Vertical AI employee
Prepare maintenance work orders
Maintenance Work Order Preparation Agent
The AI employee that turns maintenance and service tickets into actionable work orders.
Standard core plus process adaptation
The AI employee has a repeatable core and is adapted to the last 20 percent of your process logic, systems, rules and approval paths.
Existing systems remain leading
ERP, CRM, MDM, CMMS, email, Excel and specialist systems are not replaced. Digamma automates the manual work between them.
Human-in-the-loop
Critical decisions stay with humans. The agent checks, structures, prepares and routes exceptions in a traceable way.
Enterprise readiness
Built to pass corporate buying standards
Large companies do not buy a black box. The first workflow is scoped tightly, connected to existing systems and introduced with clear roles, approvals, data sources and success criteria.
01
No system replacement
ERP, CRM, MDM, CMMS, document repositories and ticketing systems remain the source of truth. The agent automates the manual work between them.
02
Controlled pilot
Start with one measurable process, clear data sources, defined exceptions and a business owner. No big-bang project.
03
Audit-ready handover
Checks, sources, open questions and decision packages are structured so business teams can review them.
04
Approvals stay inside the company
Critical values, new suppliers, compliance decisions and special cases are routed to humans instead of being pushed through automatically.
Value proposition
Better prepared maintenance work orders, fewer follow-ups, less downtime, stronger technician productivity and better first-time-fix chances.
Maintenance processes lose time before anyone starts the actual work. The agent turns incomplete tickets into prepared work orders with history, spare part hints, manuals, safety information and open questions.
What problem does the agent solve?
Maintenance leads, dispatch or technicians manually collect asset history, fault codes, spare parts, manuals, safety requirements and missing information before work can begin.
The focused entry point
Do not fully automate dispatch. Start by preparing maintenance and service work orders so humans, dispatch or technicians can decide and act faster.
Workflow
How the AI employee operates
The agent does not sit next to your systems. It works between intake, rule checks, system matching and human decisions.
Input
Email, ERP, form, ticket or document comes in.
Check
Rules, completeness, ownership and plausibility are checked.
Match
Data is matched with ERP, CRM, MDM, CMMS or document sources.
Route
Standard cases are prepared, exceptions go to humans.
Maintenance Work Order Preparation Agent
The agent does not sit next to your systems. It works between intake, rule checks, system matching and human decisions.
From ticket to actionable work order
A ticket, fault code or service request is read and structured.
Asset, history, location, manuals and spare parts are combined.
Open information, safety requirements and qualification are checked.
The prepared work order is handed to maintenance, dispatch or technicians.
What the AI employee takes over
- Reads incident tickets, service requests, maintenance work orders or fault codes.
- Identifies asset, machine, location, history and relevant documents.
- Finds similar incidents, matching manuals and possible spare parts.
- Checks open information, safety requirements and technician qualification.
- Creates prepared work orders including follow-up questions and handover to maintenance or dispatch.
Use case fit
When does this use case fit?
This use case is strongest when operational teams repeatedly check, sort and prepare exceptions before work can move forward.
Many maintenance, service or repair cases.
Follow-ups, missing parts or poor preparation slow technicians down.
Asset history, manuals, spare parts or safety information must be collected manually.
Strong fit: production, plant operations, facility management, technical services, energy and infrastructure.
Strong signals in a first conversation
- Many tickets are incomplete or require follow-up questions.
- Technicians lose time searching for history, manuals or spare parts.
- Downtime or waiting time occurs before the actual repair begins.
Check whether better work order preparation reduces downtime
In the intro call, we look at which maintenance cases occur frequently and which data sources can support productive preparation.
Book intro callConnectors
Connects to your existing business software
The AI employee does not replace your core systems. It works with ERP, CRM, tickets, documents, databases and collaboration tools.
Integration map
Where the agent works in your IT landscape
The AI employee connects intake, rules and core systems. That creates automation without replacing ERP, CRM or specialist software.
01
Inputs
- ERP
- Forms
- Documents
02
Digamma AI employee
- Check
- Match
- Summarize
- Route
03
Core systems
- ERP / CRM
- MDM / CMMS
- Tickets
- Databases
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What does it cost if this process stays manual?
Many operational AI projects do not pay back through big visions. They pay back through recurring minutes lost every month in checks, follow-ups and preparation.
Cases × minutes per case ÷ 60 × loaded hourly cost
Directional estimate, not a fixed savings claim. All values are calculated monthly figures. In the intro call, we replace these assumptions with your real volumes, cycle times and approval logic.
Manual process cost per month
€9,333
Calculated monthly values
The displayed amounts are estimated monthly costs based on your inputs.
Manual hours per month
133
Potentially relieved cost per month
€4,200
Check with real numbers
Book intro call