Performatica’s AUTONOMOUS AI Expertise

AWP™ enables enterprises to build, deploy and manage Digital Employees that work alongside people across business and industrial operations.

From executing processes to monitoring operations and simulating scenarios, AWP™ brings AI from assistance to autonomous work.

AWP™ provides the foundation for creating, deploying, operating and continuously improving Digital Employees.

It brings together: Knowledge | Skills | Workflows | Tools | Data | AI Models |Memory | Governance | Enterprise Systems

One platform. Three Digital Employees.

• AI Worker: Do the Work 
• AI Advisor: Watch the Work 
• AI Tutor: Learn. Simulate. Optimise.

Digital Employee Studio: Build, configure, test and deploy AI Workers, Advisors and Tutors for specific business processes and domains.

Enterprise Knowledge & Context: Connect SOPs, manuals, data, process knowledge and enterprise context to ground AI decisions.

Workflow & Tool Integration: Connect Digital Employees to enterprise applications, APIs, MCP tools and operational systems.

Multi-Agent Orchestration: Coordinate multiple AI agents and Digital Employees to execute complex workflows.

Human-in-the-Loop: Keep people in control with approvals, escalation and intervention where judgement matters.

Simulation & What-If Analysis: Model scenarios, test assumptions, compare alternatives and understand potential outcomes.

Governance & Observability: Control access, monitor activity, maintain audit trails and govern Digital Employee behaviour.

Carbon Management: Digital Employees automate data collection, analysis, reporting and carbon-management workflows.

Methane Monitoring: AI Advisors monitor emissions events, identify anomalies and trigger investigation and response workflows.

Flare Reduction: AI Workers execute operational workflows while AI Tutors simulate flare-reduction scenarios and evaluate alternatives.

ESG & Regulatory Reporting: Automate evidence gathering, validation, reporting and compliance workflows across sustainability frameworks.

Predictive Maintenance: Identify emerging equipment risks, investigate anomalies and simulate maintenance scenarios before intervention.

Operational Optimisation: Combine real-time operational data with simulations and AI-driven recommendations to improve efficiency, emissions performance and resource utilisation.