Why do the chemical, pharmaceutical and process industries in Berlin need a tailored AI strategy?
Innovators at these companies trust us
The challenge on the ground
In Berlin's dynamic tech ecosystem, high innovation potential meets strict regulatory requirements and complex process landscapes. Companies in the chemical, pharmaceutical and process industries struggle to identify promising AI projects and to set them up so they can be scaled sustainably from a regulatory, technological and organizational perspective. Without clear prioritization, there is a risk of high expenditure with little impact.
Why we have the local expertise
Although our headquarters are in Stuttgart, we are regularly on site in Berlin and work closely with leadership teams, lab managers and production experts. We understand how Berlin’s startup culture and the establishment of research institutions interact with industrial requirements — that is precisely where we start when designing AI strategies.
Our work begins with a view of the local conditions: talent from universities, regulatory discussions, proximity to tech startups and the specific requirements for secure internal models and compliance in regulated industries. This perspective allows us to build pragmatic roadmaps that ensure both innovation speed and traceability.
Our references
For the process industry and manufacturing we worked with Eberspächer on solutions for noise reduction in production processes — a project that combined data capture, signal processing and robust model validation under strict manufacturing conditions. The experience from this project helps translate industrial measurement data into actionable inputs for AI strategies.
With STIHL we collaborated over multiple years on product and education initiatives ranging from customer research to production-near digital tools; these projects demonstrate how long-term cooperation leads to product-market fit. For chemistry-adjacent technology projects, the know-how from such long-term product developments is relevant.
In the field of technology-driven product development and implementation strategies we worked with TDK on PFAS removal technologies and with Flamro on intelligent chatbots and technical consulting services. These projects provide transferable knowledge for secure models, regulatory validation and field tests in critical environments.
About Reruption
Reruption was founded on the conviction that companies should not only react, but actively redesign their business models. Our co-preneur approach means we embed ourselves in your organization like co-founders: we take responsibility, deliver at high speed and connect strategy with technical execution.
We combine strategic clarity with engineering depth: from the assessment phase to piloting we deliver working prototypes, clear KPIs and an actionable roadmap for scaling. In Berlin we work on site with decision-makers, lab staff and IT teams, without claiming to have an office there — our headquarters remain in Stuttgart.
Our focus is on four pillars: AI Strategy, AI Engineering, Security & Compliance and Enablement. This combination makes us the partner for companies seeking robust, scalable and compliant AI solutions.
Would you like to explore your AI potentials in the chemical or pharmaceutical industry in Berlin?
Contact us for a short assessment: we come to Berlin, work on site with your teams and create a prioritized roadmap for AI pilots.
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 Berlin: a comprehensive roadmap
Developing a viable AI strategy for chemical, pharmaceutical and process industries requires a deep understanding of technical, regulatory and organizational aspects. Berlin offers access to talent and digital expertise, while strict compliance and quality requirements apply. A structured roadmap reduces risk and maximizes economic benefit.
Market analysis and environment
Berlin is not primarily a chemical hub like Leverkusen, but the city has established itself as a nexus for research, biotech startups and digital expertise. This means: local partnerships with software and data experts are easily accessible, research collaborations with universities and institutes are possible, and investors see potential in technology-driven solutions for regulated industries.
For decision-makers this means concretely: leverage Berlin’s breadth of tech services for rapid prototyping and combine these with tightly controlled pilot setups in your production or laboratory environments. This way you can marry innovation speed with process safety.
Concrete use cases
In chemical and pharmaceutical fields four classes of use cases repeatedly appear: laboratory process documentation, Safety Copilots, knowledge search and secure internal models. Laboratory process documentation automates the capture of work steps and promises higher reproducibility and lower audit risk. Safety Copilots support employees with context-sensitive guidance and reduce human errors in critical procedures.
Knowledge search links historical experimental data, SOPs and literature and makes implicit knowledge accessible. Secure internal models — for example for predicting reaction kinetics or quality deviations — must not only be performant but also explainable and data-protection compliant.
Use-case discovery and prioritization
Our approach begins with an AI Readiness Assessment and a broad use-case discovery across 20+ departments to uncover data sources, bottlenecks and value drivers. In Berlin we often use this phase to identify interfaces to local tech partners and academic research.
