Route optimization software
Plans multi-stop routes around time windows, vehicle capacity, driver hours and zone rules, and replans when orders change.
Custom AI · Logistics
Custom Logistics AI Development for Routing, Forecasting & Warehouses
Route optimization, demand forecasting, warehouse AI and logistics tech MVPs, built on your own data and constraints and delivered as software your planners will use.
Overview
Custom logistics AI development for problems off-the-shelf tools miss
Some logistics problems don't fit a packaged tool. Your delivery zones have rules a generic route planner ignores. Your demand swings with Ramadan, harvest seasons or one large customer's promotions. Your warehouse has a layout and a product mix no slotting module was configured for. Custom logistics AI development builds models around your own data and constraints, then wraps them in software your planners, dispatchers and warehouse staff actually use.
We build route optimization software that accounts for delivery windows, vehicle capacity, driver shifts, COD collection limits and real road conditions from Google Maps Platform. We build AI demand forecasting models that learn from order history, promotions, seasonality and supplier lead times, and show planners why a forecast moved. For warehouses, warehouse AI suggests slotting so fast movers sit close to dispatch, predicts pick volumes for shift planning and, with computer vision, can flag damaged cartons or count stock from photos.
Our closest shipped work is outside logistics, and the engineering transfers directly. Medsuccour is a deep-learning computer-vision model that went from concept to working MVP in about twelve weeks. Vikk is a multi-agent platform built on RAG that serves more than 120,000 users. IWMCRM, our AI-powered CRM for pharma distribution, includes predictive analytics on vendor and product data, which is the same kind of modelling a distributor's replenishment depends on.
Our supply chain AI software development covers the full path: data assessment, model choice, backtesting against your own history, the user interface, integration with your TMS, WMS and ERP, and deployment in your own cloud. You keep 100% of the code and trained models.
Use cases
Custom AI Development use cases in Logistics
AI demand forecasting model
Forecasts demand by SKU, location and week from order history, promotions and seasonality, so buyers order with confidence.
Warehouse AI for slotting and labor
Recommends product placement by pick frequency and predicts daily pick volumes for shift planning.
ETA prediction
Predicts arrival times from telematics, traffic, dwell history and lane patterns, more reliably than distance divided by speed.
Freight rate prediction
Estimates lane rates from your historical quotes and bookings so your pricing desk responds faster and more consistently.
Computer vision for damage and counts
Flags damaged cartons, checks labels and counts stock from photos taken at receiving or loading.
How it works
How it works
- 01Problem and data assessmentWe define the decision to improve and check what order, shipment, telematics and warehouse data exists and how clean it is.
- 02Model approachWe choose between optimization solvers, machine-learning models, LLM agents or a combination, depending on the problem.
- 03Training and backtestingModels are tested against your past weeks and months, so you see how they would have performed before anyone relies on them.
- 04Product and integrationWe build the planner interface and connect it to your TMS, WMS, ERP and telematics.
- 05Deployment and monitoringSecure deployment in your cloud, with tracking of forecast accuracy, plan adherence and drift.
Capabilities
Key features
Optimization and machine learning
Solvers for routing and allocation, combined with models that predict demand, ETAs and rates.
Planner overrides
Planners can lock stops, move orders or change a forecast, and the system respects their decision.
Explainable outputs
Every plan and forecast shows the main drivers behind it, so your team can trust or challenge it.
Computer vision and LLMs
Image models for inspections and counts, and language models for documents and exception handling.
Full-stack delivery
Model, backend, web or mobile app and integrations, delivered by one team.
100% IP ownership
You own the code, the models and the trained weights.
Integrations
Works with your Logistics stack
- Google Maps Platform
- Google OR-Tools
- Samsara
- Motive
- SAP S/4HANA
- Manhattan WMS
- PyTorch
- Google Vertex AI
- AWS
- Azure
- Google Cloud
Using something else? If it has an API, a database or a webhook, we can connect to it.
Outcomes
Benefits for Logistics teams
Plans that fit your operation
Models built on your own lanes, zones and constraints produce routes and forecasts your planners actually follow.
Fewer kilometres and stockouts
Better routes and forecasts cut empty running, expediting and excess stock.
A product you can sell
Logistics tech startups get an MVP built to production standards, not a throwaway demo.
No per-seat fees on your core
Owning the AI at the center of your operation avoids paying rent on it forever.
Our work
Related Work
Custom AI Model Development2024
Medsuccour AI: Revolutionizing CT Scan Diagnosis
Medsuccour is an AI-powered project designed to revolutionize the diagnosis of traumatic brain injuries through rapid and accurate analysis of CT scans. Given that every minute counts after a head trauma, our model drastically reduces the time required to detect and localize brain injuries, generating detailed reports in seconds.
View case study →
AI Chatbot2024
Vikk AI: AI-Powered Legal Assistant
Vikk AI is a 24/7 AI-powered legal assistant that gives users instant, private legal guidance, analyzes their documents, and connects them with verified lawyers when needed. We helped build a conversational platform that delivers state-specific answers across dozens of legal categories and languages.
View case study →
FAQ
Custom AI Development for Logistics: FAQs
Can you build route optimization software for our fleet?
Yes. We build routing engines that handle your time windows, vehicle types, driver hours and zone rules, with an interface for dispatchers and a driver-facing app if you need one.
How much data do we need for an AI demand forecasting model?
It depends on how stable your demand is. A year or more of order history is a good start, and we assess what you have before recommending an approach. Sparse items can be forecast at a group level.
Should we build custom AI or buy a TMS or WMS module?
It depends. If a packaged module fits your operation, buy it. Custom makes sense when your constraints, data or product are what set you apart, and we'll tell you honestly which side you're on.
Can warehouse AI work with our existing WMS?
Yes, in most cases. We read stock, location and pick data from your WMS through its API or database exports and push slotting or staffing recommendations back to your team.
Do you build logistics tech MVPs for startups?
Yes. We take logistics AI ideas such as freight marketplaces, visibility tools or dispatch apps to a working MVP with the model, app and backend included.
Who owns the models and code?
You do. You keep 100% of the intellectual property, including trained model weights and the data pipelines.
How long does custom logistics AI development take?
It depends on scope. Our Medsuccour model took about twelve weeks to a working MVP and the Vikk MVP took six. We set milestones after a short discovery phase.
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Further reading
Custom AI Development for other industries
Plan with your data, not last year's averages.
Bring the routing, forecasting or warehouse problem and the data you have. We'll tell you what's feasible and what it will deliver.