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Solvios is the leading IT company in the business to give you growth, revenue, and a global platform.
We build custom logistics software from transportation management systems and warehouse management platforms to fleet management tools, last-mile delivery applications, freight and shipment tracking portals, and AI-integrated supply chain intelligence for logistics companies, 3PLs, freight brokers, carriers, and supply chain operators that need software built around how their operation actually runs, not around how a generic logistics platform assumes it should. Logistics software lives in an environment where downtime means trucks waiting at docks, deliveries failing SLAs, and customers calling for exceptions that could have been prevented. We build for that environment.
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Solvios is a logistics software development company building custom transportation management systems, warehouse management platforms, fleet and driver management tools, last-mile delivery applications, freight tracking portals, freight brokerage platforms, and AI-integrated supply chain visibility and analytics for logistics companies, third-party logistics providers, freight brokers, carriers, and supply chain operators. We work across the full logistics technology stack from driver-facing mobile applications and warehouse floor tools through dispatch management systems and executive supply chain dashboards with real-time data architecture, IoT integration depth, and AI-integrated operations intelligence built into every engagement.
Custom TMS platforms covering load planning, carrier selection, shipment execution, freight audit, and the supply chain visibility that shippers, brokers, and 3PLs need to manage transportation spend and service performance across a complex carrier network.
Custom WMS platforms for warehouse and distribution centre operations covering inbound receiving, putaway, inventory management, pick and pack, outbound shipping, and the real-time stock visibility that drives fulfilment accuracy and throughput.
Fleet tracking, driver management, vehicle maintenance scheduling, compliance and HOS monitoring, fuel consumption tracking, and the operational dashboards that give fleet operators visibility into where every asset is and what it is costing to run.
End-to-end last-mile delivery management covering order dispatch, route optimisation, driver mobile applications, real-time delivery tracking, proof of delivery, and the customer notification infrastructure that reduces inbound enquiries and improves delivery satisfaction.
Shipment tracking portals, supply chain visibility platforms, and carrier performance dashboards that give shippers, consignees, and operations teams real-time visibility into shipment status across carriers, modes, and geographies without manual status chasing.
Logistics software has a failure mode that most other software categories do not share: the cost of a system failure is not measured in user inconvenience but in operational disruption that cascades immediately into delayed shipments, missed SLAs, idle assets, and phone calls from customers who needed their freight to arrive and did not. We have built in this category and understand the operational stakes that logistics software has to be designed for.
Most logistics operations have reasonable visibility into their own facilities and assets and very little visibility into what happens once a shipment moves outside their direct control at a carrier partner, in a port, at a customer's receiving dock. The status update that a customer needs, where is my shipment, when will it arrive, why is it delayed, requires manual status chasing from the operations team when there is no integration between the logistics management system and the carrier or partner systems that actually hold the data. Every manual status call is a cost, and every status call that arrives after the customer has already noticed the problem is a service failure.
Static route planning that was set up when the operation was smaller does not reflect the traffic patterns, delivery time windows, driver HOS constraints, vehicle capacity variations, and dynamic customer requirements of a growing last-mile operation. Dispatchers making routing decisions manually, or on tools that do not incorporate real-time traffic and delivery constraint data, are leaving route efficiency on the table. In a last-mile operation where fuel and driver labour are the primary variable costs, route inefficiency is a direct margin problem not a planning inconvenience.
Fleet operators are typically running vehicle tracking in one system, driver compliance and HOS in another, vehicle maintenance scheduling in a spreadsheet, fuel card data in a separate portal, and driver performance data in whatever the transport manager keeps in a personal spreadsheet. None of these systems share data automatically. The safety and compliance officer cannot get a complete picture of a driver's compliance status without manually pulling from three sources. The operations manager cannot get the total cost-to-serve per route without reconciling fuel, labour, and maintenance data from systems that were not designed to communicate.
Warehouse throughput is constrained not by physical space or labour headcount but by the efficiency of the workflows those resources are executing. Pick paths that are not optimised for the current storage layout. Put-away decisions made by individual operators rather than driven by a system that understands slot velocity and inventory flow. Receiving backlogs that block put-away and create receiving dock congestion. Inventory inaccuracy that only surfaces when a pick fails because the system said something was in a location that the physical inventory count says is empty. These are all software problems with measurable throughput and cost consequences.
