Initial Situation
Life science companies (e.g., pharmaceuticals, biotechnology, diagnostics, chemicals) generate large amounts of experimental data, test protocols, analytical measurements, and formulation parameters. Many laboratory steps are still manual, fragmented, or documented in a decentralized manner. This leads to:
- Delays due to data imports and manual transcription
- Inconsistencies, miscommunication, and data incoherence
- Repeat experiments due to a lack of transparency in the experimental pathways
- Long development cycles (e.g., for formulations, active ingredient optimizations)
Difficulties with traceability, compliance (e.g., GLP, GMP), and audit evidence
In addition, the pressure to innovate is growing: market cycles are shortening, and competitors are demanding faster product development. At the same time, regulatory requirements for documentation, data integrity, and traceability are increasing.
Objective
- Automation and standardization of central laboratory processes and data flows
- AI-supported recipe development and test planning
- Increased efficiency, reduction of redundancies and repetitions
- Ensuring high data integrity and compliance (auditability, traceability)
- Relieving laboratory scientists of administrative tasks, leaving more time for creativity and innovation
Description of the use case
1.Central data integration & workflow automation
LabOptimizer is introduced as a central LIMS/data platform. All devices, measurement systems, and data sources (spectroscopes, chromatographs, automation robots, etc.) are connected.
Incoming measurement data is automatically recorded (e.g., via interfaces/APIs/instrumental connections).
Standard operating procedures (SOPs), protocols, test definitions, and metadata are managed and versioned in the system.
Workflows are controlled digitally: e.g., from sample acceptance to pretreatment, analysis, data release, and report generation.
2. AI-supported recipe and material optimization
AI-based predictions of material properties: LabOptimizer uses AI to derive prediction models for material or product properties (e.g., stability, viscosity, solubility) from historical experimental data, material characteristics, and test parameters.
For new formulation variants or material combinations, the system suggests optimized parameters (e.g., ratios, concentrations, process conditions).
The “Product Navigator” in LabOptimizer enables a reverse search—i.e., identifying existing similar formulations or materials to avoid duplicate developments.
New experiments are prioritized by the system based on uncertainties, influencing parameters, or cost-benefit predictions (design of experiments optimization).
3. Quality control, validation, and compliance
Every measurement and every step is stored with complete audit trail information: who, when, with which device/parameter, who gave approval, etc.
Versioning of SOPs and test programs is managed transparently.
Automatic plausibility checks and data validations are built in (e.g., limit values, calibration status, outlier detection).
In the event of deviations, workflows for root cause analysis are automatically initiated.
Reporting modules generate quality-related documents that can be used for audits or regulatory requirements.
Benefit
- Cost reduction: Less manual processing means lower personnel costs.
- Productivity increase: Faster turnaround times for customer and financial processes.
- Customer satisfaction: 24/7 service through voicebots and self-service offerings.
- Sustainability: Less paper, more efficient processes, and optimized grid integration of renewable energies.
- Future-proofing: Compliance with the EU AI Act creates trust and legal certainty.
Success Factors
- Data quality: AI requires clean, structured data.
- Strategic integration: AI is not a tool silo, but a digital workforce that holistically supports business processes.
- Governance: Clear rules for transparency, monitoring, and risk management.
- Gradual rollout: Start with pilot projects, then scale up to other processes.
Advantages of LabOptimizer
In short:
With LabOptimizer, life science companies can digitize their laboratories, automate processes, and accelerate recipe and material development through AI-supported modeling. The results are greater efficiency, better quality, regulatory compliance, and an accelerated innovation cycle.
Future-proofing your company
