From proof-of-concept to production deployment with full training, support, and knowledge transfer – we deliver working AI solutions.
Start Implementation →Wir begleiten Sie durch den gesamten AI-Implementierungsprozess: Von der Proof-of-Concept-Phase über Pilot-Deployments bis zum Production-Rollout, inklusive Team-Training, Dokumentation und kontinuierlichem Support. Unsere Implementierungen sind produktionsreif, skalierbar und wartbar.
Proven methodology that takes AI projects from concept to production with minimal risk and maximum impact.
4-6 week sprint to validate technical feasibility and business value. Build prototype with subset of data, demonstrate core capabilities, and quantify expected ROI. Go/No-Go decision point before full investment.
Start POC →2-3 month implementation at limited scale (single site, department, or asset). Production-grade architecture with monitoring and security. Gather user feedback, measure KPIs, and refine before scaling.
Launch Pilot →Full-scale deployment across all sites/assets with CI/CD pipelines, automated testing, and blue-green deployments. Includes load testing, disaster recovery, and compliance validation (ISO 27001, GDPR, etc.).
Scale to Production →Comprehensive training programs for data scientists, ML engineers, and operations teams. Hands-on workshops, documentation, runbooks, and code walkthroughs. Ensure your team can maintain and extend the AI system.
Train Your Team →Set up model versioning (MLflow/DVC), experiment tracking, feature stores, and automated retraining pipelines. Real-time monitoring of model performance, data drift, and concept drift with alerting.
Setup MLOps →3-6 month hypercare period with dedicated support team. Performance tuning, bug fixes, user training, and continuous optimization. SLA-backed response times and escalation procedures.
Get Support →Implemented LSTM-based day-ahead price forecasting for a energy trading desk. POC (6 weeks) showed 18% better accuracy than existing vendor solution. Pilot (3 months) validated performance in live trading. Production rollout (2 months) deployed to 24/7 operations. Model retrained nightly with automated data pipelines. Result: €2.4M/year in improved trading decisions.
Deployed XGBoost models to predict BHKW failures 2-4 weeks in advance. Implemented MLOps pipeline with automated model retraining, A/B testing, and shadow mode deployment. Integrated with SAP PM for work order generation. Trained 4 maintenance engineers on model interpretation. Reduced unplanned downtime by 82%, increased availability from 96.4% to 99.2%.
Built reinforcement learning agent for HVAC control optimization. POC in 1 building (€50k savings/year). Pilot across 10 buildings validated 15-22% energy savings with zero comfort complaints. Automated deployment pipeline rolled out to 400+ buildings in 6 months. Cloud-native architecture handles 2M sensor readings/minute. Trained in-house data team now maintains and extends the system.
Let us handle your end-to-end AI implementation – from POC to production with full training and support.
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