The customers of logistics companies whether B2B shippers or end consumers receiving deliveries have expectations set by the Amazon and UPS consumer tracking experience: real-time GPS tracking, proactive delay notifications, ETAs that update continuously rather than at scanning events, and a self-service tracking experience that does not require calling the carrier. Most B2B logistics and freight operations are delivering a tracking experience that is a generation behind these expectations because the TMS or WMS they are running was designed for operational management rather than customer-facing visibility.
Every logistics software engagement starts with the operational model how freight moves, how assets are managed, how customers need to interact with the operation, and what the technology has to do to support the service commitments the business makes. The software is built for the operational environment, not for a reference architecture that assumes a different one.
Logistics software buyers span single-vehicle courier operations at one end and global 3PLs managing multi-modal supply chains at the other. The technology requirements, the integration complexity, and the right software architecture are fundamentally different at each point on that spectrum. We have built for companies across it.
Third-Party Logistics Providers (3PLs)
Custom TMS, WMS, and customer visibility portal development for 3PLs that need technology built around their specific service model whether that is ambient warehousing and domestic distribution, temperature-controlled food logistics, e-commerce fulfilment, or project and heavy freight. 3PLs have the additional complexity of building software that serves multiple shipper clients simultaneously from a shared operational infrastructure, which requires the multi-tenant data architecture and customer-specific reporting that generic logistics platforms rarely handle cleanly.
Freight Brokers and Digital Freight Networks
Load matching platforms, carrier management systems, quoting and booking tools, and the operational technology that enables freight brokers to scale their transaction volume without proportionally scaling headcount. Digital freight brokerage is one of the highest-growth segments in logistics technology and the companies building competitive platforms in this space need custom technology that reflects their specific carrier network strategy and shipper relationship model.
Fleet Operators and Carriers
Fleet management, driver management, dispatch, and TMS development for asset-based carriers and private fleet operators who need their technology to support the specific vehicle types, compliance obligations, and customer service commitments of their operation. The distinction between a truckload carrier, an LTL carrier, a last-mile delivery company, and a private fleet is significant for the software architecture each has different route planning requirements, different driver workflows, and different customer visibility obligations.
Retailers and Manufacturers with Own-Account Logistics
Logistics software for retailers and manufacturers managing their own distribution network private fleet management, inbound freight optimisation, distribution centre WMS, and the supply chain visibility tools that give logistics-intensive businesses visibility into the transport cost and service performance of their own operation rather than relying on a 3PL to report it to them.
Last-Mile and Courier Companies
Last-mile delivery platforms, route optimisation tools, driver mobile applications, and customer notification infrastructure for courier and last-mile delivery businesses competing in a market where delivery speed, tracking transparency, and driver productivity are the primary commercial differentiators. The technology in last-mile is the product, the customer experience of a delivery company is the app experience, and the driver efficiency of a last-mile operation is the route optimisation engine.
Freight Forwarders and Customs Brokers
Freight forwarding management systems, customs documentation tools, and supply chain visibility platforms for freight forwarders and customs brokers managing multi-modal international shipments. The documentation complexity and the multi-party data exchange requirements of international freight are distinct from domestic logistics and require software built for the specific document types, compliance obligations, and trading partner integrations of international trade.
We will map your operational model, your carrier and partner integration landscape, and your customer visibility requirements and give you an honest architecture path before any code is written. Response within 24 hours.
These are the capabilities that logistics software requires to operate reliably in a real logistics environment managing assets, shipments, and exceptions across a distributed operation where the consequences of a software failure are measured in operational disruption rather than user inconvenience.
Live GPS tracking of vehicles and assets, carrier API and EDI integration for in-transit shipment status, milestone event processing, geofence alerting, and the unified tracking view that eliminates manual status chasing across multiple carrier portals.
Multi-stop route optimization incorporating delivery time windows, vehicle capacity, driver HOS limits, real-time traffic, and priority constraints with dynamic re-routing when exceptions occur mid-route and the dispatch interface that gives operations teams real-time control over the active delivery fleet.
Real-time inventory by location and lot, directed putaway and pick path optimization, cycle count management, outbound shipping workflow, carrier label generation, and the inventory accuracy discipline that prevents pick failures and customer shipment errors.
