Why do chemical, pharmaceutical and process companies in Dortmund need targeted AI enablement?
Innovators at these companies trust us
Local challenge: combining competence and safety
In many operations of the chemical, pharmaceutical and process industries in and around Dortmund the greatest risks are not only in machines or raw materials, but in how knowledge, processes and safety requirements interact. Teams increasingly understand the potential AI can bring, yet practical skills, safe standards and clear scenarios for how AI can be responsibly embedded into laboratory workflows, process documentation and safety copilots are missing.
Why we have the local expertise
Reruption is based in Stuttgart, travels regularly to Dortmund and works on-site with customers from North Rhine-Westphalia. We do not claim to have a Dortmund office; our experience is based on numerous deployments in regional production, research and logistics environments where we have enabled and accompanied teams directly next to their processes. This proximity allows us to tailor technical solutions precisely to local operating cultures and regulatory frameworks.
Our work does not start in the server room but at the workbench, in the lab and in the conference room with leaders. It is precisely there that our executive workshops guide decision-makers through strategic options, while bootcamps and on-the-job coaching enable specialist departments to introduce concrete automations, prompting practices and governance rules. Speed and pragmatism are central principles: we deliver prototypes and training that are immediately applicable.
Our references
For companies with high process and quality demands our industry experience is relevant: with TDK we worked on a topic that directly intersects the chemical-technical domain — PFAS removal technology and the delineation of technical, regulatory and go-to-market aspects. Such projects require an understanding of chemical processes and the ability to translate technical knowledge into clear, safe applications.
In manufacturing and production our work with Eberspächer has shown how AI-supported analysis and optimization of production noise and process data lead to measurable efficiency and quality gains. For document and knowledge work we collaborated with FMG on AI-supported research and analysis, a transfer that directly leads to improved knowledge search and laboratory process documentation in chemical and pharmaceutical companies. Additionally, our cooperation with education and training partners such as Festo Didactic has shown how digital learning platforms can make technical training scalable.
About Reruption
Reruption builds with the co‑preneur method: we act like co-founders within the organization, take responsibility for outcomes and deliver functional solutions, not just concepts. Our focus is on four pillars: AI Strategy, AI Engineering, Security & Compliance and Enablement — exactly the combination that chemical, pharmaceutical and process-intensive operations need to make AI safe and productive.
Our enablement offering includes executive workshops, department bootcamps, an AI Builder Track for non-tech creators, enterprise prompting frameworks, playbooks, on-the-job coaching and support in building internal communities of practice. Especially in environments with high safety and compliance requirements we place particular emphasis on secure internal models and clear governance.
Interested in practical AI enablement for your team?
We travel to Dortmund regularly, come to your site and show how executive workshops, bootcamps and on-the-job coaching can make your processes safe and productive.
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 for chemical, pharmaceutical & process industries in Dortmund: deep dive
The transition from manual, document-driven workflows to AI-supported processes is not a luxury but a strategic necessity in the chemical, pharmaceutical and process industries. In Dortmund, a hub that has shifted from steel to software, industrial DNA and modern technology competence meet — an ideal starting point for targeted AI enablement.
Market analysis and regional dynamics
North Rhine-Westphalia is a dense network of research institutions, suppliers, logistics and IT providers. For process industries this means short distances to technology partners, intensive exchange with universities and a strong regulatory environment. Dortmund itself benefits from this density; innovations are not only discussed here but tested in real production lines.
From the perspective of a CIO or head of production, the main drivers for AI investments in the region are: cost and energy optimization, quality improvement, traceable compliance and reduction of downtime. These drivers also define which use cases should be implemented first.
Specific use cases
Four use cases deserve special attention in chemical, pharmaceutical and process industries: lab process documentation, safety copilots, knowledge search and secure internal models. Lab process documentation can be automated with AI by merging, annotating and standardizing measurement values, lab notes and test protocols. This increases reproducibility and accelerates audits.
Safety copilots support operators in real time by explaining process deviations, giving recommended actions and ensuring that safety regulations are followed. Such systems require robust, tested models and a clear escalation logic so that human-machine interaction remains trustworthy.
Knowledge search is the lever for everyone who makes decisions in complex processes: AI can consolidate historical experiment data, operating manuals, SOPs and external literature into a semantic layer so engineers and researchers arrive at reliable answers faster.
