AI Consulting & ML Services

We guide you through AI implementation step-by-step, handling technical complexity while training your teams. Your data scientists learn MLOps practices, your executives track progress monthly, and your IT teams get deployment guides for independence.

We assess data quality first and fix foundational issues before training models. Your data engineers see quality scores, business teams understand what gaps need filling, and executives get realistic timelines. This prevents building models on flawed data that produce unreliable predictions.

We implement production-ready AI with explainability built in. Every model includes clear documentation showing how predictions work, which data drives decisions, and when human review is needed. Your business users see simple explanations while data scientists access full technical details.

We define success metrics with your executives upfront, then track them weekly through dashboards your CFO can access. You see concrete results: churn model saved €240K by retaining 15% more customers, not vague accuracy claims

We implement governance from day one with model versioning, audit logs, access controls, and data lineage tracking. Your security team approves architecture before deployment, compliance officers get automated audit trails for every decision, and policies are enforced at infrastructure level.

We build predictive maintenance models that forecast equipment failures days or weeks early, reducing unplanned downtime 30-50% and maintenance costs 20-40%. Your operations managers see risk scores on real-time dashboards, technicians get alerts before failures occur, and your CFO tracks prevented downtime costs monthly.

We define success metrics with your executives before any development begins, then track them through weekly dashboards your CFO can access directly.

You see concrete results, e.g. churn prediction model saved €240K in Q1 by retaining 15% more customers, rather than vague "improved accuracy" claims.

Monthly reports compare AI predictions to actual outcomes in business terms, showing exactly which models deliver value and which need adjustment.

This transparent measurement approach prevents the common problem where AI pilots look impressive but fail to demonstrate real business impact.

We implement governance frameworks from day one with model versioning, audit trails, access controls, and data lineage tracking built into the infrastructure, not added as an afterthought.

Your security team reviews and approves architecture before any deployment, your compliance officers receive automated audit logs for every AI decision, and your legal team can demonstrate regulatory compliance through documented lineage that satisfies GDPR, EU AI Act, and industry-specific requirements.

This governance-first approach prevents compliance issues before they start rather than scrambling to fix them after deployment.

Every model we deploy includes clear documentation showing how predictions are made, which data drives decisions, and confidence levels for each output.

Your business users see simple explanations ("this customer is high-risk because payment history shows 3 late payments in 6 months"), while your data scientists can access full technical details including feature importance, SHAP values, and model architecture.

We also document when AI recommendations should be trusted versus when human judgment is needed, ensuring your teams understand both the capabilities and limitations of each model.

We build your team's independence through knowledge transfer at every phase, not consultant dependency.

Your data scientists receive hands-on MLOps training during development, your IT teams get deployment guides they can follow independently, and your business users learn to interpret AI outputs through role-specific workshops.

We provide 3-6 months of hands-on support as your teams gain confidence, then offer tiered support options from basic monitoring (monthly health checks) to full managed services.

Enterprise AI projects typically run 4-8 months from discovery to production, and we strongly recommend starting with a 6-12 week pilot that validates one critical use case before full commitment.

The pilot proves your approach works with real data, demonstrates measurable business value to executives, and identifies any data quality or integration issues early, preventing the "big bang" failures where organizations invest heavily only to discover fundamental problems during deployment.

Weekly progress dashboards show exactly what's built, tested, and production-ready at each phase, so you're never wondering where the project stands.