Five Sustainable Development Goals, eight years of work, and the commitments we are taking on next

Enterprise AI has reached an uncomfortable stage: GenAI demos look impressive, LLM copilots answer quickly, and AI agents can now plan, act, and connect with enterprise tools.

How Cody helped build Obrii — a national AI-powered platform designed to bring 4.5 million Ukrainians back into economic activity by 2030.

Process Mining + Generative AI changes that equation. Process mining reveals the operational truth: real process flows, variants, bottlenecks, delays, cost leaks, and rule violations. Generative AI turns that evidence into recommendations, explanations, process redesign options, and automation logic. Together, they create process intelligence — a fact-based understanding of how work moves through the business and where automation will deliver measurable value.

AI-driven automation is crossing a threshold: workflows are no longer just executed —they’re planned, decided, and acted on by software agents. Agentic AI enables systems to reason, plan step-by-step, and take coordinated action across applications to achieve goals. That autonomy changes the governance risk curve. Speed compresses response time. Scale multiplies the blast radius of a single misconfiguration. And cross-system actions (APIs, tickets, payments, customer data, infrastructure) create failure chains that are hard to spot until they’re already customer-visible.

Multi-agent AI systems are shifting from “assist” to “act.” Instead of one model answering a prompt, multiple specialized agents can plan, share context, call tools/APIs, and execute multi-step work across business systems. That autonomy also creates new risks: duplicated effort, conflicting actions, brittle handoffs, and decision trails that are hard to audit.