Logistics and supply chain teams have stopped asking whether AI helps. The hard part in 2026 is different: demand that swings faster than the forecast, multi-leg routing that has to respect hundreds of real-world constraints, and a model that has to act on data fragmented across five to ten carrier, TMS, and WMS systems. We build supply-chain AI that survives that reality — forecasting, routing, visibility, and document automation engineered to run in production, not stall in a pilot.
A supply chain is a live network of carriers, warehouses, customs brokers, and shippers, each running its own system and its own version of the truth. That is exactly why so much logistics AI looks impressive in a demo and stalls in operations. The constraints below are not edge cases — they decide whether a forecast, a route, or an exception alert is ever trusted on the floor.
Consumer demand volatility is the challenge planning leaders name most often for the next three years. A forecast built on last quarter’s pattern misses the swing that matters, and the cost lands twice — as stockouts on one side and excess inventory on the other. Forecasting has to read more signal and adapt faster than a monthly batch.
Multi-leg, multi-modal routing is not a shortest-path problem. Time windows, vehicle types, driver hours, accessorials, and carrier compliance rules stack into hundreds of real-world constraints. Optimising at network scale — and re-optimising when conditions move — is where most planning tools quietly fall back to the planner’s spreadsheet.
Many shippers operate across five to ten platforms — TMS, WMS, carrier portals, rail systems, and finance tools — that disagree with each other. Around 69% of supply-chain leaders report persistent data quality and integration problems. Fragmented data, not a weak model, is the single biggest reason pilots never scale.
When a shipment is late, a port backs up, or a load is damaged, the response still runs on manual workarounds — calls, emails, and tribal knowledge — exactly when speed matters most. Real-time exception handling means detecting the disruption and starting the response in the same system, not the next morning.
Bills of lading, customs declarations, and freight invoices still move as PDFs, scans, and email attachments — and a large share of bills of lading are still keyed by hand. Every manual touch is a delay, an error, and a margin leak on documents that should flow into the tracking system within minutes.
Logistics runs on margins that punish every empty mile, failed delivery, and re-keyed document. That makes it the right place for AI — the gains are concrete — and the wrong place for a black box. A model that cannot be trusted on the floor is a model that costs more than it saves.
For years AI in the supply chain meant a dashboard a planner read and then acted on by hand. That is changing. The frontier in 2026 is agentic AI that does not just predict a disruption but begins the response — the same system that spots the late shipment is the one that re-plans the route. The gains are real, but only when the model can act safely on data it can actually trust. We work exactly where prediction has to become action.
Built for the network
Engineered to connect across TMS, WMS, and carrier systems — so the model acts on one reconciled view, not a stack of disagreeing exports.
You own it
Code, models, and integrations are yours, designed so your operations and data teams can run, monitor, and extend them independently.
We work on the use cases where logistics and supply chain teams see concrete return and where the data is hardest to tame. Each maps to a Miniml expertise team that has shipped it before — so the build, the integration, and the handover come from people who have done it on a live network.
Forecasting that reads far more signal than a monthly batch — seasonality, promotions, weather, and live network data — to predict demand and arrival times, and adapt as the pattern swings. Better forecasts cut both the stockout and the dead inventory.
Predictive analytics →Multi-leg, multi-modal routing that respects time windows, vehicle types, driver hours, and carrier rules — optimised at network scale and re-planned when conditions move, with the reasons behind every route a dispatcher can see and trust.
Decision intelligence →Extraction that reads bills of lading, customs declarations, and freight invoices accurately, validates them against the shipment, and flags the exceptions — so documents flow into the tracking system in minutes instead of being re-keyed by hand.
Document intelligence →Agentic workflows that detect a delay, a port backup, or a damaged load and start the response in the same system — re-planning, notifying, and escalating within defined rules, with a human in the loop on the cases that carry real cost.
AI agents →Grounded assistants over standard operating procedures, carrier contracts, compliance rules, and tickets — so warehouse, dispatch, and customer teams find the right answer and cite the source, instead of phoning around for tribal knowledge.
Knowledge & retrieval →Vision systems that check loading, count and verify pallets, and detect damage at receipt and dispatch — catching the error before it becomes a claim, a chargeback, or a returned shipment that the thin margin cannot absorb.
Computer vision →In logistics, the model is rarely what stops a pilot — the data and the integration are. We treat the constraints below as part of the build, because a forecast or a route the floor cannot trust is one nobody acts on.
A data foundation that connects your TMS, WMS, and carrier systems — mapped and reconciled before any model ships.
Real-time scoring where a minute matters, batch where it does not — the architecture matched to the use case.
Rate rules, accessorials, and compliance checks defined and validated, so an agent optimises the right objective.
Forecast accuracy and routing performance watched continuously — with alerts before a model degrades on a live network.
That is the normal starting point, not a blocker — but it is the first thing we fix. We begin by mapping and reconciling the systems into one view and surfacing where they disagree, because fragmented data is the single biggest reason supply-chain pilots never scale. The model comes after the foundation, not before it.
It depends on your data and how volatile your lanes are, so we will not quote a number we cannot stand behind for your network. What is consistent is the direction: forecasts that read more signal and update more often beat a monthly batch, and the value shows up as fewer stockouts and less dead inventory. We baseline against your current accuracy before we promise a lift.
Agentic AI is the shift from a system that predicts to one that acts — the agent that spots a disruption is the one that begins the response. It is safe when it runs inside defined business rules, on clean constraint logic, with a human in the loop on the decisions that carry cost. We scope those bounds with you before anything runs autonomously.
Yes — this is one of the clearest wins in logistics, because so many freight and customs documents are still keyed by hand. We build extraction that reads the document, validates it against the shipment, and flags exceptions for a person, so clean data flows into your tracking and billing systems in minutes instead of hours.
Yes. The code, the models, the integrations, and the documentation are all yours, designed so your operations and data teams can run, monitor, and extend them independently. Enablement is built into the engagement, not sold back to you as an upsell.
Start with a consultation. We will talk through your network, your systems, and where the cost actually sits — then tell you which use cases are ready, which need the data foundation first, and what a realistic delivery plan looks like for your environment. No obligation to build with us afterwards.
We assess it against what actually decides whether logistics AI reaches production — the state of your data across TMS, WMS, and carrier systems, the real-time and constraint demands of the decision, and the integration path into your operations. You leave knowing what is ready now and what needs the data foundation first.
What you get: a readiness assessment of your priority use case; a target architecture for your systems, latency, and constraints; a staged delivery plan with timelines and effort estimates; and one workshop with your operations and data leads. Led by a senior consultant — fixed scope, fixed fee.
Book a consultation →A 30-minute conversation with a senior consultant. Bring the use case stuck in a proof of concept, or the network you have been told is too fragmented to model. We’ll tell you what is ready to ship, where the data gaps are, and what a Supply Chain AI Review would surface.
Book a consultation →