How can AI engineering make the chemical, pharmaceutical and process industries in Dortmund more resilient and efficient?
Innovators at these companies trust us
Local challenge
The chemical, pharmaceutical and process industries in Dortmund face strict regulatory requirements, complex laboratory processes and pressure to minimize production outages and safety risks. Without robust, production-oriented AI solutions, many AI initiatives remain stuck at the pilot stage.
Why we have the local expertise
Reruption is based in Stuttgart, travels regularly to Dortmund and works on-site with clients from North Rhine-Westphalia. We understand the structural shift from steel to software and know the local challenges from logistics hubs and energy providers to manufacturing operations.
We don’t just enter meeting rooms: we embed temporarily into your teams, work in the P&L and deliver technical prototypes that can be scaled from initial tests to production readiness. This way of working is particularly important when it comes to safety copilots, laboratory process documentation or operating sensitive internal models.
Our references
In manufacturing we have supported extensive projects with STIHL — from saw training to ProTools — and gained experience in how complex production processes can be digitized and improved with AI. For industrial production challenges, this experience maps directly to chemical and pharmaceutical production lines.
For noise analysis and process optimization in manufacturing environments, we worked with Eberspächer on AI-driven solutions for noise reduction. The methodological steps for data collection, feature engineering and validation are also relevant for chemical process monitoring.
Technology and product development projects with companies like BOSCH, TDK and AMERIA demonstrate our ability to support complex integrations, go-to-market considerations and, where necessary, spin-offs. This project experience helps design robust architectures for secure internal models and on-prem infrastructures.
About Reruption
Reruption was founded because companies should not merely be disrupted — they should reinvent themselves. Our co-preneur philosophy means we join like co-founders: we take responsibility for outcomes, build prototypes in days and drive implementation through to production.
Our focus rests on four pillars: AI Strategy, AI Engineering, Security & Compliance and Enablement. For Dortmund we bring fast engineering, deep technical understanding and experience with secure, self-hosted systems that are often required in regulated environments.
Interested in a fast technical proof for your use case?
Arrange an initial scoping meeting. We are happy to come to Dortmund, work on-site with your team and deliver a clear PoC roadmap.
What our Clients say
Hans Dohrmann
CEO at internetstores GmbH 2018-2021
This is the most systematic and transparent go-to-market strategy I have ever seen regarding corporate startups.
Kai Blisch
Director Venture Development at STIHL, 2018-2022
Extremely valuable is Reruption's strong focus on users, their needs, and the critical questioning of requirements. ... and last but not least, the collaboration is a great pleasure.
Marco Pfeiffer
Head of Business Center Digital & Smart Products at Festool, 2022-
Reruption systematically evaluated a new business model with us: we were particularly impressed by the ability to present even complex issues in a comprehensible way.
AI engineering for chemical, pharmaceutical & process industries in Dortmund: A deep dive
Dortmund and the surrounding Ruhr area have undergone a fundamental transformation over the past decades: from heavy industry to a diverse technology and logistics cluster. For the chemical, pharmaceutical and process industries this brings new opportunities, but also new demands on digital systems and how AI is integrated into production processes.
Market analysis and regional dynamics
The North Rhine-Westphalia region combines manufacturing expertise with a dense network of logistics providers, energy suppliers and IT service providers. This proximity is an advantage: data streams from production facilities can be quickly fed into analytics environments, and local partners support implementation and operations. At the same time, regulatory requirements—GMP, ISO standards, and industry-specific safety regulations—must always be considered.
For companies in Dortmund this means: every AI initiative must be operationalizable from the outset and take security aspects and auditability into account. Proof-of-concepts that shine in a cloud demo are of little use if they cannot be transferred to on-prem, private cloud or hybrid setups with strict access controls.
Concrete use cases
The range of use cases is large, but some recurring high-value cases deserve special attention. First: laboratory process documentation. AI-driven systems can automatically detect and document inconsistencies, measurement deviations or steps that deviate from SOPs — simplifying audits and reducing compliance risks.
Second: safety copilots. In facilities with process risks, copilot systems can support operators in real time, suggest control steps and provide action recommendations for abnormal measurements. Third: knowledge search and enterprise knowledge systems that make production documentation, experimental data and maintenance logs searchable — without exposing sensitive information to external models.