Secure internal models are the prerequisite for productive use: models trained only on internal data or operated in a local data center prevent data leakage and are easier to validate; governance mechanisms ensure auditing, versioning and rollback.
Implementation approach: from workshops to on-the-job
Our enablement modules are designed to build a direct bridge from strategy to operational implementation. Executive workshops create decision pressure and prioritization, department bootcamps enable specialist departments, the AI Builder Track turns non-tech employees into productive creators, and on-the-job coaching secures the transfer into daily work.
A typical project starts with a two-day executive workshop, followed by focused bootcamps in the lab, production and quality assurance. In parallel we build a proof-of-concept that usually delivers first results within days to a few weeks. Afterwards, playbooks, prompting frameworks and support for building an internal community of practice follow.
Technology stack and integration issues
The technical base ranges from secure local model infrastructures to hybrid cloud setups and specialized on-premise solutions for sensitive lab data. Key components include data ingestion pipelines, MLOps for model management, access control as well as logging and monitoring for compliance. For many customers in the process industry the ability to run models offline or in certified data centers is a must.
Integration problems often arise at interfaces: old MES/SCADA systems deliver data in proprietary formats, laboratory information management systems (LIMS) are not standardized, and organizational silos prevent fast data exchange. Technical adapters, semantic mapping layers and pragmatic data governance rules are therefore part of every implementation.
Success factors and common pitfalls
Success depends less on the newest model architecture and more on clear use cases, clean data access and the involvement of operations. A common mistake is treating AI as a research experiment rather than as a product that requires maintenance, monitoring and clear responsibilities. Another error is underinvesting in training and change management: without practical training units many models remain unused.
Transparency, documented performance metrics and robustness tests are required to build trust. Equally central: regulatory traceability. For pharmaceutical applications audit trails, validation processes and documented decision paths are indispensable.
ROI, timelines and team requirements
The return on an enablement program often becomes visible within months: faster root-cause analysis, shorter experiment cycles, less rework and reduced downtime. A pragmatic PoC can be realized in 4–8 weeks; wide-scale rollouts and governance implementation typically take 6–18 months, depending on complexity and regulation.
The core team should mix domain experts (process engineers, QA, lab leads), AI enablers (AI builders, data engineers) and governance owners. Executive sponsorship is another key factor: without continuous support from the top, projects stagnate.
Change management and building internal capabilities
Training alone is not enough; practiced spaces are needed. Our department bootcamps are therefore hands-on and complemented by on-the-job coaching: participants work directly with relevant data and tools, accompany live operations and learn prompting and model understanding in real situations. This creates sustainable competence.
Long term, resilience builds through community building: internal communities of practice share best practices, maintain playbooks and ensure that knowledge is not lost. These communities are the lever that turns temporary successes into lasting productivity gains.
Safety, compliance and governance
Safety and compliance requirements are particularly strict in chemical and pharmaceutical sectors. We implement governance frameworks that include model registration, access controls, explainability standards and audit logs. We also recommend hybrid architectures that keep sensitive training data within certified infrastructures.
Regular validation, re-training cycles and documented release processes prevent drift and ensure that models remain clinically and operationally meaningful. Only in this way can the balance between innovation speed and regulatory safety be maintained.
Ready for the next step?
Book an initial conversation or an AI PoC to validate concrete use cases in lab, production or quality assurance.
Key industries in Dortmund
Dortmund has become a hub emerging from structural change, combining traditional industrial competence with modern services and tech offerings. Decades ago the region was known primarily for steel and coal; today logistics centers, IT service providers and energy companies shape the landscape. This mix creates a particular dynamic for the process industry, as manufacturers find strong partners here in data infrastructure, automation and energy supply.
The logistics sector in and around Dortmund ensures that chemical and pharmaceutical raw materials and products are distributed quickly and reliably. AI-driven planning systems, predictive maintenance and optimized supply chains are core applications directly relevant to process companies: fewer delays in production flow, better traceability and more efficient inventory management.
IT service providers and software companies deliver the digital infrastructure modern process plants need. From data platforms to cloud and edge solutions, local IT firms support the digitization of lab and production processes. This proximity facilitates proofs-of-concept and pilot phases because integration partners can be on-site quickly and understand industry-specific requirements.
