Process Mining

AI-powered analysis of event logs to optimize and automate business processes continuously.

Business impact

• Operational Efficiency — Improves by identifying and removing bottlenecks and redundant steps
• Process Cycle Time — Reduces by streamlining workflows and automating repetitive tasks
• Cost Reduction — Lowers operational costs through optimized resource utilization and waste elimination
• Order Processing Speed — Increases by accelerating approval and fulfillment processes
• Compliance — Enhances by detecting deviations and enforcing process conformance

Data requirements

• Enterprise Resource Planning (ERP) logs (Structured) — Provide transactional event data to reconstruct process flows
• Customer Relationship Management (CRM) logs (Structured) — Supply customer interaction events for end-to-end process visibility
• Workflow and task management systems (Structured) — Capture task execution details to identify bottlenecks and delays
• Unstructured documents and emails (Text) — Extract workflow information from communications to enrich process context

AI methods and techniques

• Predictive AI — Forecasts process outcomes and potential delays based on historical patterns
• Generative AI — Generates natural language insights and recommendations from process data
• Agentic AI — Autonomously executes process analysis and simulation tasks for continuous optimization

AI models and model families

GPT-4o, Claude, Llama, Gemini 2.5-Pro, Sonnet-4

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Accenture, Cosentino, Merck KGaA, University Hospitals Coventry Warwickshire UHCW, Westrock.