Tepia adds AI route planning and ETA prediction to the TMS, dispatch and driver apps logistics teams already run. Routes are built with optimization solvers that respect time windows, vehicle capacity and hours of service, and arrival estimates come from models trained on your own stop and dwell history.
Better routes start the day right, but accurate arrival times are what customers and dock managers actually notice.
Tepia builds routes with optimization solvers that respect time windows, vehicle capacity and hours of service rules.
Read: Route Optimization Software Development
Routes likely to miss a delivery window are flagged early enough for dispatch to call the customer or move a stop.
Read: Telematics Integration with Samsara, Geotab and Motive
Arrival estimates learn how long each customer site actually takes to unload, using your own completed stop history.
Read: Logistics and Transportation App Development
Tepia replays past weeks of orders through the planner and models so you see results before any live route changes.
Industry figures Tepia plans around when scoping logistics AI.
The American Trucking Associations reports trucks move about 72 percent of US freight by weight, so road routes carry most loads.
ATRI's 2025 operational report found empty miles rose to an average of 16.7 percent of truck miles, room that better planning can win back.
ATRI found truck drivers reported being detained at 39 percent of all stops in 2023, which is why dwell aware ETAs matter.
A route plan looks good at six in the morning and starts drifting by nine. A receiver makes a driver wait forty minutes at the dock. A customer adds a pickup. A truck goes down. The plan was built on average drive times and a fixed fifteen minutes per stop, and none of that reflects how your actual customers behave.
Tepia’s logistics AI work starts from that gap between the plan and the day. Much of the delay in regional delivery is not driving, it is time at the stop: finding parking downtown, waiting for a receiving clerk, checking in at a guard shack, unloading by hand through a restaurant’s narrow back door. Those times are consistent per location, and your delivery history already records them if the driver app captures arrival and departure.
AI helps in three places: building better plans from realistic stop times, predicting arrival times that update as the day unfolds, and flagging routes likely to miss a window early enough to act on it.
Tepia matches each problem to the method that answers it best, and only some of them call for a language model.
| Problem | Method | Output your team sees |
|---|---|---|
| Sequencing stops for each truck | Vehicle routing solver with time windows, capacity and driver hours | Ordered routes with a planned arrival per stop |
| Estimating time at each stop | Prediction model trained on dwell history per location | Site specific service times feeding the plan |
| Updating arrival estimates | Model combining live GPS, traffic and remaining stops | ETA per stop that refreshes through the day |
| Spotting late risk | Classifier trained on past late deliveries | Alerts on routes likely to miss a window |
| Reading orders and instructions | Language model extracting fields from emails and PDFs | Clean orders with delivery notes and windows |
| Answering where is my shipment | Language model calling the tracking API | Customer replies with the current ETA |
Tepia keeps routing itself on a deterministic solver, not a language model. Given the same orders and constraints, the solver returns the same plan, which dispatchers and auditors can check. Language models handle the messy text around the plan. For the routing engine on its own, see route optimization software development.
Mapping services give drive time between two points. They do not know that one grocery distribution center makes carriers wait, or that a downtown customer always takes twice as long on Mondays. Tepia builds ETA models that learn those patterns from your completed stops: location, day of week, time of day, order size, vehicle type and driver.
Accuracy is measured against actual arrivals every week, by region and customer, and the model is retrained when it drifts. Tepia also captures the delay reasons drivers log in the app, which explains errors the data alone cannot.
Tepia adds AI to the stack you have rather than replacing it. Typical connections include:
When a client runs a homegrown dispatch tool, Tepia exposes the planner and ETA service as APIs the tool calls, so dispatchers keep the screens they know and the new logic arrives behind them.
Typically Discovery runs 1 to 3 months, development and QA 2 to 6 months, and launch about a month. Replay testing lets you see results before any live route changes. More on our process.
Tepia agrees on measures with you up front: planned versus actual miles, stops per route, on time arrivals within the promised window, average ETA error, and the share of late deliveries flagged before they happened. Those numbers come from data you already collect once the driver app records arrivals properly.
Routing products built for delivery fleets do a good job of sequencing stops, and if your operation fits their model, adopting one may be the faster path. A custom build makes sense when your constraints are unusual, such as mixed pickup and delivery, multi day routes, hazmat or temperature rules, or customer specific receiving windows, or when you want ETA predictions inside your own customer portal. See logistics app development, review our work, or contact Tepia.
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