Engagement tiers and scope
Turn AI ideas into operational capabilities
Work with AILabWerk to move from exploration to stable, measurable AI deployments. We provide pragmatic implementation plans that respect compliance and operational constraints.
Statistics reflect engagements delivered by AILabWerk through 07-02-2026; outcomes vary by sector and project scope.
Select an engagement model
Packages structured for clarity on deliverables, timelines and responsibility boundaries
Discovery Session
A focused assessment to identify key AI opportunities, constraints and a recommended next-step roadmap. Includes data scan and stakeholder interviews.
- Data asset inventory
- Use case prioritization
- Estimated effort and risks
- High-level roadmap
- Deliverable: assessment report
Pilot Project
Rapid prototype of a prioritized use case, including model proof-of-concept, validation on representative data and integration blueprint for production.
- Prototype model
- Validation metrics and tests
- Integration blueprint
- Security and privacy review
- Handover documentation
Production Deployment
Full engineering and operationalization of AI systems: scalable deployment, monitoring, retraining pipelines, role-based access and compliance artifacts for audits.
- Containerized deployment
- Monitoring and drift detection
- Retraining and CI/CD for ML
- Operational SLAs and runbook
- Ongoing support and reviews
Applied AI Integration — from strategy to production
AILabWerk delivers pragmatic AI implementation services tailored to the operational realities of businesses in Switzerland. Our approach begins with a structured discovery to map data sources, business objectives and constraints, followed by an evidence-driven roadmap prioritising measurable improvements. We design and validate models using reproducible engineering practices, integrate solutions into existing IT landscapes with attention to security and compliance, and establish MLOps processes for monitoring, versioning and controlled rollout. Throughout engagement we emphasise explainability, risk mitigation and transfer of skills to client teams so outcomes remain sustainable and auditable. Our recommendations are based on technical feasibility, regulatory context and expected PERFORMANCE metrics, and we collaborate with client stakeholders to define realistic success criteria and implementation milestones.
Enterprise-grade methodology
Structured discovery, pilot execution and production readiness with clear handover and documentation.
Compliance and data stewardship
Data protection practices aligned with Swiss and EU data protection expectations; emphasis on minimising exposure and retaining control of sensitive data.