Turn-by-turn navigation with optimised route integration, electronic proof of delivery with photo and signature, delivery exception recording, customer communication, HOS logging, and offline capability for areas with intermittent mobile coverage.
EDI 210, 214, 204, 990, and 997 transaction handling for carrier communication, real-time carrier API integration for tracking and booking, ASN and shipment notification exchange, and the integration architecture that connects the logistics management system to the carrier and trading partner systems it depends on.
Shipper and consignee facing shipment tracking portal with live status, document access, exception notifications, delivery confirmation, and the self-service visibility that reduces inbound enquiries to the operations team without reducing the transparency customers expect.
Automated exception detection for late pickups, in-transit delays, missed delivery attempts, and temperature or compliance events with alert routing to the responsible operations team member and the documented response workflow that keeps exception handling consistent and auditable.
On-time performance by carrier, lane, and facility, dwell time and transit time analysis, freight cost per unit and per lane, driver productivity and safety metrics, warehouse throughput and accuracy reporting, and the operational KPIs that give logistics management teams the data to improve performance rather than just measure it.
AI in logistics is not about automating what humans already do well, it is about making decisions at a speed, scale, and data complexity that no human operation can match. Route optimisation that incorporates real-time traffic, driver availability, vehicle capacity, and delivery time windows simultaneously. Demand forecasting that positions inventory before the order arrives rather than responding after it does. ETA prediction that accounts for the variance in transit time rather than promising the scheduled departure plus average transit. Anomaly detection that identifies the supply chain disruption before it becomes a missed SLA. Every logistics platform we build is architected with AI as a first-class operational tool from the first sprint.
ML-based route optimisation that goes beyond static stop sequencing incorporating real-time traffic, weather conditions, driver HOS availability, vehicle payload capacity, delivery time window constraints, and customer priority tiers to generate routes that maximise delivery completions per driver shift. Dynamic re-optimisation triggered by delivery exceptions, traffic events, and new order insertions that keeps the active route as efficient as the changing operational reality allows.
Highlights:
ML-based route optimisation that goes beyond static stop sequencing incorporating real-time traffic, weather conditions, driver HOS availability, vehicle payload capacity, delivery time window constraints, and customer priority tiers to generate routes that maximise delivery completions per driver shift. Dynamic re-optimisation triggered by delivery exceptions, traffic events, and new order insertions that keeps the active route as efficient as the changing operational reality allows.
Highlights:
Logistics software projects fail most often when the operational model is not fully mapped before architecture decisions are made the EDI integration complexity was underestimated, the carrier API behaviour differs from documentation, the driver mobile app was designed for office use rather than for a driver wearing gloves in a vehicle cab, or the real-time data volumes from GPS tracking were not designed into the database architecture. Our logistics software development services follow a structured, operations-first delivery methodology designed to surface those problems at discovery when they are cheap to fix.
Here's exactly how it works.
We map the operational model freight flows, asset types, carrier network, integration dependencies, customer visibility requirements, compliance obligations, and the specific operational pain points the software is being built to address. Output is a project scope with integration architecture, data volume estimates, compliance requirements, and a phased delivery plan that accounts for operational risk.
Technical architecture designed with the real-time data processing requirements, IoT integration depth, and operational reliability constraints of the logistics environment. Driver and operations interface design reviewed for the actual use context a dispatch screen used by an operations team under time pressure is different from a management dashboard, which is different from a driver mobile app used with one hand in a vehicle.
Two-week sprints with working logistics software delivered each cycle. Core tracking and dispatch flow ships first. Carrier integrations, route optimisation, and AI features are layered on the operational foundation that everything else depends on. GPS and carrier data integration begins in a test environment in the first sprint not deferred to later phases where timeline pressure prevents adequate testing.
Integration testing against real or representative carrier APIs, EDI trading partner testing, performance testing under production-realistic GPS event volumes, driver app testing on actual mobile devices in realistic operating conditions including offline scenarios, and failure mode testing for network interruption and carrier API unavailability.
Phased deployment with parallel operation of old and new systems during transition, driver and operations team training, integration go-live sequencing, and post-cutover monitoring. Logistics software deployments require careful coordination with the operational calendar avoiding peak freight periods and high-volume days where operational disruption has direct customer and commercial consequences.
