We build connected car and telematics platforms, ADAS and computer-vision data pipelines, predictive maintenance systems, and dealer management integrations engineered to the reliability bar a moving vehicle actually requires — not a demo that only works in the showroom.
Automotive software development is the engineering of the software layer that sits on top of (and increasingly defines) a vehicle program — connected car and telematics platforms, ADAS and computer-vision systems, predictive maintenance built on IoT sensor data, in-vehicle infotainment and AI voice assistants, EV and battery management software, and the dealer management (DMS) systems that run the sales and service side of the business. It spans cloud platforms, mobile apps, and the data pipelines that connect them to a moving vehicle.
From a focused telematics dashboard to a full connected-vehicle platform with ADAS data pipelines and predictive maintenance, we scope and build the exact system your program needs.
ADAS is only as good as the data pipeline and models processing what the sensors see — here's where our engineering depth actually lives.
Camera, radar, and lidar sensor-fusion pipelines that turn raw signals into usable object and lane data.
Computer-vision models for real-time hazard detection, pedestrian recognition, and driver-monitoring fatigue alerts.
Safety-conscious system architecture, informed by ISO 26262 and ISO/SAE 21434 practices, from the first design decision.
Edge inference pipelines that keep latency-sensitive detection running on-device, not round-tripped to the cloud.
From predictive maintenance to computer-vision driver monitoring, AI has moved from a differentiator to a baseline expectation across connected vehicle programs. Here's where we apply it in the platforms we build.
AI models flag developing brake, battery, or engine faults using sensor telemetry before they cause a breakdown.
Computer vision and sensor fusion — camera, radar, lidar — for real-time lane-departure and collision-hazard detection.
Cabin-camera pipelines detect driver fatigue and distraction through facial and gaze analysis, not a simple seatbelt sensor.
Natural language processing gives drivers hands-free control of navigation, climate, and media without touching a screen.
Seat position, climate, and media preferences applied automatically the moment a recognized driver gets in.
Vehicle-to-infrastructure signal exchange feeding fleet routing and traffic-aware navigation decisions in real time.
A fleet dashboard is only useful if the data behind it is current to the second — here's what that requires under the hood.
Sub-second latency vehicle location, speed, and route-replay tracking across an entire fleet.
GPS geofencing with automated alerts the moment a vehicle enters or leaves an assigned zone.
Driver safety scorecards built from harsh-braking, speeding, and idle-time telemetry.
Remote diagnostics and DTC fault-code triage surfaced to fleet managers before a driver notices a problem.
We haven't shipped a public automotive-industry case study yet — but the real-time tracking, sensor-driven data, and safety-conscious engineering behind these platforms is exactly what a connected-vehicle program needs.
A logistics marketplace connecting shippers and businesses with reliable, secure cargo carriers — real-time booking, tracking, and carrier discovery.
Digital engineering support for Piper Aircraft, a general aviation manufacturer, spanning its aircraft showcase and owner-facing digital experience.
A SOC analyst dashboard for cyber security protection through deception — real-time alert triage, customer and analyst management, and role-based incident workflows.
“Movesy has completely transformed the moving and delivery industry thanks to Apptechies. The GPS tracking, dynamic pricing, and route optimisation they built has made our operations incredibly efficient.”
Predictive maintenance models trained on your own sensor and service history — flagging brake, battery, and engine wear early enough to schedule around it, not react to it.
We run every automotive build through the same structured process as the rest of our work — described in full on our How We Work page.
Mapping your fleet, existing telematics hardware, and integration points before scoping a single feature.
Data architecture for sensor ingestion and safety-conscious system design decided upfront, not bolted on later.
Interfaces built for a dashboard glance at 60mph or a fleet manager scanning 500 vehicles at once.
Working software in testable increments, reviewed against real telemetry and edge cases as we go.
Tested against dropped-connection scenarios and real sensor data, not just the happy path.
Rollout planned around your fleet or dealership operations, not our sprint calendar.
Monitoring, iteration, and support that continues as your fleet and feature set grow.
Sensor networks for telematics and predictive maintenance.
ADAS, object detection, and driver-monitoring models.
Predictive maintenance and demand-forecasting models.
Turn fleet and vehicle telemetry into decisions.
Fleet, dispatch, and delivery coordination platforms.
EV charging, battery, and range-management software.
Purpose-built platforms for how your program actually runs.
Scalable infrastructure for vehicle telemetry at fleet scale.
Driver, technician, and fleet-manager companion apps.
Connect telematics hardware, DMS, and third-party data.
Dedicated engineers for predictive-maintenance models.
End-to-end engineering talent, flexibly engaged.
Answers to the questions fleet operators, dealerships, and mobility teams ask us most — and a direct line to our team if you don't see yours.
It spans connected car and telematics platforms, ADAS and computer-vision systems, predictive maintenance built on IoT sensor data, in-vehicle infotainment and AI voice assistants, EV and battery management software, and dealer management system (DMS) integration.
A focused telematics dashboard or dealer-integration project typically runs $60,000–$140,000, while a full connected-vehicle platform with ADAS data pipelines and predictive maintenance can run $200,000–$500,000+ depending on scope.
A focused telematics or fleet dashboard MVP typically takes 12–16 weeks. A full connected-vehicle platform with ADAS integration and predictive maintenance modeling usually runs 7–10 months.
Yes. We regularly ingest data from OBD-II dongles, CAN bus gateways, and third-party telematics control units, normalizing raw vehicle signals into a usable data layer rather than requiring new hardware.
We build the software layer around ADAS: camera and sensor data pipelines, computer-vision models for object and driver-monitoring detection, and the fleet-facing dashboards that consume that data. For automotive-grade embedded firmware certification, we partner with or defer to specialized hardware-safety vendors.
Yes. Our models are trained on your own sensor and maintenance-history data — engine, brake, and battery telemetry — to flag developing faults before they cause a breakdown, rather than relying on a generic fixed-interval schedule.
Yes. We regularly integrate new platforms with existing DMS, CRM, and inventory systems rather than requiring a dealership to replace its core systems to get modern software on top.
Tell us about your fleet, dealership, or connected-vehicle idea — we'll respond within 24 hours with real next steps, not a sales script.
