Introducing Kinelo and the New Hybrid Team

Every AI tool promises to make your team faster. None of them know what your team is trying to accomplish.

Think about the last time you asked an AI coding assistant for help. You gave it a ticket description, maybe some context about your codebase. But it didn't know that the API you're building depends on a schema change another engineer mentioned in standup yesterday. It didn't know the PM clarified scope in a Slack thread this morning. It didn't know the deadline moved because of a customer escalation.

You knew all of that—or you were supposed to. You spent twenty minutes reconstructing context so the AI could help you for five.

This is the state of AI in teams today: powerful tools, completely disconnected from how work actually gets coordinated.

The fragmentation problem isn't new. Managers have always struggled to synthesize information across systems—Linear, Slack, GitHub, meetings, documents, the dozen places where commitments get made and broken. What's changed is that this fragmentation now affects AI workers too.

AI agents are emerging that can write code, conduct research, analyze data, draft documents. They're increasingly capable. But they can't participate in how teams coordinate. They weren't in the meeting. They don't know the priorities. Every interaction requires manually reconstructing context, so they remain disconnected tools rather than participants in how work gets done.

The result: human managers are overwhelmed trying to synthesize everything. AI workers are isolated from coordination. And the hybrid teams everyone talks about—humans and AI working together—remain impossible to actually run.

The problem isn't that AI tools aren't smart enough. It's that they don't have a seat at the table.

We're building Kinelo to change this.

Kinelo is a persistent intelligence layer that lives within your company, continuously building understanding of goals, commitments, and progress across all the places work happens. Not another dashboard. Not another tool to check. A shared context that both humans and AI can draw from.

For managers, this means visibility into what's actually happening—threats to commitments surfaced before they cause delays, without spending hours reading every Slack thread and Linear comment.

For individuals and their AI tools, this means working with full context about what matters, not just what's in the ticket.

For teams, this means AI agents that can eventually participate in coordination: understanding priorities, reporting status, requesting help when blocked—like remote team members, not disconnected scripts.

We're starting with engineering teams because that's where we have the deepest experience and where the pain is most acute. Threat detection—catching scope creep, priority misalignment, stalled work before they derail your sprint—is our entry point.

But threat detection is a feature of something larger. We're building toward a future where companies can organize differently: where the intelligence layer handles the synthesis that humans can't scale, where AI workers have the context to actually participate, where hybrid teams become not just possible but natural.

We don't know exactly what that looks like yet. Neither does anyone else. But we believe the path runs through coordination—through giving AI a seat at the table rather than leaving it outside the room.