The subsequent prioritization combines business-impact models, feasibility analyses and regulatory evaluation fields. The result is a portfolio of short-term realizable pilots and strategic initiatives that promise the highest returns in the medium term.
Technical architecture & model selection
Technically we recommend modular architectures: robust data foundations, clear APIs and consistent interfaces to model-serving platforms. For the process industry on-premise or hybrid deployments are often necessary to meet regulatory requirements and protect IP.
Model selection is guided by use case, data availability and security requirements: for documentation or knowledge-search applications, NLP models with fine-tuned vocabularies are appropriate, whereas structured ML models or physics-integrated approaches suit process predictions. We quantify performance, cost per run and robustness already in the PoC phase.
Data foundations & integration strategy
Many projects fail due to data quality: incomplete metadata, different formats from lab and production systems and siloed knowledge are central problems. A Data Foundations Assessment identifies data sources, necessary transformation processes and governance rules.
In Berlin we often work with local integrators and cloud providers to implement hybrid architectures: sensitive measurement data remains in secure environments while aggregated insights flow into cloud-supported platforms. The key is a pragmatic, iterative approach that quickly delivers reliable results.
Pilot design, KPIs and business case
A pilot must have clear success criteria: reduction of error rates, time savings in documentation, higher yields or reduced downtime. We define KPIs for precision, cost per process run and time to market readiness.
Business-case modeling considers both direct effects (OEE, throughput) and indirect benefits such as faster product development or lower audit risks. In Berlin we help teams design pilots so they demonstrate scalable operating models.
Governance, compliance and secure models
Regulated industries need a strong AI governance framework: roles, data access rules, model monitoring and audit trails. Especially for models that influence decisions, explainability and validation processes are mandatory.
We design policies for model versioning, retraining triggers and incident response. In Berlin it is important to align these requirements with compliance teams, external auditors and, where necessary, authorities to ensure operational safety and regulatory traceability.
Change management & enablement
Technology alone is not enough: change and adoption planning is crucial so that employees accept tools and processes are sustainably changed. We rely on training programs, co-piloting phases and the involvement of multipliers from labs and production.
Our enablement modules address both technical skills and process understanding and responsibilities. In Berlin we leverage the broad talent pool for complementary training and thus accelerate operational adoption of AI solutions.
Implementation timeline, team and ROI expectations
Typical timeframes: 4–8 weeks for an AI Readiness Assessment & use-case discovery, 6–12 weeks for a reliable PoC and 6–18 months for scaling depending on complexity. The team composition includes data engineers, ML engineers, domain experts from chemistry/pharma and compliance managers.
ROI depends on the use case: automated documentation can deliver significant cost savings within a year, Safety Copilots reduce error costs in the long term. Our business cases present conservative and aggressive scenarios so leaders can make informed decisions.
Common pitfalls
Expected challenges include insufficient data quality, unrealistic targets, poor integration into operational processes and underdimensioned governance. A structured, iterative approach with clear milestones avoids many of these pitfalls.
We recommend involving auditors and compliance teams early, defining clear KPIs and designing pilot projects so that they can be quickly scaled if successful. In Berlin local partnerships are often used to accelerate this process.
Ready for a reliable proof-of-concept?
Book our AI PoC for €9,900: In a few days you will receive a working prototype, performance metrics and a clear production roadmap.
Key industries in Berlin
Historically Berlin has evolved from an industrial factory city into a European hub for research, the creative industries and technology. While traditional chemical centers are located elsewhere, Berlin has built a strong base for biotech, pharmaceutical research and process-related digitization through its universities, hospitals and research institutions. This mix creates an environment where digital solutions can be prototyped quickly and linked to clinical or laboratory expertise.
The tech and startup scene gives the city an innovation tempo that is essential for developing data-driven solutions in regulated industries. Startups bring modern software stacks, agile methods and access to talent — a combination that helps established chemical and pharmaceutical companies inject speed into their innovation projects.
At the same time there is a strong presence of service providers and specialized consultancies that know production processes, quality management and regulatory requirements. These service providers close the gap between modern data-science approaches and physical-chemical processes in labs and manufacturing.
