Process Automation

Transactional data and natural language instructions automate multi-step workflows, improving accuracy and efficiency

Business impact

• Operational efficiency — Automating tasks accelerates workflows and reduces manual effort and delays
• Process accuracy — Automation minimizes human errors, ensuring consistent and reliable task execution
• Cost reduction — Lower labor and error correction costs through automated, repeatable processes

Data requirements

• Transactional records (Structured) — Used to trigger and validate automated process steps and decisions
• User interface elements (Image) — Detected to enable automation of multistep app operations and interactions
• Natural language instructions (Text) — Processed to interpret user commands and guide automation workflows

AI methods and techniques

• Predictive AI — Forecasts process bottlenecks and optimizes workflow sequencing
• Generative AI — Creates or refines automation scripts and responses from natural language prompts
• Agentic AI — Autonomously executes complex workflows combining rules and AI reasoning

AI models and model families

GPT-4o, Claude, Llama, DeepSee.ai, LLMPA

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: ABB, Alipay, Cognizant, Danone Group, JPMorgan Chase & Co., Matricia Solutions.