Predictive Car Care Agent

For

Small fleet operators with 5 to 20 vans and remote workers who drive older cars

Solves

Car maintenance hits you with surprise bills and wasted mornings.

Delivers

Scheduled maintenance bookings, part orders and a digital maintenance log

Built in

about 3 weeks of creation time, MVP in 3 days

Investment

$5,500 for the MVP, $18,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For small fleet operators with 5 to 20 vans and remote workers who drive older cars, turn live vehicle data, service history and local parts inventory into scheduled maintenance bookings, part orders and a digital maintenance log.

1. Monitor live OBD-II codes and telemetry.
2. Cross-reference service history and parts inventory.
3. Predict upcoming maintenance needs.
4. Book appointments via integrated calendar.
5. Order parts from preferred suppliers.
6. Update maintenance log and adjust predictions.

What goes in, what comes out

The workflow

1. In
   Start with

   Live vehicle data, service history, local parts inventory

2. 1

   Connect vehicle

3. 2

   Read live data

4. 3

   Analyse fault codes and history

5. 4

   Flag predicted issues

6. 5

   Send plain-text alert

7. 6

   Book slot and order parts

8. 7

   Then file receipt and update log

9. Out
   Finish with

   Scheduled maintenance bookings, part orders and a digital maintenance log

AI does the heavy lifting, people stay in charge

Use language models to interpret alerts and generate plain-text messages, and predictive models to estimate part wear and failure windows. A human operator reviews all bookings and part orders before they are sent. Confirm diagnosis with deterministic thresholds and manual spot checks.

What your team sees

Key screens: Vehicle dashboard, booking calendar, parts order list. Use a vehicle dashboard showing fault codes and predicted issues, a booking calendar with confirmed slots and a parts order list with supplier status. Let users approve or cancel actions with one tap. Display alerts as plain sentences with a 'book' or 'ignore' button. Provide a history view of past repairs and invoices. In this product, the first view is vehicle dashboard, followed by booking calendar and parts order list.

Accounts and administration

Vehicle ownership, user roles, booking approvals, part order limits, spend caps, audit trail of all actions, and a permission setting for automatic versus manual booking.

Integrations and data access

OBD-II adapters, manufacturer APIs, Calendly, Stripe, email and SMS gateways, and parts supplier catalogues.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   3 days

   One buyer segment, one recurring use case; first modules: OBD-II code monitoring and calendar booking. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   4 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will small fleet operators with 5 to 20 vans and remote workers who drive older cars use it to solve "car maintenance hits you with surprise bills and wasted mornings"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Run a four-week paid pilot with five vans.
4. Measure, then decide. Track two measurable outcomes: reduction in unplanned maintenance hours per vehicle and and percentage of predicted issues booked without user intervention. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One car brand, one city, two trusted garages. First two modules: OBD-II code monitoring and calendar booking. Manual review of every alert and order before sending.

After the MVP. Automate parts ordering, voice memo intake, and multi-brand support after the paid pilot.

What the build depends on. Reliable OBD-II data access, stable manufacturer API terms, and a conservative prediction threshold to avoid false alerts.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at USD 19 per vehicle per month, half the average unexpected repair bill.

Message to test

Your car books its own mechanic and orders its own parts, so you never lose a morning to maintenance.

Where to find buyers

Fleet management forums, local garage newsletters, and social media groups for remote workers and van owners.

Lead magnet

A free one-week vehicle health report showing predicted issues and estimated costs.

The first 30 days

1. Week 1: Build OBD-II reader and alert parser.
2. Week 2: Connect calendar booking and SMS alerts.
3. Week 3: Add parts order draft and manual approval.
4. Week 4: Pilot with two garages and five vehicles, collect feedback.

Paid pilot

Run a four-week paid pilot with five vans. Baseline is average hours lost to maintenance and number of surprise repairs. Decision to expand if the agent cuts hours lost by half and books at least 80 percent of predicted maintenance without user follow-up.

Success metrics

Two measurable outcomes: reduction in unplanned maintenance hours per vehicle, and percentage of predicted issues booked without user intervention.

Retention and expansion

It keeps earning as a monthly subscription, with the agent continuously updating the maintenance log and prediction model, making the service more accurate and harder to leave.

Why clients would pick it

It gets harder to copy as the prediction model learns each vehicle's wear patterns and garage preferences, and as it accumulates approved service histories and supplier relationships.

Alternatives and positioning

People use manual reminders, garage phone calls and spreadsheets. This differs by automating the entire chain from detection to booking to payment.

Main delivery costs

Cloud inference, OBD-II adapter subsidies, calendar and payment API fees, and a human reviewer for initial alerts.

Limits on spend per vehicle, approval required for any booking over a set amount, no automatic part ordering without user consent, and a hard stop on driving alerts that could indicate immediate safety risk. It must not book emergency repairs or order parts without explicit approval.