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Enabling Accelerated Drug Development

with closed-loop R&D workflows for AI-enabled drug discovery

We transform pharma R&D with digital services that connect data, AI, and lab execution into measurable outcomes.

Enabling accelerated drug development

Imagine a world where AI models don’t just suggest hypotheses, but directly trigger and optimize real-world experiments in fully connected, autonomous labs.

So that hypotheses from your predictive models become protocols, protocols become experiments, and data from your experiments continuously improve your models in a closed loop.

A world where data, algorithms, and lab systems continuously learn from each other, accelerating discovery and development at an unprecedented pace.

Closed-loop R&D

The drive to accelerate drug discovery and development processes through the use of AI and thereby reduce time-to-market and development costs is growing ever stronger in the pharmaceutical industry.

Companies already possess vast amounts of data and are building increasingly powerful predictive models. Yet, most organizations struggle to translate this potential into real impact.

The core challenge are not the predictive models or lab equipment to produce data—it’s the missing operational backbone: a flexible yet powerful IT and automation infrastructure. AI outputs rarely make it into real lab execution, and experimental data is often not enriched and fed back systematically to improve models.

This is where ZEISS Digital Innovation comes in.

Why us

We design and implement closed-loop R&D architectures tailored to your existing system and lab landscape—connecting data, AI models, and laboratory systems into one continuous, automated cycle. This includes automating lab workflows, harmonizing and centralizing internal and external data, building AI pipelines with predictive models, translating model outputs into autonomously executable laboratory experiments, and feeding the resulting data back into ongoing model refinement.

The result is a robust foundation for AI enabled, closed loop drug development that:

accelerates time-to-market
reduces overall R&D costs
increases success rates in discovery and development
while adhering to GxP, data-integrity and audit readiness requirements

We turn AI from isolated potential

into operational reality in the lab

Circular diagram with arrows connecting three labeled sections: AI Models with a brain network graphic, Fully Automated Labs with lab equipment and robotic arm, and Data Platforms with server and database icons

AI-to-Lab translation services

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Experiment orchestration services

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Lab connectivity services

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Data connectivity & Fusion services

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Scientific data engineering

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Data architecture services

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MLOps and Dev services

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Most models don't fail in development - they fail in production. We support in operationalizing AI models in regulated R&D environments, ensuring they are reliable, traceable, and continuously improving based on real experimental feedback

How we support you:

Operationalization of models in production environments
Versioning and monitoring of models (drift, performance, data quality)
Governance workflows for model validation and approval in regulated environments
Integration of feedback loops from experimental results
Continuous improvement of model performance
One of the biggest bottlenecks in AI-driven R&D is the translation from model predictions to concrete experimental steps. We help you converting model outputs into autonmously executable lab protocols.

How we support you:

Automated translation of model outputs (targets, compounds, conditions) into structured experimental protocols and parameter sets
Standardization of experiment design formats
Interface layer between Machine Learning platforms and lab orchestration, scheduling and execution systems
Monitoring, logging and Machine Learning Operations integration for traceability and retraining
Many labs have invested in automation—but in isolated silos. We connect these islands into an orchestrated, closed loop system that plans, schedules and executes experiments end to end.

How we support you:

Digital lab twin representing equipment, capacities, constraints and workflows
Cross-system workflow engine to coordinate multil-step, cross-system experiments
Scheduling and resource allocation logic
Central control layer for experiment execution
Monitoring and exception handling
Integration of automated supply chain logistics (consumables, reagents, samples etc.)
In most labs, equipment and systems don't speak the same language and remain disconnected, vendor-specific, and hard to scale. We support in IT/OT integration, connecting your existing instruments, robots, and IT systems into a unified, interoperable environment—without replacing your current tools.

How we support you:

Integration of lab instruments and robotic systems via standardized communication protocols
Integration of ELN, LIMS, and execution systems
Real-time event streaming from lab processes
Integration of GxP software layers
Internal and external data is often fragmented across systems, formats and sources. We help you harmonizing internal and external data into a consistent, interoperable layer, enabling richer model inputs and better decisions.

How we support you:

Integration of lab data management with central data platform
Ontologies and semantic standardization
External/public data integration for model enrichment
Scientific data often lacks context to be fully useful for AI. We support you in transforming raw experimental data into structured, semantically consistent data sets that AI models can actually learn from.

How we support you:

Data harmonization and transformation
Metadata models and ontolgies for scientific contexts
Data lineage and traceability
AI readiness (feature stores, structured datasets)
In most organizations, data is siloed, inconsistent, and not usable for AI at scale. We build the technical foundation that makes scientific data accessible, structured, and continuously usable across systems, teams, and use cases.

How we support you:

Design and implementation of edge and cloud platforms for R&D data (incl. hybrid cloud strategies)
Data ingestion and integration pipelines

Real-time and batch data processing
Feedback data ingestion from experiments
Cloud-cost optimization
Cybersecurity best-practices for cloud-based data platforms

Let’s work together

to create digitalized and automated processes today to improve people's health tomorrow!

Fill out the form below to schedule a consultation with our experts and learn how we can help you realize your vision for digitalized and automated workflows and processes.