I've spent years watching the same pattern repeat across project teams: the more people involved, the faster the coordination layer grows. Not because anyone wants more overhead — but because someone has to track what's changing, follow up on blockers, update tickets, and connect people who work in silos.
I built Prodigia because I think AI can now handle that layer.
Each project gets its own set of AI agents. They watch events as they happen — a status change, an assignment, a meeting ending — and proactively surface suggestions for what should happen next. No trigger needed. Suggestions appear in a feed where you approve, reject, or redirect them.
Beyond that: natural language project intake (describe a project, agents build the structure), live meeting transcription with automatic action item extraction, and per-project AI memory — preferences you set, a logbook the agents maintain over time. Agents can also handle virtually any action on the platform through conversation — update issues, reprioritize, create milestones, assign work.
It's in early access. The hardest part to build correctly was the multi-agent architecture and the tools associated to each agent — happy to go deep on that with anyone interested.
I've spent years watching the same pattern repeat across project teams: the more people involved, the faster the coordination layer grows. Not because anyone wants more overhead — but because someone has to track what's changing, follow up on blockers, update tickets, and connect people who work in silos.
I built Prodigia because I think AI can now handle that layer.
Each project gets its own set of AI agents. They watch events as they happen — a status change, an assignment, a meeting ending — and proactively surface suggestions for what should happen next. No trigger needed. Suggestions appear in a feed where you approve, reject, or redirect them.
Beyond that: natural language project intake (describe a project, agents build the structure), live meeting transcription with automatic action item extraction, and per-project AI memory — preferences you set, a logbook the agents maintain over time. Agents can also handle virtually any action on the platform through conversation — update issues, reprioritize, create milestones, assign work.
It's in early access. The hardest part to build correctly was the multi-agent architecture and the tools associated to each agent — happy to go deep on that with anyone interested.