
AI-driven logistics & supply chain software development
Stop building tracking dashboards that humans have to monitor. We engineer AI-driven logistics platforms that connect your physical operations, telematics, ERP systems, and supply chain workflows into autonomous control systems.
Business challenges we address
As logistics operations grow, speed, coordination, and decision quality define profitability. Dispatching, warehouse execution, carrier management, and supply chain planning all depend on how well systems work together.
We develop custom logistics software that improves execution across fleet performance, warehouse efficiency, carrier reliability, and supply chain planning. The result is faster decisions, stronger margins, and a logistics environment built for scale.
Disconnected legacy systems and EDI bottlenecks
Our logistics solutions unify these environments through API architecture and semantic EDI middleware. Legacy EDI 204/214 flows, carrier systems, warehouse operations, and ERP data are translated into a single, structured operational layer, enabling leadership to make faster decisions, execute more cleanly, and maintain a single source of operational truth.
The driver shortage and retention crisis
Our AI-driven driver workflows account for Hours of Service (HOS), secure parking availability, dwell times, route profitability, and home-time planning. This improves route efficiency, supports driver productivity, and helps fleets operate more stably.
Freight fraud and double brokering defense
Our TMS integrations include behavioral AI and automated carrier vetting. Carrier activity, FMCSA authority changes, lane behavior, and onboarding patterns are continuously evaluated, helping dispatch teams work with verified partners and reduce disruption across carrier operations.
Autonomous supply chain control
AI Supply Chain Control Towers provide continuous monitoring of telematics, weather conditions, port congestion, warehouse constraints, supplier timelines, and inventory flows. This architecture supports automated rerouting, execution continuity, and more reliable supply chain performance across the network.
Custom logistics solutions we build
Autonomous supply chain control
AI supply chain control towers
AI Control Towers monitor telematics, weather, port congestion, supplier delays, warehouse constraints, and carrier performance in real time. When disruption risks arise, the system can automatically trigger rerouting, rebooking, dispatch updates, and ERP synchronization.
Digital twin of the supply chain
A Digital Twin creates a virtual model of your supply chain using ERP, telematics, and operational data. Teams can test disruptions like port strikes, supplier delays, or fuel spikes before they affect live operations.
Scope 3 emissions and carbon AI
Carbon-tracking pipelines process telematics, routing patterns, fuel usage, and carrier activity across the network, automatically generating audit-ready Scope 3 reporting.
Freight execution and transportation management
Agentic freight matching
AI agents process load requests, evaluate carriers, negotiate rates, verify compliance, and assign the optimal carrier with minimal manual work.
Freight management software
Shipment planning, carrier coordination, freight auditing, billing, and reporting are centralized within one streamlined execution flow.
Transit time optimization and management
Transit optimization combines traffic, warehouse readiness, delivery windows, and carrier availability to inform routing decisions in real time.
Last-mile delivery solutions
Dispatching, route planning, delivery tracking, notifications, and proof of delivery are handled in a single flow.
Warehouse, yard, and product integrity
Predictive warehouse operations
Machine learning models anticipate congestion points, optimize labor allocation, improve dock scheduling, and support faster inventory movement.
Automated yard management system (YMS)
Computer Vision-powered YMS uses OCR on gate cameras and yard devices to identify trailers, verify seal integrity, monitor movement, and prioritize yard operations.
Cold chain and predictive spoilage prevention
IoT monitoring and predictive models analyze refrigeration performance, voltage, and vibration telemetry to detect reefer failures before they occur.
Product integrity and damage control
Monitoring systems track handling conditions across storage and transportation, identifying risks before product quality is compromised.
Fleet performance and operational efficiency
Fleet management solutions
Vehicle health, maintenance schedules, fuel consumption, location tracking, and driver performance are managed within a single decision layer.
Fuel price volatility optimization
Optimization systems continuously analyze route efficiency, idle time, fuel usage, and carrier performance.
Driver activity monitoring
Driver monitoring tracks route behavior, dwell times, idle periods, operational efficiency, and Hours of Service compliance.
Revolutionize Your Logistics
Move beyond off-the-shelf limits. Create an innovative software platform designed for your unique needs.
We build a logistics system that improves itself
Logistics performance depends on how quickly operations turn data into action. Fleet movement, warehouse execution, delivery planning, and supplier coordination require systems that function as a single structure.
We build logistics environments where telematics, ERP, TMS, WMS, and operational workflows work as one decision layer. IoT, AI, and machine learning support live execution across the business and help the system grow with your business.
Internet of Things and telematics systems
Your fleet continuously generates the data that defines delivery speed, maintenance planning, and asset performance. Our telematics solutions create a single operational environment across transportation and warehouse operations, giving dispatchers, operators, and leadership full visibility into how assets move and perform.
Artificial intelligence and agentic workflows
Our AI solutions turn logistics operations into faster, more coordinated systems. AI works directly with ERP and TMS environments, processing live data and executing approved business logic within clearly defined operational rules.
Your teams get faster execution, stronger consistency, and fewer manual bottlenecks across daily logistics operations.
