An AI operations workspace that turns scattered requests into prioritized, explainable workflows, clear ownership, and measurable follow-through for small teams.
InspirationSmall teams receive requests through fragmented channels and lose context. They spend time rewriting requirements, deciding what matters first, and coordinating follow-through instead of delivering outcomes.## What it doesOpsPilot is an AI operations workspace that turns an inbound request into grounded next steps, a proposed owner, suggested timing, and a human-reviewed action. The workflow captures constraints, grounds the request in available project and portfolio context, generates a concise plan with rationale, and keeps the final send or scheduling decision with a human operator.## How it was builtThe prototype uses a Python application and an agent workflow with structured portfolio data, requirements, and environment-based secrets. Its design separates intake, grounding, proposal generation, and approval so each stage can be inspected and improved. The public repository documents the workflow and includes the application materials.## Responsible operationOpsPilot is assistive, not autonomous. It does not send external messages or schedule meetings without human review. The prototype does not claim an unverified third-party integration. Future production work would add authenticated workspaces, durable storage, observability, integration adapters, and an evaluated model gateway.## What we learnedThe most valuable part of an AI operations tool is not generating text; it is preserving context, making ownership explicit, and keeping people accountable for the final decision. We designed the workflow around explainability and approval rather than blind automation.
Log in or sign up for Devpost to join the conversation.