Transform R&D and manufacturing

Organizations identify valuable AI use cases, but execution often stalls because legacy systems cannot sufficiently expose data to AI models. Additional hindering factors include manual processes that create bottlenecks, compliance frameworks that do not account for AI-driven decisions, and teams lacking proven patterns for deploying AI in production.

We enable AI transformation by modernizing existing systems, eliminating costly replacement projects while accelerating time-to-value.

Technical Transformation: Infrastructure Modernization
Our approach addresses foundational constraints that prevent AI adoption:

• AI-Powered Dependency Mapping – Exposes hidden interconnections within legacy codebases, reducing architectural risk
• Automated Refactoring – Systematically reduces technical debt while preserving critical domain knowledge and institutional intelligence
• Incremental API Extraction – Enables selective AI integration without disrupting mission-critical systems
• Data Pipeline Automation – Converts fragmented, siloed data into governed, AI-ready feeds that ensure compliance and quality

Organizational Transformation: Governance and Enablement
Sustainable AI adoption requires operational frameworks and cultural readiness:

• AI Governance Structures – Establish approval workflows, audit trails, and accountability mechanisms aligned with regulatory requirements
• Secure Integration Layers – Connect legacy platforms to AI services while maintaining security boundaries and data sovereignty
• Operational Playbooks – Standardize deployment protocols, monitoring frameworks, and incident response procedures
• Capability Development – Upskill engineering and operations teams to collaborate effectively with AI systems

Your environment becomes AI-ready. You can deploy, scale, and maintain AI solutions with confidence.

Assess your foundation across five dimensions.

• Data Accessibility: Can AI models access needed data without manual extraction? Red flags include data locked in legacy systems or paper-based processes,
• Integration Capability: Can AI services connect to existing platforms through modern APIs? Watch for monolithic systems with no service interfaces.
• Governance Framework: Do you have approval workflows and audit trails for AI decisions? Concerns arise without defined accountability.
• Operational Maturity: Can teams deploy, monitor, and troubleshoot AI systems in production? Warning signs include no AI experience beyond pilots.
• Compliance Readiness: Can you demonstrate to auditors how AI systems maintain regulatory compliance? Red flags include no validation strategy for AI outputs.

Score yourself: Four to five yes answers mean you are ready for AI-native solutions. Two to three yes answers suggest parallel readiness work plus targeted pilots. Zero to one yes answers indicate starting with AI-readiness transformation makes sense.

When established workflows need AI capabilities, we deliver Brownfield AI-native solutions. Smart process automation replaces manual handoffs with AI agents that coordinate tasks across departments. Augmented decision systems layer AI intelligence onto existing platforms. Intelligent document processing transforms unstructured workflows into AI-analyzable data streams. Predictive operations add forecasting and optimization to current systems.

Examples span industries. In pharma R&D, AI-accelerated literature review feeds existing LIMS. For medtech quality, automated deviation detection works within established QMS platforms. In semiconductor manufacturing, AI-driven recipe optimization integrates with process control systems.

These solutions deliver immediate value while respecting existing investments.

When launching new systems or capabilities, we build Greenfield AI-native solutions. Agentic architectures allow AI to orchestrate workflows across R&D, production, and quality from day one. Data mesh foundations ensure AI models access clean, governed data without manual intervention. Built-in observability logs every AI decision for compliance. Security-first design meets FDA 21 CFR Part 11, MDR, ISO 13485, and IEC 62304 requirements from initial architecture.

Examples include digital lab environments with AI-native experiment design for pharma, AI-powered design validation platforms for medtech product lines, self-optimizing production lines for semiconductor fabs, and predictive lifecycle management for industrial equipment fleets.

These solutions are designed for AI from the first line of code. 

The initial assessment takes 90 minutes. A comprehensive readiness evaluation typically requires 2-3 weeks, including stakeholder interviews, technical architecture review, and governance assessment.

Timelines vary based on current maturity. Organizations with high readiness scores can deploy targeted solutions in 8-12 weeks. Organizations requiring readiness transformation typically see production deployments in 4-6 months.

We design for regulatory requirements from initial architecture. All solutions include audit trails, human oversight protocols, and validation frameworks. ZEISS Digital Innovation have experience with FDA 21 CFR Part 11, MDR, ISO 13485, and IEC 62304.

Yes. Our Brownfield transformation approach modernizes existing systems without replacement. We create integration layers that connect legacy platforms with AI capabilities while maintaining security and compliance.

We serve regulated industries including pharmaceuticals, medical devices, laboratory technology, semiconductor manufacturing, and infrastructure. Our experience spans R&D, manufacturing, and quality operations.

Pricing depends on scope and delivery model. We offer fixed-price assessments, outcome-based project pricing, and capacity-based team augmentation. We discuss pricing models during initial strategic discussions.

Our readiness-first approach minimizes this risk. We establish clear success criteria before deployment. All solutions include monitoring and optimization frameworks. ZEISS Digital Innovation work with you to adjust approach based on results.

We use on-premise or private cloud deployments. No client data goes to public models without explicit consent. All team members sign NDAs. We honor data residency requirements. Security and IP protection are built into every engagement.

Yes. We offer support and optimization services. We also provide team training to build internal capability. The goal is to transfer knowledge while ensuring solutions continue to deliver value.

Schedule a strategic discussion. We will explore your specific challenges, assess preliminary readiness, and outline a potential approach. There is no obligation.