AI Consulting for Logistics

Our solutions are designed to work alongside the systems you already run, not replace them. Depending on what your platforms expose, integration can happen through APIs, database connections, file-based exchange, or messaging — and during discovery we assess exactly which paths your TMS, WMS, and ERP support before anything is built. Where a system offers no clean interface at all, we design around it (for example, processing the documents or exports it produces) rather than forcing a migration. The goal is that your dispatchers and warehouse staff keep working in the screens they know, with AI feeding them better data.

More accurate than last-year-plus-a-percentage, which is the benchmark that matters. Machine learning models capture patterns that manual planning cannot: interactions between weekday, seasonality, customer mix, promotions, weather, and market signals. Just as importantly, a good forecasting system quantifies its own uncertainty — telling you not only the expected volume but the realistic range — so you can plan staffing and capacity against scenarios instead of a single number.

We validate accuracy on your own history before you rely on it: models are back-tested against past periods, including volatile ones, and we report the error honestly. If your data cannot support a useful forecast, we tell you that in the proof of concept — not after a rollout.

Yes. Warehouse computer vision in particular often runs best at the edge — on local hardware next to the cameras — so video never has to leave the site, and counting or damage detection keeps working even when connectivity is poor. Forecasting and document processing can run on-premise, in your private cloud, or in ours, depending on your IT policy. Deployment architecture is a design decision we make with your IT team during the MVP stage, not a constraint imposed by our stack.

Telematics and driver-related data are personal data under GDPR, and we treat them that way by default — as a German company, we apply European data-protection standards to every project worldwide. In practice that means data minimization, aggregation and pseudonymization wherever the use case allows it, clear data processing agreements, and architectures where the data stays under your control.

Many of our German and European clients also need to align AI projects with employee representatives. Because our solutions are typically built to evaluate operations — lanes, loads, sites, processes — rather than to score individual employees, that conversation is usually straightforward, and we support you with the technical documentation it requires.

Peaks are exactly where AI earns its keep. Forecasting models are trained on your historical peaks — year-end, harvest, promotions, industry cycles — so the surge is predicted, not discovered. Document automation and camera-based counting scale with volume in a way manual teams cannot: the system that processes two hundred delivery notes a day processes two thousand without hiring. We also stress-test solutions against your worst historical weeks before go-live, so peak behavior is a tested property, not a hope.

A proof of concept on a well-scoped problem — one document type, one site, one lane — typically takes weeks, not months, because it runs on data exports before deep integration. An MVP integrated with your live systems follows in the next stage, with the timeline depending mainly on how accessible your TMS, WMS, and data are. The staged model exists precisely so you see evidence early: you get a working prototype and measured accuracy before committing to full integration.

Almost every logistics company we talk to believes its data is too messy — and almost every one has more usable data than it thinks: shipment histories in the TMS, scan events in the WMS, telematics logs, carrier invoices, emails, and PDFs. Assessing what you actually have is the first step of every engagement, and it is also where we give you an honest go/no-go: if a use case is not supported by your data, we say so before you invest in development, and we tell you what to start collecting so it becomes feasible.

That variability is precisely why generic OCR tools disappoint and custom document AI works. Modern OCR combined with NLP and large language models can handle customs forms, delivery notes, CMRs, packing lists, and carrier invoices across layouts, scan qualities, and languages — extracting the fields your process needs and validating them against your master data. We typically start with your highest-volume document type, measure extraction accuracy against a human-keyed baseline in the PoC, and route low-confidence documents to a human review queue so quality is guaranteed while automation handles the bulk.

It depends on how standard your problem is — and in logistics, less is standard than vendors claim: your lanes, contracts, document formats, and warehouse layouts are your own. Off-the-shelf tools fit generic tasks; custom solutions fit the workflows where your margin actually lives. Because we are an AI software development company and not a reseller, we have no stake in either answer: where an existing tool genuinely covers your need, we help you select and integrate it, and we build custom only where it beats buying.

Yes. We are headquartered in Darmstadt in the Frankfurt Rhine-Main region — surrounded by one of Europe's densest logistics corridors — with a second office in Berlin, and we serve clients worldwide. Projects run remotely with structured communication at every stage, from discovery through deployment and evaluation. Reach us at info@aisuperior.com or +49 6151 7076909.