Another driver are research institutions and hospitals like the Charité, which serve as partners for clinical studies, validation work and access to specialized research data. For pharmaceutical companies this proximity is particularly valuable: it enables early validation steps and collaborations that would otherwise be difficult to orchestrate.
Funding and the investor landscape in Berlin support early innovation phases. Business angels and VC funds with a tech focus facilitate the scaling of digital solutions that create value in lab or production environments. This availability of capital is a significant advantage for pilots and the implementation of AI strategies.
The challenges are clear: fragmented data landscapes, high regulatory hurdles and the need for robust validation processes. This is exactly where AI strategies can be a lever: through targeted use-case prioritization, companies in Berlin can quickly create value while ensuring compliance.
For the chemical and pharmaceutical sectors topics like secure model deployment, explainability and auditability are central. Berlin offers the competence to address these topics technologically — through an ecosystem of developers, researchers and consultants who work together on practical solutions.
In short: Berlin is a place where digital strategies for regulated industries can emerge — the potential lies in the combination of research, tech talent and an open founder culture that combines experimental approaches with industrial rigor.
Would you like to explore your AI potentials in the chemical or pharmaceutical industry in Berlin?
Contact us for a short assessment: we come to Berlin, work on site with your teams and create a prioritized roadmap for AI pilots.
Key players in Berlin
Zalando as a large e-commerce player in Berlin has shown how data-driven processes can optimize customer experience and logistics. Although Zalando primarily operates in retail, the company shapes the local tech scene through data expertise, machine-learning initiatives and a culture of rapid experimentation — resources from which industry-related projects can also benefit.
Delivery Hero has built a strong engineering presence in Berlin and demonstrates how real-time data and process automation can be scaled. The expertise in resilient platforms and scalable architectures is relevant for the process industry, which has similar requirements for availability and data integrity.
N26 stands for modern product thinking and regulatory sensitivity in the digital space. Their handling of compliance requirements and secure system architectures offers valuable impulses for pharma and chemical companies that need to design their digital services to be compliant and user-centered.
HelloFresh combines logistical complexity with data-driven planning. The optimization of supply chains and quality assurance in a highly automated environment provides methods and technical patterns that can be transferred to process plants and production chains in the chemical and pharmaceutical industry.
Trade Republic has built a modern platform in Berlin that combines regulatory requirements with high scalability. The lessons learned from building audit trails, security standards and transparent processes are direct inspirations for AI governance in regulated industries.
In addition to these big names, Berlin has a dense network of startups, research labs and consultancies working on AI, data engineering and health-tech. This local diversity provides a positive lever: companies in the chemical and pharmaceutical sectors can selectively find partners for prototyping, validation or long-term system integration.
Research institutions, incubators and industry networks complement the ecosystem and make it easier to access specialized professionals. For companies in regulated industries this is a decisive advantage because technical know-how can be quickly combined with domain expertise.
Overall, the Berlin ecosystem shows how cross-industry best practices from fintech, e-commerce and platform economics can be applied to the challenges of the process industry — provided governance, compliance and data foundations are properly considered.
Ready for a reliable proof-of-concept?
Book our AI PoC for €9,900: In a few days you will receive a working prototype, performance metrics and a clear production roadmap.
Frequently Asked Questions
The starting point is a focused AI Readiness Assessment: we analyze data availability, the IT landscape, regulatory requirements and the organizational structure. In Berlin we often complement this analysis with a mapping phase involving local technology partners and research institutions to quickly explore prototyping potential.
Next comes a broad use-case discovery across 20+ departments: lab, quality assurance, production, R&D and supply chain. This discovery is not just a collection of ideas but a structured evaluation of impact, feasibility and time-to-value.
Prioritization combines quantitative business-case models with qualitative criteria such as compliance risk and change readiness. The result is a portfolio of short-, medium- and long-term initiatives and a clear plan for piloting and scaling.
Practical takeaways: start small, define clear KPIs, involve compliance early and assemble cross-functional teams. In Berlin it makes sense to use local tech partners for the prototype phase to shorten time-to-proof and bring knowledge into the company.
In regulated environments, use cases for laboratory process documentation often deliver quick value by automating repetitive documentation tasks, improving reproducibility and reducing audit burden. Such projects reduce manual errors and create reliable evidence for regulators.