Big Data and ML
Our Big Data and machine learning solutions turn fragmented operational data into a continuous optimization layer across the supply chain. Planning, resource allocation, and performance management move into one continuous optimization process across the business.
With such a logistics environment, execution stays efficient, growth remains predictable, and resource allocation becomes significantly more precise.
Logistics software we developed
Start Your Transformation
The future of logistics is custom. Partner with us to build the software that will define your success.
Fleet-ready deployment roadmap
Replacing logistics infrastructure requires more than a standard software rollout. Dispatching must stay uninterrupted, telematics must remain stable, and warehouse operations must continue without delays across physical assets.
Our deployment model is built for operational continuity. Performance is validated before cutover, ROI is proven before scale, and rollout happens in controlled stages without disrupting the business.
The first step is an audit of your operational environment – core systems, warehouse workflows, and critical integration points across the supply chain.
During this stage, we identify where operational inefficiencies create delays, increase manual work, and reduce delivery performance. We also define baseline KPIs so every technical decision directly supports measurable business outcomes and margin improvement.
At the end of this phase, you receive:
- A system blueprint
- Integration architecture
- Rollout priorities
- A clear ROI model
Before replacing operational workflows, the new system runs in parallel with your existing process. AI-driven planning and dispatching work alongside human teams while live operations continue without interruption. This allows us to validate execution reliability and measure real operational impact under production conditions.
Leadership receives clear proof of cost savings and performance improvements before production cutover, along with go/no-go criteria for scaling the solution across the business.
This stage gives leadership measurable proof before investment decisions move to full-scale rollout.
We devote the third phase to field testing with real devices.
System behavior is tested against real field hardware and warehouse infrastructure to ensure stable system performance under real field conditions. After validation, rollout moves in controlled stages across locations, fleets, and operational teams.
Deployment includes:
- A controlled rollout
- Team enablement and educating
- Post-launch operational support
The result of our three-phase deployment process is a production-ready platform deployed without operational disruption, downtime risk, or uncontrolled scaling costs.
Frequently asked questions
How do you prevent an AI routing algorithm from “hallucinating” an impossible delivery route?
We never use Generative AI to draw a physical map. We engineer a Dual-Engine Routing Architecture. We use strict, deterministic heuristic algorithms (like Dijkstra’s or Reinforcement Learning models) to calculate the physical route based on weight limits and bridge heights. The LLM is only used as the “Translation Agent” to summarize the route output and communicate exceptions to the customer.
Can we deploy predictive maintenance AI to our fleet if our trucks frequently drive through “dead zones” with zero cellular connectivity?
Yes. We engineer Offline-First Edge AI Telematics. The predictive machine learning models are compressed (TinyML) and installed directly on the truck’s onboard IoT gateway. The AI continuously monitors engine CAN bus data (like oil pressure and vibration) locally. If an anomaly is detected, it triggers an instant dashboard warning for the driver and syncs the telemetry to the cloud later when the truck enters a Wi-Fi or 5G zone.
How do we safely integrate Agentic AI workflows with our legacy on-premises ERP (e.g., SAP or Oracle)?
We do not rely on fragile screen scraping or direct database injection. We engineer Zero-Trust API Abstraction Layers. We build secure middleware that sits in front of your legacy ERP. The AI agents interact only with this middleware using strictly formatted webhooks and REST APIs. This ensures the AI can draft purchase orders and update inventory without ever gaining direct, dangerous access to your core SQL databases.
Can AI help us consolidate LTL (Less-Than-Truckload) shipments into multi-stop FTL (Full Truckload) runs automatically?
Yes. Manual LTL consolidation leaves a massive volume of space empty. We engineer 3D Volumetric AI Consolidation algorithms. The system ingests the dimensions, weight, and delivery windows of thousands of LTL pallets. It uses complex spatial-packing mathematics to build multi-stop FTL routes autonomously, maximizing trailer utilization and slashing your linehaul costs.
How do you handle the massive latency of processing telematics from 10,000 trucks simultaneously?
Sending millions of IoT pings to a central cloud database every second will crash standard applications and inflate AWS costs. We use Distributed Stream Processing Architectures (such as Apache Flink). This allows our systems to process, filter, and analyze the high-velocity fleet data in-stream (in memory) before it even hits the database, ensuring your AI Control Tower updates in sub-second real-time.
Cost factors of custom logistics software
Project complexity
The major portion of the price depends on a project’s volume and complexity. Since transportation and logistics solutions vary from a single mobile application for couriers to an enterprise platform combining TMS, WMS, last-mile, and analytical dashboards, we discuss each business idea individually with a Client to offer the best quality-price ratio.
The required number of roles and scenarios, the need for real-time operation, data volumes, and non-functional requirements, such as fault tolerance, SLA, security, compliance with standards (GDPR, ELD, etc.), increase the project complexity and its cost, respectively. The more complex the architecture and algorithms, the more development, testing, and integration circuits are required.
UI/UX design
The cost of the UX/UI design portion of the project depends on the labor intensity that the frontend requires.