Insurers and financial service providers in Dortmund shape the risk and coverage landscape. For chemical and pharmaceutical companies this means: risk reduction models, predictive maintenance and safety copilots can also influence insurance processes by demonstrably reducing risks and recalculating premiums.
The energy sector plays a dual role for the process industry: as a supplier and as a partner for energy management. AI-driven optimization of energy consumption, load shifting and predictive control are elements with which process plants can lower operating costs and meet sustainability targets.
Together these industries create an ecosystem that provides ideal conditions for AI enablement: access to data, integration competence, short cooperation routes and market demands that generate innovation pressure. For chemical, pharmaceutical and process companies this means targeted enablement scales particularly quickly because the environment already provides the necessary building blocks.
Furthermore, the migration background of many companies in Dortmund is relevant: businesses that were previously mechanically oriented are now investing in digital capabilities. This cultural momentum eases the transfer of AI solutions from pilots into productive use.
Finally, local research and higher-education institutes are important partners for validating new methods. Collaborations with science and applied research are a gateway to mastering demanding regulatory tests and validations in the pharmaceutical context.
Interested in practical AI enablement for your team?
We travel to Dortmund regularly, come to your site and show how executive workshops, bootcamps and on-the-job coaching can make your processes safe and productive.
Important players in Dortmund
Signal Iduna is one of the major insurance groups with a strong presence in Dortmund. The company plays an important role in risk management for industrial customers and drives digital services relevant to chemical and pharmaceutical companies, for example in reinsurance, business interruption coverage and predictive risk analysis.
Wilo, known for pumps and pumping systems, has its roots in the region and stands for industrial engineering. For the process industry efficient pump systems and their digital monitoring are crucial; Wilo is moving here toward predictive maintenance and user-centered service concepts with digital solutions.
ThyssenKrupp has left a lasting mark on the industrial landscape of the Ruhr area. Even though parts of the group are globally organized, local industrial expertise remains relevant — especially when it comes to robust manufacturing processes, materials science and large-scale plants used in process industries.
RWE is a central energy supplier and partner for large industrial consumers. For chemical and pharmaceutical companies stable energy prices and partnerships for load management and flexible energy supply are important topics. RWE also advances digital energy solutions that require integration with factory controls and energy management systems.
Materna is an IT service provider with a strong regional connection and offers solutions for authorities and companies. Materna can act as an integration partner, for example in building data protection-compliant platforms for lab and production data, and brings experience in handling complex, regulated IT landscapes.
Together these local actors form an ecosystem that opens up diverse cooperation opportunities for process companies: from IT integration to energy partnerships to insurance and service concepts. For chemical and pharmaceutical companies this results in a range of supporting competencies that accelerate regional innovation projects.
In addition, medium-sized suppliers and specialized engineering firms in the Dortmund area are important innovation drivers. They deliver tailored solutions for process automation and contribute to the rapid implementation of prototypes into operations.
The local scene is characterized by pragmatic speed: decisions are implemented, pilot projects scaled quickly and learning cycles kept short. These qualities are precisely what is required when introducing AI projects into safety-critical production environments.
Ready for the next step?
Book an initial conversation or an AI PoC to validate concrete use cases in lab, production or quality assurance.
Frequently Asked Questions
Expected results vary depending on the use case, data situation and organizational maturity. As a rule, we deliver tangible results from a PoC within 4–8 weeks: this can be an improved search function for lab reports, a prototype of a safety copilot or an automated classification model for process documentation. These early results are designed to be immediately usable and measurable.
The second phase includes refinement, validation and scaling. In this phase models are hardened, integration with existing systems is implemented and governance mechanisms are put in place. This phase typically requires another 3–6 months, depending on the complexity of interfaces and regulatory requirements.
Long-term benefits — such as significant reductions in downtime, faster onboarding of new employees or notable quality improvements — often become visible within 6–18 months. Crucial is management commitment, resources for data engineering and regular evaluation cycles.
Practical advice: start with a small, clearly defined use case that has high visibility and measurable KPIs. This builds trust and budget for the next expansion stage. Our modules are designed to intentionally support these steps: executive workshops for prioritization, bootcamps for operational enablement and on-the-job coaching for transfer into daily work.
Safety and compliance must be part of the design from the start, not an afterthought. This begins with data intake: only verified, versioned data should enter training pools, and incremental validation tests must be carried out as standard. In pharmaceutical contexts documented validation processes, audit trails and reproducible experiments are mandatory.