Implementation approach: From PoC to production
Our AI PoC methodology (€9,900 offering) starts with a precise use-case definition: inputs, outputs, acceptance criteria and metrics. For Dortmund we recommend prioritizing scenarios with high compliance or safety requirements. In these cases architectural decisions — on-prem vs. cloud, encryption, access control — must already be validated during the prototype phase.
Rapid prototyping demonstrates technical feasibility in days, not months. This is followed by performance evaluation (latency, quality, cost per run) and a concrete production plan: infrastructure, timeline, budget and team roles. Especially in regulated environments we plan validation and audit steps into the process.
Technology stack and infrastructure
For the process industry reliable, reproducible models and robust data pipelines are central. We implement modular solutions: ETL pipelines for sensor data, aggregated time series in data lakes, vector-based knowledge systems with Postgres + pgvector, and private chatbots without insecure RAG integrations when the data situation requires it.
Self-hosted infrastructure is often not just a preference but a requirement. We build heterogeneous stacks based on Hetzner, Coolify, MinIO and Traefik — combined with containerized services and clear backup and disaster recovery processes. These setups enable low latency, data sovereignty and insulation from unauthorized third-party access.
Integration with existing systems
Integration means connecting LIMS, MES, SCADA, ERP and local historian data sources without disrupting production processes. We follow an iterative integration model: small, tested data extractions, gentle synchronizations and clear monitoring interfaces. This minimizes risk and maximizes business value.
Another important point is API and backend design for generative models: interfaces to OpenAI, Groq or Anthropic are possible, but for sensitive processes we favor model-agnostic architectures that allow switching between models while maintaining data protection policies.
Validation, compliance and security
Validation in regulated environments is not a retrospective step but an integral part of development. We document data provenance, training pipelines, model versioning and evaluation results. For safety copilots we implement human-in-the-loop mechanisms, fail-safes and comprehensive logging structures for forensic analysis.
Security encompasses both IT security and algorithmic robustness: access control, encryption, monitoring for drift and countermeasures against data poisoning. In Dortmund’s industrial facilities these measures are critical because production outages carry substantial follow-up costs.
Success criteria, ROI and timelines
Success is measured not only by accuracy but by reliability, efficiency gains and compliance fulfillment. Typical KPIs are reduction of scrap, faster lab cycles, reduced downtime and accelerated audits. ROI calculations must account for operational costs, infrastructure, validation and change management.
Timelines vary: a valid PoC can be achieved in days to a few weeks; scaling to production can take months depending on regulatory reviews and integration complexity. We plan conservatively and deliver iteratively so stakeholders see quick wins while following a path to full production readiness.
Team & change management
Technical skill alone is not enough. Successful implementations require process owners, data engineers, DevOps/infra teams, compliance specialists and domain experts from lab and production. We assist in building these roles, provide training and work as co-preneurs closely with operational teams.
Change management in Dortmund means respecting the expertise of local workshops, operational staff and IT departments, communicating transparently and designing solutions that simplify daily work instead of complicating it.
Common pitfalls and how to avoid them
Too often projects remain at the proof-of-concept level because data governance, missing infrastructure or unrealistic expectations block implementation. We avoid this through early architectural decisions, clear value metrics and binding production plans. For companies in Dortmund pragmatic infrastructure (often on-prem or private cloud) is a frequent success factor.
Another mistake is blind trust in external models without safeguards. We build model-agnostic, auditable pipelines that can switch to local models if necessary, ensuring data sovereignty and compliance.
Ready to bring AI into production?
Contact us for a proposal to implement a secure, production-ready AI architecture including infrastructure, compliance and rollout plan.
Key industries in Dortmund
Dortmund’s economic history is shaped by steel and coal, but the structural transformation has turned the city into a hub for logistics, IT and energy. This transformation creates a special industrial density: manufacturing companies, logistics centers and energy providers operate in close spatial and economic proximity.
The logistics sector benefits from Dortmund’s infrastructure: motorway connections, rail links and storage space make the region attractive for distribution centers. For AI engineering, opportunities arise from linking production data with supply chain and picking data.