We need to take into account the number of screens for different roles (dispatcher, driver, storekeeper, client), interactive maps, timelines, offline mode, barcode/RFID scanning, accessibility, and localization. A design system and prototyping with usability tests also require sufficient labor, but they help in the future to reduce errors.
Third-party integrations
The more integrations you need, the more complex the architecture might be, or just the time needed to configure everything properly.
This includes connecting ERPs, CRMs, carriers, marketplaces, telematics, OBD, cartography, and payment and tax providers. All these add value due to different API maturity, quota restrictions, format differences, certifications, and transition from sandbox to production.
Reliable integrations require queues, retries, idempotency, monitoring, and alerts, meaning sufficient time and effort for development and testing.
Team composition
The composition and seniority of the team directly affect the budget and speed. Not only developers are needed, but also a business analyst with experience in logistics, UI/UX, QA (including autotests), DevOps/Cloud, and sometimes data/ML specialists. A large team speeds up releases, but increases budget “burning” and the synchronization load; a small one is cheaper, but takes longer.
The optimal core is 4–6 people with the necessary competencies and transparent management.
Post-launch support
We recommend that you do not neglect post-release support. It includes monitoring, alerts, security patches, dependency updates, response to changes in third-party APIs, minor improvements, infrastructure maintenance (cloud, networks, storage), and SLA reporting. It’s also valuable to get post-launch user training and development according to the roadmap.
Budget support in advance; it’s sensible for your product evolution.
Project complexity
The major portion of the price depends on a project’s volume and complexity. Since transportation and logistics solutions vary from a single mobile application for couriers to an enterprise platform combining TMS, WMS, last-mile, and analytical dashboards, we discuss each business idea individually with a Client to offer the best quality-price ratio.
The required number of roles and scenarios, the need for real-time operation, data volumes, and non-functional requirements, such as fault tolerance, SLA, security, compliance with standards (GDPR, ELD, etc.), increase the project complexity and its cost, respectively. The more complex the architecture and algorithms, the more development, testing, and integration circuits are required.
UI/UX design
The cost of the UX/UI design portion of the project depends on the labor intensity that the frontend requires.
We need to take into account the number of screens for different roles (dispatcher, driver, storekeeper, client), interactive maps, timelines, offline mode, barcode/RFID scanning, accessibility, and localization. A design system and prototyping with usability tests also require sufficient labor, but they help in the future to reduce errors.
Third-party integrations
The more integrations you need, the more complex the architecture might be, or just the time needed to configure everything properly.
This includes connecting ERPs, CRMs, carriers, marketplaces, telematics, OBD, cartography, and payment and tax providers. All these add value due to different API maturity, quota restrictions, format differences, certifications, and transition from sandbox to production.
Reliable integrations require queues, retries, idempotency, monitoring, and alerts, meaning sufficient time and effort for development and testing.
Team composition
The composition and seniority of the team directly affect the budget and speed. Not only developers are needed, but also a business analyst with experience in logistics, UI/UX, QA (including autotests), DevOps/Cloud, and sometimes data/ML specialists. A large team speeds up releases, but increases budget “burning” and the synchronization load; a small one is cheaper, but takes longer.
The optimal core is 4–6 people with the necessary competencies and transparent management.
Post-launch support
We recommend that you do not neglect post-release support. It includes monitoring, alerts, security patches, dependency updates, response to changes in third-party APIs, minor improvements, infrastructure maintenance (cloud, networks, storage), and SLA reporting. It’s also valuable to get post-launch user training and development according to the roadmap.
Budget support in advance; it’s sensible for your product evolution.
Why logistics operators trust SumatoSoft
ROI-first architecture
Every project starts with business economics. We define KPIs such as cost-per-mile, OTIF, fleet utilization, empty miles, dock dwell time, and spoilage loss before development begins. This allows leadership to see clear ROI, controlled TCO, and measurable business impact.
98% of our Clients are satisfied with our delivery quality, and 70% return with another project.
Legacy EDI to AI translation
Your 3PL partners still use EDI 204s and 214s. AI uses structured APIs.
We build semantic EDI middleware that converts legacy flat files into structured JSON and routes them into production-ready business systems and vector databases. This makes decades of logistics infrastructure usable for automation, analytics, and AI without replacing your partner ecosystem.
Fleet-ready deployment approach
We use shadow pilots, hardware-in-the-loop testing, and phased rollout before production cutover. AI routing runs in parallel with dispatchers first, predictive maintenance is tested on real telematics hardware, and warehouse automation is validated in live operations.
Enterprise-grade security and system reliability
We use ISO 9001- and ISO 27001-aligned processes, role-based access control, encrypted data flows, audit trails, and a zero-trust API architecture. AI agents never have direct access to the ERP database. All sensitive actions move through controlled middleware.
With our custom logistics software development, your operations stay protected, and compliance risk is minimal.
Awards & Recognitions
Let’s start
If you have any questions, email us info@sumatosoft.com



























