Ask a towing controller what their busiest hour looks like and you won't hear about route planning. You'll hear about the phone: a breakdown on the A-road needing the flatbed that's already out, a police contract demanding a 45-minute response, a customer wanting to know where the truck is, and a garage closing at five. The trucks are findable and the jobs are known. What's scarce is the attention needed to turn one into the other, fast, over and over.
That's the gap vehicle towing operations run into with software. Tracking tools tell you where every recovery vehicle is. Dispatch tools help a person assign jobs. Neither actually absorbs the work — the calls, the rearranging, the follow-ups — that fills a controller's day.
What changes when dispatch runs itself
Finmile Autonomy puts AI workers into a towing operation to do that work, not just describe it. A worker has an objective (keep response times inside contract while using the fewest vehicles), approved tools and data, and a defined level of authority. It runs a continuous loop — understand the incoming request, investigate across your systems, decide, act, follow through — and it's judged by jobs completed correctly, not by how polished its answers are.
In a recovery operation, the loop is busy from the first call:
- A job comes in. The worker reads the vehicle type needed, the location, the contract's response window, and which trucks are free. It assigns, briefs the driver, and logs the decision — no controller touching the board for a routine recovery.
- The day shifts. A flatbed gets held at a garage past its slot. The worker sees the delay forming, works out which waiting jobs it puts at risk, and either protects the contractual ones first or moves jobs between vehicles to hold the line.
- A recovery completes. The driver captures proof of recovery — photos, signature, condition notes — and the worker closes the job, updates the record in your system, and can trigger the invoice rather than leaving the paperwork for the evening.
The controller's role changes shape. Instead of executing every assignment, they supervise: they set the authority levels, handle the jobs the worker escalates, and step in where judgement genuinely belongs. The routine flows past them.
Tracking that leads somewhere
Most tracking software ends at the screen. You can see the truck is delayed — and then a person picks up the phone to explain it, again, to the customer, the garage, and the account manager.
An AI worker uses the same signal as a starting point instead of an endpoint. A delayed vehicle means recalculated ETAs pushed to everyone waiting on that truck, a reshuffled afternoon for the affected jobs, and a note on the record explaining what changed and why. The visibility you already pay for becomes the trigger for action rather than a queue of things a human still has to do.
Every action sits in an audit trail — the trigger, the sources checked, the communications sent, the decision, the outcome. When a police contract or an insurance client asks three weeks later how a recovery was handled, the answer is a record, not a reconstruction.
Built on top of what you run today
Towing operations live on their TMS, their CRM, and a set of habits nobody wrote down. Finmile doesn't ask you to replace any of it. Workers connect to your existing systems — by API and webhook where available, otherwise through approved databases, files, and controlled interfaces — and work inside them the way a trained controller would: pulling the job in, updating the status, sending the confirmation. No rip-and-replace is the whole design point; it's why the first workflow can be live in 30 days instead of after a migration project.
That first workflow should be the one that hurts most. For most recovery fleets it's either job assignment during peak hours or status communication with contract customers. One AI worker. One workflow. — proven on the real operation, then widened from there.
For operators who'd rather consolidate everything onto one platform — job intake, dispatch, tracking, invoicing, customer portal — Finmile OS is the full operating system for the physical operation, with Autonomy built in. It's an option, not a step you're forced through. Most towing operations start with the layer.
Response times are a contract, not an aspiration
Recovery work is unforgiving in a way general couriering isn't. Police, council, and insurance contracts carry hard response windows; miss them and the contract itself is at risk. That's precisely the kind of constraint an AI worker is good at holding — because it checks the clock on every decision, every time, rather than when someone remembers to look.
Finmile was built by a team that ran their own delivery network and built the tool the operation actually needed: workers that follow each case through to confirmed done. If your control room is the bottleneck on how much work the fleet can absorb, that's the place to start — one workflow, one worker, and a month to see whether the phones go quiet.
Frequently asked questions
Does this replace our towing management system? No. Workers operate across the systems you already run — TMS, CRM, job boards — reading and updating them under permissions you define. If you later want a single end-to-end platform, Finmile OS covers intake through invoicing, but nothing forces that move.
How much can an AI worker actually decide? You set it, action by action: Assisted (it recommends, a person confirms), Supervised (it acts within boundaries, defined decisions come to a human), or Autonomous (it owns the outcome, exceptions escalate). Authority is granted, never self-assigned, and every worker has financial and operational limits it can't exceed.
What about proof of recovery and invoicing? Drivers capture photos, signatures, and condition notes on completion. The worker attaches the evidence, closes the job in your system, and can start the invoice — the case stays open until the proof is actually there.
How quickly can this be running? When system access, ownership, data, and decision rules are in place, a first workflow goes live in 30 days.
Is this for large fleets only? It's built for mid-market and enterprise physical operations. The fit question is decision volume, not fleet size — if controllers are the constraint on growth, the work is there to automate.