IT service providers and software firms have established themselves in Dortmund to support industrial digitization. They provide the necessary expertise for API integrations, data platforms and security solutions — competencies that are indispensable in process industry projects.
Insurers and financial service providers in the region, especially those that work closely with industrial risks, are also advancing data-driven services. Predictive analytics for risk assessment or damage forecasting are typical interfaces where AI engineering can connect with the process industry.
The energy sector around Dortmund, with active regional players, drives topics like energy optimization, grid stability and load forecasting. Processes in chemical plants are energy-intensive; AI can help smooth consumption and reduce peak loads.
For the chemical, pharmaceutical and process industries, Dortmund’s landscape offers a combination of suppliers, IT competence and logistics that enables complex AI systems to be developed, tested and operated locally. At the same time, industry-specific regulations and safety requirements demand special care in architecture and operations.
The close networking of local players makes collaborations possible: from shared data spaces to testbed installations and cooperative training programs. Companies that leverage this networking significantly shorten the time-to-market for their AI initiatives.
In conclusion, Dortmund is not an isolated industrial center but an ecosystem. AI engineering in the region means leveraging this local advantage: short distances between production, IT and logistics, combined with the expertise needed for secure, reproducible production systems.
Interested in a fast technical proof for your use case?
Arrange an initial scoping meeting. We are happy to come to Dortmund, work on-site with your team and deliver a clear PoC roadmap.
Key players in Dortmund
Signal Iduna is one of the large insurance groups in the region with historical roots in Dortmund. The company plays an important role in the risk debate for industrial processes and promotes data-driven approaches to risk assessment — a relevant environment for AI-driven prediction models and scenario analyses.
Wilo is a globally active pump manufacturer with a strong presence in NRW. The combination of mechanical manufacturing, IoT-enabled pumps and service processes makes Wilo an ideal partner for AI solutions around predictive maintenance, energy consumption optimization and digital service offerings.
ThyssenKrupp is a shaping industrial and engineering group in the region. Although parts of the group are diversified, the technological expertise and manufacturing depth provide a foundation for joint development projects, especially when it comes to scaling AI solutions in industrial environments.
RWE as an energy provider is central to supply security and projects for energy efficiency. For chemical and pharmaceutical manufacturers in the region, partnerships with energy companies are a lever to realize AI-driven load management and efficiency programs.
Materna is an IT service provider with a strong focus on digital transformation and public as well as industrial customers. Materna and similar IT providers form the local support infrastructure needed to integrate and operate AI projects long-term.
Beyond these big names there is a dense network of medium-sized companies, suppliers and specialized service providers in Dortmund. These companies are often agile, technically skilled and willing to use AI pilot projects as an opportunity to increase efficiency — an ideal basis for co-preneur projects that we implement on-site.
Academic and research institutes in the region additionally contribute know-how: from data science to process modeling. This connection between industry and research creates a fertile environment for innovation-driven AI applications in the process industry.
In sum, these players form an ecosystem that provides both the requirements of the process industry and the infrastructure for digital transformation. For companies in Dortmund this means: short decision paths, local expertise and the possibility to quickly transfer prototypes into productive environments.
Ready to bring AI into production?
Contact us for a proposal to implement a secure, production-ready AI architecture including infrastructure, compliance and rollout plan.
Frequently Asked Questions
The decision for on-prem or cloud solutions depends on several factors: regulatory requirements (e.g. GMP), corporate policies on data sovereignty, latency requirements and the sensitivity of raw data. In many cases within the chemical and pharmaceutical industries it is advisable to run critical workloads on-prem or in a private cloud to retain full control over access and storage.
An on-prem setup also enables finely grained security and audit mechanisms that auditors can trace. This ensures traceability of data paths, training sets and model versions — a central aspect during regulatory inspections.
Practically, many companies use hybrid architectures: less sensitive services or model experimentation can take place in certified cloud environments, while production models, knowledge databases and copilot services are operated locally. This separation combines agility with compliance.
Reruption recommends not making the decision dogmatically: we perform feasibility analyses, evaluate costs, latency and security requirements and propose pragmatic architectures — including concrete migration paths from PoC to on-prem production operations.
