Making vehicle repair faster and more predictable
Otter Work is an AI decision layer that diagnoses vehicles and predicts the spare parts a repair will need — before the truck reaches the workshop bay. Higher technician efficiency, higher throughput, less downtime.
Built for independent truck workshops and multi-location service networks across Europe.
Repair time is lost between diagnosis and parts availability
Workshops lose hours because parts are only sourced after diagnostics — while the bay and the technician sit blocked.
Ordered too late
Parts are ordered only after diagnostics are complete, so the wait starts when the truck is already on the lift.
Fragmented inventory
Stock is scattered across local warehouses, group stock and suppliers — with no single view of what is available when.
Manual search
Staff hunt across DMS, OEM portals, catalogs, email and phone calls to find and confirm the right part.
Active repair is only one part of the cycle
Two steps — parts search/order and parts wait — are where avoidable time hides.
The two highlighted steps are avoidable waiting. A day of truck downtime runs ≈ €500 — the pain that pays for prediction.
Breakdown → return to service
Check-in → release
Time in a productive bay
Active technician work
Measurement distinctions that matter. The 16-hour shop-TAT baseline is a pilot hypothesis, to be validated on timestamp-level workshop data.
An AI decision layer on top of the systems you already run
The AI does the heavy lifting; the human stays in control at the business-rule gate — and every finished repair feeds the model.
Capture
Vehicle, mileage, symptoms, DTCs and repair history — from phone, email or intake.
Predict
Likely required parts with confidence scores, normalised across manufacturers.
Check
Local, group and supplier availability with ABC-class lead times.
Reserve
Reserve or pre-order parts under defined business rules — human-approved.
Prepare
Stage the parts before the truck reaches the productive repair bay.
Learn
Learn from actual diagnosis and repair outcomes to sharpen the next prediction.
One connected workflow, from repair order to prepared parts
Digital repair-order intake
Structured capture of VIN, symptoms and history — even from a phone call.
Predictive parts recommendation
Confidence-scored part predictions from the prediction engine.
Inventory & supplier visibility
Local, group and supplier stock in a single availability view.
Reservation & procurement
Reserve, advance-order or find alternative sourcing under business rules.
Bay scheduling & staging
Schedule the bay and stage parts so work starts without a wait.
KPI dashboard & APIs
Operational metrics plus integration APIs into existing systems.
The workshop floor, end to end
From the service order board to the technician bay, parts availability and purchasing — one connected system.
Less waiting, more capacity, better predictability
Illustrative 4-bay workshop model — a value hypothesis for pilot validation.
88–132
Conservative to average
352–528
Per month
€21k–32k
At €60 / hour
€3.2k–4.8k
At 15% capacity realization
These figures are a value hypothesis for pilot validation, not a guaranteed outcome. Actual impact depends on repair mix, current utilization, parts availability, and the workshop's ability to convert released capacity into additional work.
The intelligence layer between fleet and workshop systems
Not another system of record — a predictive operating layer that makes existing systems more useful. And the advantage compounds into a data flywheel.
Simple pricing aligned with workshop size
Additional revenue: implementation and data preparation (€1,500–5,000 per workshop) · custom integrations · premium SLA and support · group-level analytics and benchmarking.
Otter Work makes repair faster, more predictable, and easier to scale
We are validating predictive parts availability in live workshops — reducing avoidable waiting, improving bay throughput, and building a scalable B2B SaaS platform for Europe's repair ecosystem.