Post-launch monitoring, carrier API maintenance as integration endpoints change, AI model retraining as operational data accumulates, new carrier and partner onboarding, and feature development as the logistics operation grows. Logistics carrier and partner integration maintenance is an ongoing requirement carrier APIs change, EDI versions evolve, and new trading partners need to be onboarded continuously.
Logistics software projects range from a targeted carrier integration to a full TMS or WMS build. The right engagement model depends on the operational integration complexity, how well-defined the requirements are, and the commercial timeline the logistics operation is working against.
You are running a multi-quarter TMS, WMS, or last-mile platform build, scaling an existing logistics system to new customers or freight modes, or augmenting your in-house technology team with logistics-specific engineering expertise without the cost and lead time of full-time hires.
Hand-picked engineers with logistics software experience, a QA specialist, and a technical lead working exclusively on your platform. Sprint planning and operational demos run on your calendar. Carrier integration, route optimisation, driver mobile, and AI logistics intelligence modules are all handled in-house.
Monthly retainer. No surprise invoices, no scope-creep billing. Team composition flexes as your logistics technology roadmap evolves.
3–10 engineers
6-month minimum
Scales with 30-day notice
Most logistics businesses start with one model and evolve into another as the platform scope expands across carriers and operational complexity grows. Let's figure out the right starting point together.
Building logistics software requires a development team that understands the operational stakes where a system failure is not a support ticket but a truck waiting at a dock the integration complexity of carrier and trading partner systems, and the real-time data architecture that logistics operations require to function at production event volumes. Here is what makes the difference in practice.
Operations-First Architecture Decisions
Every architectural decision in a logistics software engagement is evaluated against the operational reality it has to support. The database design that handles a million GPS events per day without degrading query performance. The carrier integration that fails gracefully when an API is unavailable rather than losing events. The driver mobile app that queues transactions offline and syncs when connectivity returns rather than failing silently. We design for the operational environment before we design the features, because in logistics the operational environment is not forgiving of architectural assumptions that turn out to be wrong.
Carrier and EDI Integration Experience
EDI integration 204, 210, 214, 990, 997 transactions across multiple carrier and trading partner specifications is where logistics software projects routinely encounter scope surprises. Trading partner EDI specifications vary, implementation guides contain exceptions, and live EDI behavior frequently differs from documentation. Carrier API integration has the same characteristic. We have done real carrier and trading partner integrations and the patterns that cause problems are not new to our team.
Real-Time Data Architecture for Logistics Scale
GPS tracking, IoT sensor data from vehicles and facilities, carrier event processing, and warehouse transaction volumes all generate data at rates that standard web application database architecture was not designed for. We design with the real-time data volumes of the specific logistics operation in mind event streaming architecture, time-series database selection, caching strategy, and the horizontal scaling patterns that keep the platform performing as the fleet, shipment, and event volume grows.
AI-Integrated Logistics Intelligence from Day One
Route optimisation, predictive ETA, demand forecasting, carrier performance scoring, and supply chain anomaly detection all require operational data collected and structured correctly from the first shipment. We address the AI architecture in the operational mapping phase so that the intelligence features can be deployed and improved as operational data accumulates rather than requiring a data layer rebuild before the first optimisation model can be trained.
Driver and Operations Interface Design for the Field
A driver mobile app that was designed for a developer's demonstration but not for a driver using it one-handed in a vehicle cab is a logistics software failure. Operations interfaces designed for a desktop monitor that has to be used on a tablet in a dispatch centre are a different kind of failure. We design for the actual use context of each user role field, dispatch, and management because the interface assumptions built into the design are as operationally consequential as the data architecture.
US-Based Communication, Logistics Calendar Awareness
Logistics businesses operate around commercial calendars where peak season deployments carry higher operational risk than off-peak ones. Project management and client communication on US business hours and the awareness that logistics operations do not pause for software project convenience. When a deployment decision needs to be made quickly to avoid a peak period, the communication model needs to support that.
Honest answers to the questions every logistics CTO, VP of operations, and technology leader asks before choosing a logistics software development company. If something is not covered here, our solution architects will walk you through it on a discovery call, no sales pitch, no fluff.
