Aguru

Job of Work (JoW) Mapper

The Job of Work Mapper identifies the manual tasks worth turning into a paid AI-powered service. It maps the steps, stakeholders, systems, success criteria, and where AI can be effectively utilized, taking the guesswork out of automating what matters to your customers.

Execution Engine

Aguru's Execution Engine manages jobs of work as a graph of tasks, overseeing the work, data, context, and human review. This results in auditable and repeatable outcomes, ensuring that valuable jobs of work can be completed reliably over extended periods.

Stakeholder Coordination

This feature integrates AI understanding into a deterministic framework for managing conversations and interactions. It works with preferred communication channels, handles non-responses and exceptions, and escalates issues for human review when necessary, ensuring smooth workflow progression.

Your own intelligence flywheel

Aguru captures traces, stakeholder context, AI outputs, corrections, exceptions, outcomes, and cost performance to turn each workflow into execution intelligence. This helps users benchmark models, improve accuracy, and optimize costs, providing valuable insights for future workflows.

Design for failure

Aguru's infrastructure is built to handle failures, including server crashes and API issues. It includes a durable execution engine with built-in checkpoints, retries, and automated recovery, ensuring that workflows continue to progress even in the face of unexpected challenges.

Make AI Small Again

This principle involves using small, focused AI models within a deterministic framework instead of relying on large general-purpose models. This approach boosts accuracy, reduces costs, and allows for quick detection and correction of errors, enhancing the reliability of AI-driven workflows.