Technically, we rely on architectures that allow clear access controls, logging and model registration. Hybrid approaches — local model inference for sensitive data combined with central orchestration — are often the best option to combine data sovereignty and scalability. Additionally, explainability methods are necessary so operators and auditors can understand why a model makes a recommendation.
Organizationally we recommend establishing clear roles: model owners, data stewards, compliance officers and operational champions. Regular reviews, re-training cycles and a change management process ensure that models do not drift unnoticed after changes in processes or raw materials.
Practical implementation tips: start with non-critical but relevant use cases (e.g. assistance with lab protocols), use playbooks for validation and ensure training and production environments are separated. Our enablement modules include governance training and playbooks that address exactly these requirements.
Internal communities of practice are the backbone of sustainable capability development. They create spaces where domain expertise, technical knowledge and successful approaches are shared. For complex areas like lab processes or safety copilots informal exchange is often the lever that ensures learnings from pilot projects are widely available.
These communities consolidate successes, catalog lessons learned and maintain playbooks so new teams do not have to start from scratch each time. They also serve as a means of quality assurance: peer reviews of prompt designs, model performances and validation protocols increase the robustness of solutions.
Organizationally, communities should be formally supported: time budgets for members, clear responsibilities for knowledge documentation and platforms for exchange (e.g. internal wiki, regular brown-bag sessions). This creates networks that combine domain knowledge with technical know-how.
Our approach is pragmatic: we support the setup of such communities through moderation, templates for playbooks and initial training. The goal is for these groups to autonomously maintain best practices and advance new use cases after 6–12 months.
Sensitive data requires special care. First we assess which data is actually necessary and whether it can be used in anonymized form. Where anonymization is not possible, we rely on locally operated models or encrypted data pipelines that ensure raw data does not leave the company.
Technically we use data access rules, isolation environments and MLOps practices to strictly separate training and production data. Versioning, hashing and audit logs are standard to ensure traceability. In regulated environments we supplement these measures with formal validation protocols.
Another approach is to use synthetic data for early model iterations: synthetic data generators can help train initial models and test workflows without exposing real sensitive data. Later, models are fine-tuned with real, controlled datasets.
Our recommendation: define a data classification early, implement access controls and include legal and privacy questions in project planning. In our enablement workshops we address these aspects hands-on and provide templates for policies and technical implementations.
The effectiveness of training depends heavily on the audience. For executives compact executive workshops are suitable; they convey strategic options, risks and ROI considerations and help set priorities. These formats are designed to accelerate decision-making and budget allocation.
Role-specific bootcamps for lab, quality and operations must be practical and hands-on: participants work with real datasets, practice prompting, interpret model outputs and implement small automations. Such bootcamps minimize the gap between theory and application and increase willingness to adopt new tools.
The AI Builder Track targets technically non-specialist creators who still need to build tools. It combines low-code/no-code techniques with basic data literacy modules and enables employees to develop their own prototypes. For technical teams deeper workshops in MLOps, data engineering and model validation provide the necessary depth.
On-the-job coaching is then the link to productive use: trainers accompany teams at the workplace, help with integration and debugging and ensure that training content is transformed into concrete processes. Our experience shows: combinations of workshop, bootcamp and subsequent accompaniment produce the most sustainable effects.
Costs depend on scope: a targeted AI PoC (technical prototype with evaluation and roadmap) starts with our standard offering at €9,900, which is often used as an economically justifiable entry. A comprehensive enablement track with workshops, bootcamps, on-the-job coaching and governance support scales depending on team size and intensity and is quoted individually.
Important are not only financial resources but also personnel capacity: you need internal champions, data owners and IT support to provide infrastructure. For short, successful projects 1–2 internal FTE equivalents on a project basis plus operational involvement from some specialists are often sufficient.
A pragmatic entry reduces risk: start with a narrowly defined use case, measure KPIs and then decide on scaling. This keeps initial costs controllable and makes the business case robust.
We support such decisions in executive workshops and provide concrete roadmaps with effort estimates, technology recommendations and governance checklists so companies in Dortmund and the surrounding area can decide with confidence.
Contact Us!
Contact Directly
Philipp M. W. Hoffmann
Founder & Partner
Address
Reruption GmbH
Falkertstraße 2
70176 Stuttgart
Contact
Phone