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AI Competence

Enterprise AI —
From Strategy
to Working Solution.

Cortexa's AI team guides your company using CRISP-DM methodology — from identifying the first use case to a scalable, GDPR-compliant production system, on any platform (AWS, Azure, on-prem).

CRISP-DMstructured AI methodology
Azure OpenAIsecure enterprise GenAI
Platform-agnosticAWS · Azure · On-prem

The Full Spectrum of AI Deployment

From strategic consulting to production-ready systems — with measurable results at every step.

AI Implementation Consulting
Business process analysis, AI maturity assessment, and prioritized use-case roadmap using CRISP-DM methodology. We show where AI delivers real value — and where it's not worth forcing.
CRISP-DMAuditRoadmap
Enterprise GenAI Platform
Microsoft Azure OpenAI-based enterprise chatbot, document processing, and internal knowledge base automation with RAG architecture — on your data, on your infrastructure.
Azure OpenAIRAGLLM
Predictive Analytics
Machine learning-based forecasting for manufacturing, logistics, and energy processes — demand forecasting, machine failure prediction, quality control automation.
MLForecastingPython
Process Automation with AI
Intelligent document processing (IDP), decision support systems, and anomaly detection in industrial data — reduced manual workload, faster decision-making.
IDPOCRAnomaly Detection
Pilot Projects
Fast, low-risk pilot program in 8–12 weeks — with measurable KPIs, validated results, and a scaling plan. See results before making a commitment.
PoC8–12 weeksKPI
GDPR & AI Act Compliance
Risk classification of AI systems under the EU AI Act, data processing review, compliance documentation, and audit trail setup.
GDPRAI ActAudit

CRISP-DM AI Project Process

A proven, industry-standard methodology — every project step is measurable and documented.

1
AI Maturity Assessment
Business process, dataset, and infrastructure assessment. Use-case identification and ROI estimation for every opportunity.
2
Use-case Prioritization
Evaluating identified opportunities by business impact, feasibility, and data maturity — a prioritized roadmap.
3
Pilot Development & Validation
8–12 week agile development cycle: model building, testing, fine-tuning, and business validation with measured results.
4
Production Deployment & Monitoring
Production deployment on Azure, CI/CD pipeline, model monitoring, drift detection, and continuous optimization.

Microsoft Azure, AWS & Open Source Tools

Azure OpenAI Service Python / scikit-learn Power BI Azure ML Studio LangChain / RAG CRISP-DM GDPR compliant On-premise / Cloud AI Act audit

Find Out Where Your Company Stands on AI

Free AI maturity assessment — we'll show which processes are ready for AI, and what the realistic ROI looks like.