Safety Copilots are another high-leverage area: AI-assisted systems provide workers with real-time guidance and reduce human errors in critical processes. The combination of sensor data, SOP information and contextualized predictions offers measurable risk reduction.
Knowledge search systems incorporate documented and unstructured knowledge into searchable repositories, saving time especially in long-running R&D projects. For companies with many experimental runs and historical data this is a quick productivity gain.
Finally, secure internal models trained specifically for process predictions or quality controls are valuable. Critical here are validation, explainability and the definition of retraining triggers to ensure predictions remain reliable over time.
Practical recommendation: prioritize use cases based on data maturity and potential business impact, and design first PoCs to guarantee auditability and traceability from the outset.
Compliance in chemical, pharmaceutical and process industries is not optional — it is operationally binding. Start with a governance framework that defines responsibilities, audit trails, data access rights and processes for model validation. The framework should be jointly owned by legal, QA and IT.
Technically this means: data minimization, encryption of sensitive measurement series, model versioning and traceable validation protocols. For ML models, test scenarios with edge cases, stress tests and performance monitoring after rollout are mandatory.
Explainability approaches help make decisions understandable; in many regulatory contexts it is important to present a model’s decisions or recommendations in a comprehensible way. Also document all training data, hyperparameters and evaluation metrics.
Practical steps: involve auditors early in the project cycle, establish regular model reviews and plan mechanisms for incident response. In Berlin you can quickly find external compliance and audit partners to strengthen validation processes.
Data preparation is often the most time-consuming part of an AI project. Lab and production data come in many formats: sensor logs, electronic lab notebooks, ERP/PLM systems or handwritten notes. The challenge lies in standardizing, annotating and linking these heterogeneous sources.
A Data Foundations Assessment identifies required transformation steps, data quality issues and missing metadata. In practice, a combination of automated ETL pipelines, manual annotations and ontology building leads to reliable datasets.
Automating documentation in the lab greatly reduces future preparation effort: if structured metadata is captured at the point of data generation, the workload for later projects drops significantly. Investing in better data capture processes is worthwhile here.
Specific advice: plan buffers for data cleaning, establish clear data ownership and use local service providers in Berlin for specific integration tasks to shorten time to PoC.
For secure models we prefer modular, hybrid architectures: sensitive raw data stays in on-premise or private cloud environments while aggregated and anonymized data is processed in scalable cloud services. APIs enable integration of models into existing production systems.
Clear interfaces for model serving, monitoring and retraining are important. Model monitoring includes drift detection, performance metrics and alerting. Only then can production risks be detected early and countermeasures initiated.
Security aspects include encryption, access controls, secrets management and role-based access models. For highly regulated use cases we recommend additional measures such as secure enclaves or dedicated on-prem model-serving infrastructures.
In practice: start with a hybrid prototype, validate architectural assumptions in a protected pilot and scale step by step after positive validation and compliance checks.
The time to visible results varies by use case and data situation. Typically a well-defined PoC delivers first reliable insights within 6–12 weeks: feasibility, initial performance metrics and a clear impression of the production work required.
Preparatory work such as data cleaning or connections to lab and production systems can extend the time to first results. Therefore we recommend planning these tasks already in the discovery phase and, where possible, executing them in parallel with prototypical model tests.
It is important to define success criteria clearly: a PoC is successful when it reaches concrete KPIs and at the same time shows a clear production path including governance and integration requirements. That creates the basis for scaled implementation.
In Berlin it is advisable to use local technology partners for rapid iterations, which can shorten development cycles — provided the data infrastructure is prepared.
Change management starts with involving the affected stakeholders: lab staff, production managers, QA and IT. Communicate goals early, expected changes to workflows and the benefits for employees. Transparency creates acceptance.
Operationalize change through trainings, co-piloting phases and the appointment of champions within business units. Small, tangible successes in pilot projects help build trust and reduce skepticism.
An enablement plan should include technical training, process adjustments and the definition of new roles (e.g., model owner). In the long term it makes sense to establish a knowledge base and recurring training cycles to anchor know-how in the company.
Practical tip: measure adoption with concrete KPIs (usage rate, reduction of manual interventions, time savings) and use these data to prioritize further rollouts and steer communications.
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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