How do you develop production-ready AI systems for logistics, supply chain & mobility?

An AI PoC for logistics can typically be delivered within a few weeks, with the first deliverable being a technically validated prototype that demonstrates concrete metrics such as forecast accuracy or routing runtime. The focus is on testing assumptions: data availability, latency requirements and integration effort.

We structure PoCs to deliver immediate insights: data ingestion, model and architecture tests, a simple UI/demo and metrics. This allows decision makers to realistically assess technical feasibility and the estimated implementation effort.

Preparation is important: clear KPI definitions, access to relevant data sources (TMS, telematics, sales) and a test environment where models can be validated against historical and live data. Without these foundations the validation phase will take significantly longer.

After the PoC follows production planning with an effort estimate for transfer to production environments, including infrastructure, monitoring and compliance. Typically our clients see a clear roadmap for a 3–9 month production rollout after a successful PoC.

A robust demand forecasting engine requires a combination of internal transactional data (orders, SKU-level sales), inventory and delivery data, as well as external signals like weather, seasonality, market data and promotion plans. For mobility applications, telematics and usage data are added.

Data quality is ensured through multiple layers: automated ETL checks, missing-value handling, anomaly detection during ingest and feature validation before model training. A feature store and strict data lineage help ensure reproducibility and auditability.

In practice we work with periodic data profiling, validation suites and backtests against historical seasonal patterns to ensure forecasts remain stable under real-world conditions. We also implement retraining triggers based on drift detection.

Governance aspects are also central: permission management, masking of personal data and logging of data access are mandatory, especially when personal driver data or customer information are involved.

Private chatbots and contract copilots must be operated to enterprise standards: that starts with isolated infrastructure (e.g. self-hosted on Hetzner or in private VPCs), encrypted storage (MinIO or encrypted Postgres backends) and ends with role-based access and audit logs. This ensures confidential contract data does not end up in open API logs or third-party services.

Technically we use model-agnostic architectures that either host models locally or use controlled API integrations with data filtering. Sensitive inputs can be masked or tokenized before model interaction; outputs are reviewed to prevent data leaks.

Additionally we implement explainability mechanisms: the copilot provides not only suggestions but also source references and confidence scores so lawyers and buyers can understand decisions. Audit trails document all change and Q&A sequences for compliance purposes.

Regular security audits, red-teaming and data protection impact assessments (DPIAs) are mandatory before a contract copilot is released into production. Only then can confidentiality and regulatory compliance be assured.

For scalable fleet and routing solutions we recommend a hybrid infrastructure: self-hosted components for data sovereignty (e.g. Hetzner, MinIO, Traefik) combined with optional cloud services for specialized compute loads. A Kubernetes-based platform or orchestrated Docker stacks (e.g. Coolify) provide the necessary scalability and resilience.

It is important to clearly separate storage, compute and network: telemetry data should go to cost-efficient object stores; features and vector indices to Postgres + pgvector; models run in separate inference services with autoscaling. This allows prioritization of latency-critical paths (e.g. real-time routing).

For integration we recommend standardized API gateways, observability stacks (Prometheus, Grafana, centralized logs) and alerting pipelines. This enables fast troubleshooting and clear SLAs for dispatchers and dispatcher interfaces.

Finally, we support the implementation of canary releases and blue/green deployments for models so new model versions can be rolled out in a controlled way and quickly rolled back if issues arise. This significantly reduces operational risk.

The combination of classical operations research (OR) algorithms and learnable ML components is particularly powerful: OR delivers guaranteed, constraints-compliant solutions for vehicle routing problems, while ML modules provide forecasts for demand, traffic conditions and service times that feed the OR models.

Our approach separates prediction and optimization layers: first ML generates precise input signals (e.g. expected loading times, traffic delays), then an OR backend optimizes based on those signals while considering capacities, priorities and SLAs. This separation preserves traceability and allows targeted improvements.

In operational environments we also introduce receding-horizon strategies: routings are re-optimized in short intervals to react to real-time deviations. Performance is critical here — the optimization engine must deliver answers within operational time windows.

For validation we use backtesting with historical events and stress tests with simulated disruptions (e.g. road closures, sudden demand spikes) to ensure the robustness of the hybrid solution. Only then does a system deliver lasting value in live operations.

Time and cost frames depend heavily on data readiness, integration complexity and compliance requirements. A technically viable PoC is possible within 4–6 weeks (our AI PoC offering: €9,900). This PoC validates core assumptions and provides the basis for effort estimates.

For a full production rollout including integrations into TMS/ERP, monitoring, security and change management we typically estimate 3–9 months. Smaller, clearly scoped modules (e.g. a contract copilot or a demand model for a product group) can be implemented faster than enterprise-wide platforms.

Costs vary: infrastructure, data engineering, model development, integrations and organizational measures each contribute differently. We always provide a transparent roadmap with milestones, effort estimates and expected savings so decision makers can evaluate ROI within 6–12 months.

Iterative delivery is important: small releases with measurable business impact secure funding and acceptance for follow-up phases and reduce risk compared to a big-bang approach.