AI Trends 2025 for Mechanical Engineering in Stuttgart: Agentic Systems, Private LLMs & Rethinking Production

Why 2025 is a turning point for mechanical engineering in the Stuttgart region

Mechanical engineering around Stuttgart faces a dual challenge: rising cost pressure and the simultaneous need to deliver highly complex products faster, error-free and more customer-focused. We see 2025 not as a year of experiments but as a year of implementation: working AI systems in production, not just pilot projects on PowerPoint slides. With our Co-Preneur approach we build solutions that deliver value from day one — in production environments that demand reliability and compliance.

In this article we explain concretely how agentic systems, private LLM infrastructure (e.g., on Hetzner), Python-based SSR applications and domain-specific knowledge models (as an alternative to classic RAG setups) create impact in the areas of production, quoting, service and knowledge transfer. Our statements are based on practical experience from projects with manufacturers like STIHL and Eberspächer as well as on our fast PoC practice.

Agentic systems: autonomy where decisions matter

Agentic systems are no longer sci-fi. They are combinations of orchestrators, specialized modules and feedback mechanisms that autonomously carry out concrete tasks — from spare parts ordering to fine production scheduling. Crucial is that an agent does not act blindly but operates within a defined governance framework with robust decision rules.

For mechanical engineering this means: autonomous workflow chains can coordinate orders, quality checks and repair approvals while human experts intervene only in exceptional situations. Our experience shows that a modular agent architecture significantly increases acceptance on the shop floor because it acts locally and is explainable.

Practical example: spare parts management

In a project with a manufacturing company we developed an agent that monitors stock levels, forecasts lead times and proactively triggers orders. Through simple but robust rules and transparency in decisions, we were able to reduce response times for critical parts from days to hours.

Private LLM infrastructure on Hetzner: control, cost, compliance

Cloud-native LLMs bring performance, but for many mid-sized companies data protection and cost are hurdles. The solution is a private LLM infrastructure hosted on trusted colocation or IaaS providers like Hetzner. There, companies retain data sovereignty, can control network segments more strictly and scale model execution cost-efficiently.

We operate such setups for clients because they allow implementing multi-model routing: smaller models for routine queries, larger specialized models for complex engineering questions. Combined with tenant separation and logging, this forms a robust foundation for productive applications.

Architecture components that proved effective

• Coolify for fast deployments and simple DevOps pipelines.
• Postgres as the primary transactional system and metadata store.
• MinIO for object-based storage of sensitive technical documentation.
• Multi-Model Routing for dynamic selection of the appropriate model per request.

These components allow a reproducible, maintainable and scalable setup that we can deploy in multiple PoCs within days.

Domain-specific knowledge models instead of RAG: more determinism in industrial processes

Retrieval-Augmented Generation (RAG) is useful, but in safety-critical industrial contexts it has two concrete disadvantages: unpredictable hallucinations and hard-to-audit source trails. Our alternative is domain-specific knowledge models, i.e., models trained and fine-tuned on a validated, curated knowledge graph and technical documents.

The result: higher predictability, explainable answers and lower risk of faulty decisions. Combined with strict validation workflows, such models are ideal for service triage, quote calculation and fault diagnosis in manufacturing.

How we extract expert knowledge

Extraction starts with structured interviewing and annotation by subject-matter experts, followed by normalization in Postgres and enrichment with CAD/BOM data in MinIO storage. Then an iterative fine-tuning process follows so the model delivers answers in the domain language and makes implicit knowledge explicit.

At STIHL we built knowledge modules this way that massively accelerated technical support processes: maintenance instructions, failure patterns and training material were transformed into a reliably retrievable model.

Python-based SSR applications: UI/UX that actually works on the shop floor

The best AI is useless without a reliable interface. Server-side-rendered (SSR) Python applications have proven ideal for industrial environments because they load quickly, are offline-friendly and can communicate directly with internal services. We use Python SSR for dashboards, issue tracking and interactive troubleshooters.

Important: these applications must be durable, testable and secure. That is why we rely on modular codebases, standardized CI/CD pipelines and early involvement of users from production and service.

Example: production dashboard

A production manager receives consolidated KPIs, predictions for production utilization and proactive alerts via an SSR application. Behind the scenes runs an ensemble of LLM predictors, rule engines and agents that suggest autonomous adjustments — the human makes the final decision.