A focused proof-of-concept can often be realized within a few days to weeks given a clearly defined scope. Our Kaizen approach begins with a precise use-case definition: which inputs are needed, which outputs are expected and which acceptance criteria apply — e.g. detection rate of SOP violations or completeness of documentation.
Technically this means: quick connection to existing data sources, building a minimal ETL pipeline and developing a first classification or extraction module. In this phase we demonstrate technical feasibility and deliver measurable metrics such as precision/recall or time savings.
It is important that regulatory aspects are already considered during the PoC phase: data storage, anonymization, audit logs and validation steps must be documented to avoid blocking the path to production.
After the PoC follows a clear production roadmap: effort estimation, infrastructure decisions (on-prem vs. cloud), security and compliance steps and a schedule for integration into existing systems like LIMS or MES. In Dortmund we often work on-site with teams to pragmatically manage this transition.
Security and compliance are integral components of the engineering process. Technically we start with a secure infrastructure: encrypted data transports, role- and permission concepts, hardened storage layers (e.g. MinIO with encryption) and network segmentation. On this basis we implement monitoring, logging and model age detection.
Algorithmically we emphasize traceability: versioning of training data, model versioning, documented training runs and reproducible evaluations. These artifacts are important for audits and forensic analysis after incidents.
For the process industry we implement human-in-the-loop mechanisms, alert thresholds and safe fallback processes. Copilots receive clear boundaries: for critical decisions human approval remains mandatory, and all recommendations are accompanied by explanations and confidence scores.
Finally, we work closely with compliance and QA teams to create validation plans, build automated test pipelines and conduct regular reviews. This ensures models are not only developed securely but remain secured throughout their lifecycle.
Data pipelines are the backbone of any productive AI solution. In the process industry data flows together from sensors, lab instruments, MES and ERP systems. A robust ETL architecture ensures that data is delivered to models and dashboards reliably, with low latency and in the right quality.
An enterprise knowledge system built on technologies like Postgres + pgvector enables semantic search across documentation, experimental data and SOPs. Such systems reduce onboarding time, improve troubleshooting and make expert knowledge scalable.
Data hygiene is also crucial: labeling processes, metadata management, data quality metrics and continuous monitoring (data drift). Without these foundations models are vulnerable to performance degradation and unforeseen errors in production.
We implement modular pipelines that are easily extendable and adhere to clear SLAs. This turns data flows into reliable production resources rather than bottlenecks.
Costs vary greatly depending on requirements: data volume, required high availability, compliance requirements and desired automation levels are decisive. A basic setup with storage, container orchestration and initial GPU resources is more affordable than a highly redundant, multi-site cluster with full DR mechanisms.
Typical cost blocks include hardware or hosting costs (e.g. Hetzner or colocation), network and storage (MinIO), orchestration and deployment tools (Coolify, Traefik), security components and personnel resources for operations and DevOps. Additionally, developer, data science and validation efforts are required.
We work with modular roadmaps: an initial PoC focuses on proof of concept and requires comparatively low investment. For production we plan a budget that covers both infrastructure and operationalization (monitoring, backups, compliance). We are happy to prepare a concrete, transparent cost estimate for your scenario.
A practical approach is gradual scaling: start small, prove success metrics and then expand capacity strategically. This minimizes risk and provides predictable investment cycles.
Scaling depends on several factors: complexity of integration (connecting to LIMS/MES/ERP), requirements for latency and availability, validation needs and necessary adjustments from user feedback. In many cases a pilot can be made production-ready in 1–3 months; more complex, highly regulated environments may require 6–12 months.
Key steps are: stabilizing the model, integrating into authentic data streams, load testing, security reviews and building monitoring and rollback mechanisms. User training is also part of production readiness — a step often underestimated.
We recommend an iterative rollout: initially limited deployment in non-critical areas, then gradual expansion after successful evaluation cycles. This limits risk and allows practical insights to be incorporated quickly.
Our co-preneur way of working supports exactly this process: we work temporarily within the team, deliver prototypes, accompany integration and training and take responsibility for defined outcomes during the scaling phase.
Contact Us!
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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