How can AI engineering and production-ready models transform the chemical, pharmaceutical and process industries in Berlin?
Innovators at these companies trust us
Local challenges
Research labs, production lines and process plants in Berlin are under high pressure: strict regulation, incomplete lab and process documentation, and the need to meet safety and compliance requirements without losing productivity. Data is often fragmented across lab notebooks, MES systems and Excel spreadsheets — this prevents fast decision-making.
Why we have the local expertise
Reruption is headquartered in Stuttgart but travels to Berlin regularly and works on site with clients. We don't come as distant consultants, but as embedded team members who understand real production problems and drive technical solutions into production. Our co‑preneur approach means: we take responsibility for outcomes, not just recommendations.
In Berlin we combine the speed and creativity of the local startup scene with industrial experience. We know the expectations of research institutions, labs and manufacturing companies in the capital region and speak the language of compliance, QA and engineering teams alike. On site we work closely with data, IT and domain teams to establish robust data transformation and deployment paths.
Our work is pragmatic: we build production-ready pipelines, private models and self-hosted infrastructure that meet Germany's stringent data protection and security requirements. We rely on technology stacks that scale in practice — from Postgres + pgvector to MinIO and containerized deployments with Traefik.
Our references
For the process industry our manufacturing projects are particularly telling: with STIHL we supported multiple products over two years from customer research to product-market fit, including tools like training platforms and ProTools that digitalize operational processes and train employees. These experiences translate directly to lab and manufacturing processes where documentation and training are central.
With Eberspächer we developed AI-powered solutions for noise reduction in production processes — an example of how signal and sensor data can be made industrially useful. Projects like these show how processing operations can be optimized through data while respecting safety requirements.
We also bring experience from technology-oriented projects: for BOSCH we did go-to-market work for new display technologies, and with TDK we were involved in validating and spinning out PFAS removal technology. This work connects product development, regulatory sensitivity and technical scaling — aspects that are also crucial in chemical and pharmaceutical domains.
About Reruption
Reruption builds AI products and capabilities directly inside client organizations: fast prototypes, production roadmaps and operational implementations. Our co‑preneur mentality means we act like co-founders: fast, accountable and technically grounded. We help companies not only test ideas but build production-ready systems.
We don't optimize the existing — we build what replaces it. In Berlin we help companies establish and sustainably operate Safety Copilots, secure knowledge systems and private models in production.
Would you like to improve your lab and process documentation with a Safety Copilot?
We define the use case, build a functional prototype and demonstrate in a live demo how your team works productively with the system. We work on site in Berlin regularly and accompany you through to production rollout.
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, pharma & process industries in Berlin: a deep dive
The Berlin metropolitan region is a vibrant ecosystem of research, startups and applied industry — a perfect base for implementing sophisticated AI systems in chemical, pharma and process industries. Universities, clinical research and industrial manufacturing converge there, so data-driven solutions can add value from early lab processes to the production line.
But implementation is demanding: production-ready AI requires not just a good model, but clean data pipelines, secure infrastructures, traceability, validation and integrations into existing control and MES systems. Without this holistic view many projects end as proofs-of-concept without lasting impact.
Market analysis and regional dynamics
In recent years Berlin has attracted a strong influx of tech talent, increasing the availability of data scientists, backend engineers and DevOps specialists. At the same time many chemical and pharmaceutical companies in the region are still in transformation — established corporations coexist with young biotech startups. This combination creates demand for modular, quickly integrable AI solutions that allow both strict compliance and fast innovation cycles.
For AI engineering providers this means: solutions must serve both research-adjacent use cases (e.g. assay optimization, lab automation) and industrial requirements (e.g. process monitoring, predictive maintenance). Berlin offers the talent pool, investor scene and network between research institutions and industry partners for this.
Specific use cases in chemical, pharma & process industries
In the lab environment automation of process documentation is a natural fit: from ELN data (electronic lab notebooks) to automated SOP generation. AI can help structure experiment notes, detect anomalies and uncover reproducibility gaps.
In production processes Safety Copilots are a central topic: assistance systems that provide employees with real-time safety-relevant alerts, interpret deviations and generate multi-step action instructions. Such copilots combine NLP capabilities with process data and only become production-ready through robust, verified models.
Other use cases include knowledge search and enterprise knowledge systems for regulatory needs, private chatbots for internal specialist communication without RAG risks, and predictive analytics for throughput optimization and fault detection.