Predictive automation & AI-supported production planning

Predictive automation combines forecasting capabilities with automated countermeasures. In production planning this means: detect anomalies, compute alternative routes and reassign resources — in seconds instead of hours. Our solutions connect sensor data, MES/ERP interfaces and domain-specific knowledge models.

AI-supported production planning uses simulations and optimizers to compensate for short-term plan deviations. One important point: we do not force full autonomy but provide Human-in-the-Loop loops that secure control and trust of production managers.

Concrete results from projects

In collaboration with Eberspächer we developed analysis modules that predict production noise and quality deviations. By combining signal processing, ML pipelines and automated workflows, downtime was reduced and rework rates were lowered.

Internal engineering copilots: knowledge transfer and efficiency

Engineering copilots are internal assistants that help designers and service technicians make decisions, speed up routine tasks and share know-how. Unlike general chatbots, these copilots are tailored to a company's processes, language and standards.

We have implemented copilots that learn from technical documents, change logs and inspection reports. The result: shorter onboarding times, fewer repair errors and faster quote generation.

From idea to production-ready copilot

• Step 1: Capture relevant data sources (BOM, inspection reports, service history).
• Step 2: Build a curated knowledge model with versioning.
• Step 3: Integrate into existing tools via API (SSRs, MES, ERP).
• Step 4: Iterative fine-tuning and usage monitoring.

Secure tenant separation, compliance and robust decision logic

Security and compliance are not add-ons — they must be architectural principles. For mid-sized companies we recommend a combination of network segmentation (VLANs), encrypted storage (MinIO with KMS), role-based access and strict auditing.

For tenant separation we rely on physically or logically isolated controller instances, separate databases (Postgres schemas) and strict logging policies. Decision logic must be versioned and testable; we treat rules like software artifacts, with unit tests, simulations and canary releases.

Compliance checklist (short)

• Identify and classify data silos
• Encryption in transit and at rest
• Data minimization: use only relevant data subsets for models
• Audit logs and explainability mechanisms
• Regular security and GDPR reviews

How we deliver PoCs in 3 weeks and why that matters

Many PoCs fail due to scope and complexity. Our AI PoC offer (€9,900) is designed exactly for that: within a short time a valid technical proof, a working prototype and a clear production plan. We operate according to our Co-Preneur principle: we take responsibility, work in the client's P&L and deliver results instead of theories.

In practical deployments we have built AI modules for fault diagnosis, quote generation or maintenance planning in a few days — with clear metrics for quality, runtime and cost per inference. This speed makes a decisive difference: early insights instead of months of uncertainty.

Recommended architecture: building blocks & integration

A reusable architecture for manufacturing AI typically looks like this for us:

• Edge-Collector – data collection from PLCs, MES and sensors
• Ingest-Pipeline – ETL into Postgres & MinIO
• Feature Store – time series and technical metrics
• Model Layer – private LLMs, classical ML models, Multi-Model Router
• Orchestrator / Agents – autonomous workflow chains
• SSRs & APIs – Python applications for user interaction
• Ops & Security – Coolify for deployments, monitoring, KMS

These components have proven stable and maintainable in multiple projects. They enable fast iteration while providing industrial robustness.

Takeaways & concrete recommendations for decision-makers

For CTOs and production managers in the Stuttgart region this means concretely:

• Start with a focused PoC (3 weeks) on a clear use case such as spare parts management or service triage.
• Opt for private LLM infrastructure (e.g., Hetzner) for cost and data protection control.
• Prioritize domain-specific knowledge models over generic RAG approaches when reliability matters.
• Use agentic systems for autonomous workflows, but keep Human-in-the-Loop.
• Build modular architectures with Coolify, Postgres, MinIO and Multi-Model Routing.

At Reruption we support you with our Co-Preneur approach: we don't just build prototypes, we deliver products and the organizational capabilities to operate these solutions sustainably.

Call to Action

If you want to know what a realistic 3-week PoC for your manufacturing could look like, talk to us. Together we define the use case, set up a lean technical setup (including architecture sketch and security framework) and deliver a working prototype with clear metrics. No pitch, but a built outcome.

Contact Reruption – we will show you how to generate real value with AI in 2025: faster, safer and measurable.