We Built Lisa Because Our PM Was Drowning in Tabs

Here's what a Tuesday morning looks like for most PMs at early-stage startups: Linear open in one tab, GitHub in another, Slack pinging in a third. A standup in 20 minutes. Somewhere in yesterday's Slack thread, someone made a decision about the auth flow — but nobody wrote it down. The PM opens a fourth tab to search for it.

Fourteen tabs. That's where it starts. It never gets better from there.

The math nobody talks about

Knowledge workers toggle between applications roughly 1,200 times per day. Dr. Gloria Mark's research at UC Irvine found it takes 23 minutes and 15 seconds to fully regain focus after a single interruption. And according to Reclaim.ai's 2026 data, teams lose approximately five working weeks per year just to the reorientation cost of switching tools.

For PMs, it's worse. A typical product manager sits through six meetings a day — which means five blocks of dead time between them, each too short for deep work but just long enough to start something you won't finish. Managers average 16 hours per week in meetings. That's before prep, follow-up, or the act of copying a status update from Linear into Slack for the third time this week.

We did the math on our own workflow. About 60% of a PM's time goes to what we'd call information logistics — not thinking, not deciding, not prioritizing. Just moving context from one tool to another.

The real problem isn't the tools

Linear is excellent. So is GitHub. Slack does what it does. The problem isn't any single tool — it's the gaps between them.

As Continue.dev's team put it when they built their own Slack-to-GitHub agent: they were "tired of the context switch tax." Kilo Code's founders made a similar observation — that Slack threads often contain exactly the context needed to fix a bug or build a feature, but that context evaporates the moment someone switches to their editor.

Teams have tried to solve this with integrations. Slack-GitHub bots, Linear webhooks, Zapier automations. They help at the edges. But as one engineering team found, tracking scattered events across tools still took "tens of hours every week" — causing release delays, confusion, and extra manual work for PMs, developers, and SREs.

The native integrations are limited. Slack-GitHub is mostly one-way. Linear's GitHub sync handles PR merges well, but it doesn't know about the conversation that led to the ticket. None of them synthesize. They pipe data. There's a difference.

What we built instead

Lisa isn't a project management tool. We have plenty of those. Lisa is product intelligence — she sits across your Slack, Linear, and GitHub and connects the context that currently lives in separate silos.

The distinction matters. Linear helps you manage work. Lisa helps you understand it.

Here's what that looks like in practice. You tell Lisa about a feature you're planning — in plain language, the way you'd describe it to a teammate. She asks the questions a good PM would ask. She clones your repo and reads your codebase, so when she creates issues, they reference actual files and real architectural patterns — not generic placeholders. She writes the PRD you were going to skip. She structures milestones and dependencies. And every artifact she produces links back to the conversation and context that created it.

When someone on Slack mentions a bug or proposes an idea, Lisa surfaces it as a triaged suggestion. You decide if it's actionable. If it is, it becomes a well-scoped issue in seconds — with acceptance criteria, verification hints, and project context. If it's noise, you dismiss it. Either way, you didn't have to go hunting for it.

And sync goes both ways. Update an issue in Linear, Lisa knows. Merge a PR in GitHub, Lisa reflects it. Not because she's copying fields between databases — because she understands the context behind the change.

Why we're building this now

Gartner projects that 80% of traditional project management tasks will be eliminated by AI by 2030. The global market for AI in project management is expected to hit $52.62 billion by then. That's not a trend — it's a structural shift.

But most AI PM tools are adding features to existing paradigms. Smarter task assignment. Auto-generated summaries. Copilot-style suggestions inside an existing tool. Those are useful. They're also incremental.

The harder problem — and the one nobody's really solved — is connecting the context layer. The conversation that led to the decision. The code that implements it. The ticket that tracks it. The team member who's blocked by it. That's not a feature you bolt on. It's a different product entirely.

Start here

Lisa is live. Sign in, connect your repos and integrations, and run your first discovery session. One conversation — that's all it takes to see the difference between a tool that tracks your work and one that understands it.

We're a small team building this for small teams. If you're a PM or founder at a 10-50 person startup and you're tired of being the human router between Slack, Linear, and GitHub, this is for you.

Knows the business, reads the code.

The math nobody talks about

The real problem isn't the tools

What we built instead

Why we're building this now

Start here

References