Implementation approach: from PoC to production system
A typical proven path begins with a clearly scoped PoC: use case, success criteria, minimal dataset and a runtime window. Our €9,900 AI PoC offering is aimed precisely at that: to technically prove that a use case works — with a functional prototype, performance metrics and a clear production plan.
Building on the PoC follows an engineering sprint in which data pipelines, model training, evaluation and CI/CD pipelines are created. We always recommend a modular architecture: swappable models, clearly defined API layers (e.g. integrations to OpenAI, Anthropic, Groq) and separation of infrastructure, orchestration and persistence (e.g. Postgres + pgvector for vector search, MinIO for object storage).
Technology stack and self-hosted options
For companies with high data protection requirements, self-hosting is often necessary. Berlin-area firms frequently use European providers like Hetzner combined with tools like Coolify for deployment, MinIO as S3-compatible storage and Traefik as ingress. This combination allows running low-latency, cost-efficient and controlled environments.
On the model side we support a model-agnostic approach: from proprietary LLM APIs to fully private LLMs, connected with enterprise knowledge systems and vector-based search. A clear governance layer is important here: versioning, explainability pipelines, test suites and regular re-validation against production data.
Integration with existing systems and OT/IT boundaries
A central obstacle is integration into process control systems and MES. OT systems are often not designed for cloud interactions; therefore edge gateways, secure bridges and strictly regulated data flows are needed. Our experience shows: an early focus on interface design and security architecture drastically reduces later delays.
Interoperability with LIMS, ELN, SAP and other IT systems is also crucial. A clean data map and common data models make it easier to develop reliable ML pipelines.
Success factors and common pitfalls
Successful projects combine technical excellence with organizational readiness: clear ownership, defined KPIs, governance and change communication that brings operational teams along. Without these elements solutions often fail because they are not embedded in daily workflows.
Typical mistakes are: poor data quality, overly large scope jumps in the PoC, neglecting compliance and underestimating MLOps capacity. These problems can be avoided through incremental releases, automated tests and continuous stakeholder engagement.
ROI considerations and timelines
Companies can expect realistic ROI when cost reductions, error prevention or additional throughput can be quantified. A pragmatic target: PoC in 2–6 weeks, MVP in 3–6 months, and production readiness within 6–12 months — depending on data availability, integration effort and regulatory needs.
ROI models should combine qualitative aspects (safety improvements, compliance assurance) and quantitative measures (reduced downtime, faster throughput, lower personnel costs through automation). Often investments pay off within 12–24 months with clear KPI measurement.
Team, skills and change management
A successful AI engineering team brings together data engineers, backend developers, MLOps specialists, domain experts from chemistry/pharma and accountable process owners. In Berlin many of these profiles can be found, but international recruitment and targeted training are often required.
Change management is not an add-on: training, documented workflows and introduction pilots with super-users ensure sustainable use. We accompany teams through training, documentation and co‑working phases on site in Berlin.
Security and compliance architecture
For chemical and pharmaceutical companies compliance is non-negotiable. Data classification, access control, audit logging and explainable models must be integrated from the start. We recommend encrypted storage layers, role-based access and regular pen tests.
Traceability of decisions is also critical: models need versioning, test datasets and re-training plans to withstand regulatory audits.
Conclusion and next steps
Berlin offers ideal conditions for AI innovation in chemical, pharma and process industries: talent, research and market are ready. What matters is the methodical path from PoC to production: clear use-case selection, robust data pipelines, secure infrastructure and an interdisciplinary team.
We recommend starting with a focused PoC, involving local stakeholders early and pursuing a self-hosted strategy where data protection and compliance require it. Reruption supports you in building and sustainably operating production-ready AI systems in Berlin.
Ready for a technical proof of concept?
Start with our AI PoC (€9,900): prototype, performance metrics and a clear production plan. We come to Berlin, work on site with your team and deliver tangible results.
Key industries in Berlin
Over the past two decades Berlin has evolved from a primarily administrative and cultural metropolis into an international technology and startup hub. The city attracts talent from around the world and is a node for research institutions, universities and applied industry. Historically Berlin was less focused on heavy industry; nevertheless many small manufacturing firms and lab centers emerged along rivers and rail hubs, and today they are integrated into modern research and production networks.
The tech and startup scene is the strongest driver of the local economy: incubators, accelerators and venture capital networks foster rapid innovation cycles. For the chemical and pharmaceutical industries this means access to agile development methods, modern data skills and collaborations with biotech startups that often operate in translational projects and demand fast prototyping.
