An OEM's after-sales network generates more signals than any regional team can read: contracts lapsing, claims ageing, parts pooling in the wrong depots, one dealer's response times quietly degrading. The doctrine here is deterministic signals, AI delivery, human approval — engines watch everything, agents deliver what matters, people decide.
Engines that run without being asked
- Preventive jobs generate themselves — maintenance schedules and contract campaigns create dealer jobs and reminder leads for every asset falling due, wave by wave.
- Claims assemble from dealer job cards — the claims engine sweeps completed covered work into claims with evidence on the configured schedule.
- Parts replenishment projects ahead — suggested lines from consumption and lead time, per depot and dealer, with the arithmetic on the line.
- Nudges publish overnight — lapsing contracts, ageing claims, dormant dealers, overdue receivables — scheduled sweeps put them in the right person's queue with evidence attached.
Agents that watch the network
Sense answers network questions in plain language, scoped to the asker's permissions: "which dealers' claims get rejected most, and why?", "which territories' contract renewals are pacing behind?", "where is batch-affected stock right now?" It drafts the dealer nudges, the renewal outreach, the transfer proposals — and every write waits for a human approval. The daily brief gives each network manager their territory's numbers and ranked nudges at open of business.
Your agents, your model, your rules
With AI Studio, the OEM builds its own agents — grounded in service bulletins and network knowledge, bound to execution accounts that set their read ceiling, metered by budgets enforced before the model call, fully audited. Scheduled agent workflows can watch the installed base and publish suggestions autonomously; acting stays a human click. Governance in full: AI trust.
Common questions
What runs automatically across a dealer network?
The deterministic machinery: preventive-job and reminder-lead generation from contracts and schedules, campaign waves across the installed base, claim assembly from completed jobs, parts replenishment suggestions, and scheduled nudge sweeps — all landing in human queues.
Can agents see across companies — OEM and dealers?
Agents run under the asking user's permissions on the shared data model, so an OEM network manager sees the network view their role allows while a dealer's users see their own operation — the same governance that applies to every screen applies to every agent.
How would an agent help with claim quality?
By reading claim lines and job-card attachments it can surface the pattern — rejections concentrated in two dealers, 70% missing diagnostic photos — and draft the dealer nudges for approval, before the quarter's settlement turns it into a dispute.
Is any of this a black box?
No — deterministic engines show their inputs on every suggested line, agent answers cite the records they read, budgets are enforced before model calls, and every agent action is audited.