sources are involved.
-
02 We identify friction points
We look for repetitive tasks, information searches, incidents and manual steps.
-
03 We review data and documentation
We analyze ERP, MES, databases, technical documentation and in-house software.
-
04 We design the architecture
We define which part needs AI, which part should stay in conventional software and which controls are needed.
-
05 We build and evaluate
We test real cases, errors, exceptions and edge cases.
-
06 We deploy and monitor
We measure usage, errors, times, answer quality and results.
Artificial intelligence for industry.
Custom development usually makes sense when several systems have to work together, knowledge is scattered and an off-the-shelf tool doesn’t cover the whole flow.
How can AI be used in an industrial company?
To look up technical documentation, reach operational data, manage incidents, automate processes and make internal knowledge easier to reach.
It can also connect to ERP, MES, databases and in-house software.
Can AI query production data?
Yes. A system can query operational data as long as there’s an integration path and the right permissions are defined.
The information can be retrieved from databases, ERP, MES or other applications.
Can AI be used for maintenance?
Yes. AI can help find documentation, retrieve history, classify incidents and reach information related to maintenance.
It doesn’t replace technical judgment, but it can cut search times.
Can it connect to our ERP or MES?
Yes, as long as there’s a suitable integration.
The connection can be made through APIs, databases or other available mechanisms.
Can AI be used on technical documentation?
Yes. An assistant can look up manuals, procedures, technical data sheets and other sources in natural language.
It can also show the sources it used.
Can AI be used to manage incidents?
Yes. The system can interpret an incident, classify it, retrieve related information and trigger the right flow.
Complex cases can be passed to a person.
Is AI safe in an industrial environment?
It can be, if the architecture properly defines permissions, validations, limits and traceability.
Critical actions can stay under human control or deterministic rules.
How much does it cost to bring AI into an industrial company?
The cost depends on the process, the integrations, the systems involved, the data, the architecture and the security requirements.
A documentation assistant and a system connected to several operational environments have different scopes.
Other industries
Tell us which industrial process you want to improve.
You don’t need to arrive with a defined technology solution. Tell us which process eats up time, where the knowledge lives and which systems your team uses. We’ll look at what can be automated, where artificial intelligence makes sense and what architecture it would take to get it into production.