From machine signal to accountable action
Industrial AI makes factory information easier to use. A machine anomaly, production delay, quality issue, or maintenance alert can now be surfaced with far more context than before.
But understanding the signal does not complete the work it creates.
Resolving it may require a maintenance ticket, an ERP work-order update, an inventory check, a customer-impact decision, a supplier message, and an audit record. Those steps live across independent systems and teams. People still connect them manually.
Dialogo is built for that next part: governed execution across the systems a company already uses.
Watch the industrial workflow walkthrough
The coordination gap after an industrial event
Industrial platforms such as Zerynth are valuable sources of shop-floor data, production context, and industrial connectivity. Zerynth publicly describes REST APIs and modular connectors for assets, production and energy flows, including ERP, MES, CMMS, SCADA, and BI systems. Its Zero AI Copilot is designed to query integrated ERP and machine data in natural language. See Zerynth's integration overview.
That capability should not be duplicated. It should be extended.
The operational question after an event is different:
What must happen next, who is allowed to do it, and how do we know it was completed correctly?
This is the coordination gap. It appears when the response crosses independent systems and involves both automation and human judgment.
Where Dialogo fits
Dialogo sits between an operational event and the company-wide response.
Receive the signal
A machine, MES, IoT platform, email, or operator request starts the workflow.Assemble the context
Dialogo gathers the affected production order, maintenance history, approved procedure, inventory position, customer commitment, and prior incidents.Plan the permitted response
The agent determines the next steps, required systems, responsible people, permissions, and approval checkpoints.Execute across systems
Dialogo can coordinate authorised work across ERP, maintenance, CRM, communication tools, documents, and internal APIs.Verify and record the outcome
The workflow checks each result, manages exceptions, and stores the complete action history in the Run Ledger.
The point is not to make a generic assistant sound informed. The point is to complete the operational work safely.
Example: cross-system industrial exception resolution
Consider a production interruption.
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Signal detected
An industrial platform reports an anomaly or unexpected machine stop.
Context assembled
Dialogo retrieves the machine and production status, affected work order, spare-part availability, maintenance procedure, and delivery or customer impact.
Response planned
The agent identifies which actions are permitted automatically and which require a person to decide.
Approval requested
Changes that affect production, external communication, or machine-related action pause at the appropriate Approval Gate.
Systems updated
After approval, Dialogo can create or update the maintenance ticket, update the ERP work order, notify the responsible teams, prepare supplier or customer communication, and attach the relevant evidence.
Outcome verified
Every tool call, approval, result, error, retry, and exception is visible in the Run Ledger.
This is autonomy with control: less manual coordination, without hiding consequential decisions.
What a credible industrial AI validation should measure
For this type of workflow, a useful evaluation goes beyond the quality of generated text. It should test:
- End-to-end workflow completion
- Correct system, tool, and record selection
- Correct action parameters
- Permission and approval compliance
- Recovery from API and data failures
- Prevention of duplicate actions
- Audit-trail completeness
- Time and cost per resolved exception
This aligns closely with the type of validation AI-MATTERS makes possible. Reply's published service includes agentic logic testing for complex tasks that use external tools such as APIs and databases, plus adversarial testing. Read the Reply validation service.
For requirements-based testing, FBK's published service covers functional and coverage-based testing of AI software for manufacturing. Read the FBK testing service.
A practical first experiment
The first pilot should be deliberately narrow: one recurring industrial exception that crosses at least three systems.
For example:
- A production interruption that requires maintenance, ERP, and team communication
- A quality non-conformity that affects production, documentation, and customer commitments
- A material or supplier delay that requires inventory, purchasing, and delivery coordination
The objective is to prove one workflow end to end. Then the same governance and execution layer can be reused for maintenance, quality, procurement, logistics, customer operations, and other regulated workflows.
The question worth validating
Industrial intelligence helps teams understand what happened.
Governed execution helps them complete what must happen next.
The question is not only whether an AI agent gives the right answer. It is whether it can safely and reliably complete the right operational work.