Fintechs and e‑commerce players shape Berlin's digital backbone. These industries have high requirements for data infrastructure, customer communication and automation — competencies that can be directly transferred to the process industry, for example in building scalable ETL pipelines or designing chatbots for internal requests.
The creative industries contribute a particular cultural openness: a willingness to experiment and interdisciplinary teams foster creative solutions to complex problems. This mentality also favors unconventional approaches in research and production, provided safety and quality standards are met.
At the same time a lively biotech and life-science community is growing in Berlin. Clinical partners, research institutes and biotech startups form an ecosystem relevant for pharmaceutical research. For AI engineering this means use cases around drug research, assay optimization and lab automation are practically implementable.
Regulatory requirements and quality standards also present opportunities: companies that invest early in secure, documented AI processes gain trust from partners and regulators. Especially for chemical and pharmaceutical firms traceable, verifiable ML pipelines are a competitive advantage.
The availability of cloud and self-host providers in Europe, combined with local data centers, enables flexible hosting models. For data- and security-critical applications hybrid architectures are attractive: sensitive datasets remain on-premise while less critical workloads are processed flexibly in the cloud.
Finally, collaborations between universities, Fraunhofer institutes and industry companies shape innovation dynamics. In Berlin projects arise that connect basic research with industrial application — exactly the playground where production-ready AI solutions for chemical, pharma and process industries can be particularly successful.
Would you like to improve your lab and process documentation with a Safety Copilot?
We define the use case, build a functional prototype and demonstrate in a live demo how your team works productively with the system. We work on site in Berlin regularly and accompany you through to production rollout.
Key players in Berlin
Zalando started as an online shoe retailer and evolved into a European platform with high requirements for data processing and personalization. Zalando invests heavily in ML and data engineering and shapes the talent ecosystem in Berlin, from which industrial data projects also benefit. Zalando's presence means many data scientists and MLOps engineers are available in Berlin — an advantage for companies seeking specialized profiles.
Delivery Hero has mastered the logistical challenges of large real-time systems as a global delivery platform. Its technical expertise in routing, scaling and real-time analytics provides important impulses for production and process optimization in other industries: concepts like event-driven architectures and resilient APIs can be directly applied to process data.
N26 shaped the fintech scene in Berlin and shows how regulatory sensitivity can be combined with rapid product development. For chemical and pharmaceutical firms this is an example of how compliance requirements can be paired with modern development methods, especially on topics like audit trails and data security.
HelloFresh is an example of end-to-end supply chain optimization in Berlin: from demand forecasting to operational logistics. Insights from such e‑commerce projects are directly transferable to throughput planning, material flow and inventory optimization in process operations.
Trade Republic further energized the investor and fintech landscape and shows how user-centricity and secure, scalable infrastructure go together. For industrial companies this means: user-centered product development helps make internal tools more user-friendly — an often underestimated success factor when introducing copilots and chatbots.
In addition there are many small and medium technology and biotech firms developing specialized solutions for lab automation, analytics and sensor technology. These companies form a dense network of suppliers, service providers and research partners that is very valuable for implementing AI systems in the process industry.
Universities and research institutions like Charité, FU and TU Berlin play a central role in training specialists and provide research that can be transferred into industrial applications. Collaborations between research and industry are particularly pronounced in Berlin and offer direct access to the latest methods and talented graduates.
Finally, the city has an active angel and VC ecosystem that funds early experiments and scale-ups. For chemical and pharmaceutical companies this means innovation initiatives often find support for scaling and market entry — a favorable environment for bold AI initiatives.
Ready for a technical proof of concept?
Start with our AI PoC (€9,900): prototype, performance metrics and a clear production plan. We come to Berlin, work on site with your team and deliver tangible results.
Frequently Asked Questions
The development of a Safety Copilot starts with clear, measurable requirements: which decisions may the system suggest, and which remain human? In chemical and pharmaceutical contexts the boundary between assistance and decision-making is critical. Therefore we define clear use-case boundaries, test protocols and escalation paths together with QA, EHS and legal. This specification is the basis for design, testing and validation.
In engineering we rely on traceability: every recommendation of the copilot is logged, contextualized and versioned. Models are operated with conservative defaults and equipped with explainability tools so audits can understand why a recommendation was made. Additionally we integrate validation mechanisms that check inputs and model outputs against established safety rules.
Validation is staged: offline evaluation, shadow mode in production, controlled user-acceptance tests and final approval by responsible process owners. During these phases we document performance, error rates and side effects and adjust models and business rules accordingly.