Cost depends on scope and integration complexity. A last-mile delivery platform with route optimisation, driver mobile app, customer tracking, and POD typically starts at $60,000–$130,000. A freight visibility portal integrating multiple carrier APIs starts at $40,000–$90,000. A custom TMS covering load planning, carrier management, and shipment execution ranges from $100,000 to $300,000 depending on freight mode complexity and integration scope. A custom WMS for a single-site distribution centre typically starts at $80,000–$200,000. A full fleet management platform with telematics, driver compliance, and maintenance management starts at $70,000–$150,000. Solvios provides detailed estimates after an operational and integration discovery phase.
A last-mile delivery platform takes 4–8 months. A freight visibility portal with carrier integrations takes 3–6 months. A custom TMS with full load planning and carrier management takes 8–14 months. A WMS for a single distribution centre takes 6–10 months. A fleet management platform takes 5–9 months. The biggest variables are EDI and carrier API integration scope; each carrier or trading partner integration adds time, and the sequence matters because some integrations depend on core platform components being completed first.
Carrier integration uses a combination of real-time REST APIs for booking, tracking, and rate shopping, and EDI for the batch transaction types that carriers communicate via standard EDI transactions 204 load tender, 210 freight invoice, 214 shipment status, 990 response to load tender, and 997 functional acknowledgment. EDI integration requires a VAN or direct AS2 connection, an EDI translator, and the trading partner-specific implementation guide configuration that maps standard transactions to the trading partner's specific field requirements and code values. Most carrier EDI implementations have interpretation gaps between the published guide and live behavior and we address these in the integration testing phase rather than discovering them in production.
Yes. Route optimization is one of our core AI capabilities for logistics software development. We implement constraint-based optimisation using Google OR-Tools and ML-enhanced routing models that incorporate delivery time windows, vehicle capacity, driver HOS constraints, real-time traffic data from Google Maps and HERE, and customer priority tiers. The optimisation quality depends on the accuracy of the constraint data delivery time windows that are too loose or too tight, vehicle capacity data that does not reflect actual payload limits, and HOS constraints that are not accurately tracked all reduce optimisation effectiveness. We address the data quality requirements as part of the operational mapping phase before any optimisation logic is built.
AI-integrated logistics software development means building systems where machine learning improves operational decisions that humans currently make with insufficient data or insufficient speed. In logistics this includes intelligent route optimisation that responds to real-time conditions rather than static plans, predictive ETA models that incorporate lane-level transit time variance rather than scheduled departure plus average transit, demand forecasting for inventory positioning ahead of order arrival, carrier performance scoring for load-level carrier selection decisions, warehouse slotting optimisation that updates as velocity patterns change, and supply chain anomaly detection that identifies disruptions before they cause inventory shortfalls. These capabilities require operational data collected and structured from the start which is why we address the AI data architecture in the operational mapping phase.
Fleet GPS tracking generates high-frequency event data typically one position update every 30 to 60 seconds per vehicle that accumulates into very large time-series datasets quickly. Standard relational database architectures are not optimal for this data pattern. We use time-series databases (InfluxDB) for high-frequency GPS and sensor data, event streaming architecture (Apache Kafka or AWS Kinesis) for real-time event processing, and purpose-designed indexing strategies for the geospatial query patterns that fleet tracking applications depend on. The data architecture for GPS and IoT data is specified during the discovery phase based on the fleet size, update frequency, and query patterns of the specific operation.
Yes. Driver mobile applications for logistics are one of our specific capabilities covering last-mile delivery, freight carrier dispatch, field service, and fleet management use cases. Driver apps require specific design and architecture considerations that standard mobile development does not address: offline-capable data access for areas with intermittent mobile coverage, one-handed UI design for in-vehicle use, barcode and QR code scanning for proof of delivery, photo capture for delivery documentation, deep integration with route optimisation outputs, and ELD or HOS logging integration for FMCSA compliance. We design driver apps for the operational context from the first wireframe review.
Three models: Dedicated Logistics Development Team for multi-quarter TMS, WMS, or last-mile platform builds; Time and Material for iterative development where the carrier integration scope or operational requirements are still being mapped; and Fixed Cost for well-defined logistics software projects where the operational model, carrier integrations, and data architecture are clearly established before development begins. We recommend the right model based on how complete the operational mapping is and how much of the integration complexity has already been assessed.
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