Practically this means for companies in Berlin: early inclusion of regulatory stakeholders, conservative rollouts with humans in the loop and technical measures like audit logs, access controls and regular retraining cycles. Reruption accompanies this path from specification to audit-ready production, including on-site workshops in Berlin.
Self-hosted infrastructure gives companies full control over data, model access and compliance. For chemical, pharmaceutical and process industries, where highly sensitive research and production data often arise, this is a central advantage. On-premise or in a European data center hosted solutions reduce legal risks associated with data transfers to third countries.
Technically, self-hosting enables optimized latency, deterministic performance and integration into local networks and OT environments. Tools like MinIO, Traefik and containerized deployments provide scalability and repeatability of deployments. At the same time self-hosted models allow running proprietary or fine-tuned LLMs without external API calls.
The challenge lies in operation and maintenance: companies need DevOps and MLOps skills as well as clear operational processes for backup, monitoring and security patches. For many Berlin firms a hybrid approach makes sense: highly sensitive workloads on-premise, supporting services in a trusted cloud environment.
Reruption supports architecture, implementation and operation, including setups on European providers like Hetzner and building CI/CD pipelines, monitoring stacks and security hardening. We travel to Berlin regularly to set up the infrastructure production-ready together with your team.
The integration begins with a precise data map: which data fields are needed, how are they produced, and who owns them? We analyze existing interfaces, data formats and process steps and design an integration architecture that ensures secure, observable data flows. It's important to establish semantic consistency between ELN, LIMS and MES.
Technically we rely on standardized APIs, event-driven patterns and, where necessary, edge mediators that securely couple OT segments with IT systems. Transformation layers normalize data while observability components provide metrics and logs. For critical processes we implement feature flags and canary releases so new models can be rolled out gradually.
Security aspects are central: network segmentation, encryption in transit and at rest, and role- and permission management must be implemented consistently across systems. Access rights are granted minimally and audit logs document all model interactions with production data.
In Berlin we often work on site with IT and OT teams to jointly define and test interfaces. This collaborative approach reduces integration risks and ensures models are established not in isolation but as part of operations.
Good starter projects have clear, measurable KPIs and manageable integration effort. Examples include automated lab process documentation (ELN structuring), knowledge search for regulatory documents, and private chatbots for internal expert queries. These use cases deliver quick value because they save working time and reduce errors.
Another low-threshold area is predictive maintenance for critical equipment or sensor-based quality control. These applications use existing sensor data and can often be put into production with modest data engineering. Also useful are assistance copilots for standardized inspection procedures that support employees in real time.
Building on these successes one can tackle more complex projects such as ML-assisted compound screening or process optimization across multiple production stages. The incremental approach minimizes risk and creates internal reference projects that increase acceptance.
In Berlin it makes sense to look for early adopters in research units or quality control — these areas often yield the largest efficiency gains. Reruption supports identification, the PoC and the transition to production on site.
The time to production depends heavily on the use case, data readiness and integration complexity. A realistic timeline looks like this: PoC in 2–6 weeks, MVP in 3–6 months and production readiness in 6–12 months. These timeframes balance speed and necessary robustness.
Key milestones are: problem scoping and data collection, prototype development, performance evaluation, security and compliance review, integration tests with real systems and gradual rollout (shadow, pilot, production). Each step requires clear exit criteria and documented tests.
Typical delays are poor data quality, unclear responsibilities and unexpected integration requirements in OT systems. These risks can be minimized through early data assessments, stakeholder workshops and a modular architecture.
Reruption accompanies you along the entire timeline, ensures transparent milestones and travels regularly to Berlin to accelerate the transition to productive operation together with your team.
Berlin offers a broad talent base: data scientists, ML engineers, backend developers and DevOps specialists are widely available. It's crucial to build a cross-functional team combining domain experts from chemistry/pharma, data engineers and MLOps engineers. This mix ensures models are not only developed but also robustly operated.
For scaling, a hybrid mix of in-house hires, targeted contractors for specific tasks and partnerships with local providers or consultancies is advisable. This way you can increase capacity quickly without incurring long-term fixed costs.
Training is key: existing staff from R&D or production can be developed into super-users or domain analysts through focused training. At the same time clear career paths for data and MLOps roles should be defined to reduce churn.
Reruption supports team building, recruiting support, onboarding and the operational setup of MLOps pipelines. We work regularly on site in Berlin to transfer knowledge directly and make teams fit for productive operation.
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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