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Engineering intelligence you can trust
You’ve got half a dozen AI tools in your stack, your engineers say they’ve never moved faster, and your last board meeting still ended with: “When can we see some results?”
Welcome to 2026.
The promise was simple: give engineers AI tools, they ship more, everyone wins. And on an individual level, it’s working. Most engineers are faster. Together with their agents, they can generate pull requests in minutes, migrate codebases in weeks, and automate half the tasks that used to eat up their day.
But not every organization is seeing the gains.
Some teams have doubled their output this year with the same headcount. They’re shipping features in days that used to take sprints. Bug fixes that used to take weeks now reach customers in minutes.
Others gave their engineers the same tools — and barely moved the needle. Their engineers are faster, but the organization around them isn’t. Work still gets stuck in triage meetings, coordination overhead, and review bottlenecks. The AI made the code faster. Everything else stayed the same.
Same tools. Wildly different results. And that gap is getting wider fast.
Welcome to 2026.
The promise was simple: give engineers AI tools, they ship more, everyone wins. And on an individual level, it’s working. Most engineers are faster. Together with their agents, they can generate pull requests in minutes, migrate codebases in weeks, and automate half the tasks that used to eat up their day.
But not every organization is seeing the gains.
Some teams have doubled their output this year with the same headcount. They’re shipping features in days that used to take sprints. Bug fixes that used to take weeks now reach customers in minutes.
Others gave their engineers the same tools — and barely moved the needle. Their engineers are faster, but the organization around them isn’t. Work still gets stuck in triage meetings, coordination overhead, and review bottlenecks. The AI made the code faster. Everything else stayed the same.
Same tools. Wildly different results. And that gap is getting wider fast.
How we got here
We started Swarmia in 2019 — before the AI boom, before DORA and SPACE were table stakes, before “engineering intelligence” was a category anyone was buying.
We built it because the tools that existed treated engineering like a machine to be monitored and engineers like parts to be measured.
We thought there was a better way. We still do.
After seven years of working with hundreds of engineering organizations, we keep coming back to the same thing: you can’t guess your way to engineering effectiveness, and you can’t make good decisions without understanding what’s actually happening in your organization.
Where time goes. Where work gets stuck. Why some teams thrive while others spin their wheels. And whether any of it connects to what customers need.
This has always been true. But right now, the stakes are higher than they’ve ever been. AI is rewriting the rules of how software gets built — and the organizations that figure out the new playbook first will pull away from everyone else. Engineers and agents are shipping code faster than most review processes can handle. The teams that are adapting — rethinking code review, investing in CI and automated testing, removing coordination overhead — are compounding their gains month over month.
The teams that aren’t? They’re falling behind in ways that will be very hard to reverse.
Most tools in this space don’t help you navigate that shift.
Many get the data wrong: they expect perfect Jira hygiene, assume that teams work the same way, and happily plot apples and oranges in the same graph. When developers ask where the data is coming from and you can’t answer, trust erodes fast — and once it’s gone, it’s hard to get back.
And even when they do get the data right, they still leave you staring at numbers on a dashboard, asking yourself: now what?
We built it because the tools that existed treated engineering like a machine to be monitored and engineers like parts to be measured.
We thought there was a better way. We still do.
After seven years of working with hundreds of engineering organizations, we keep coming back to the same thing: you can’t guess your way to engineering effectiveness, and you can’t make good decisions without understanding what’s actually happening in your organization.
Where time goes. Where work gets stuck. Why some teams thrive while others spin their wheels. And whether any of it connects to what customers need.
This has always been true. But right now, the stakes are higher than they’ve ever been. AI is rewriting the rules of how software gets built — and the organizations that figure out the new playbook first will pull away from everyone else. Engineers and agents are shipping code faster than most review processes can handle. The teams that are adapting — rethinking code review, investing in CI and automated testing, removing coordination overhead — are compounding their gains month over month.
The teams that aren’t? They’re falling behind in ways that will be very hard to reverse.
Most tools in this space don’t help you navigate that shift.
Many get the data wrong: they expect perfect Jira hygiene, assume that teams work the same way, and happily plot apples and oranges in the same graph. When developers ask where the data is coming from and you can’t answer, trust erodes fast — and once it’s gone, it’s hard to get back.
And even when they do get the data right, they still leave you staring at numbers on a dashboard, asking yourself: now what?
How Swarmia can help
Swarmia helps you answer three questions: Are you focused on the right problems? Is work flowing through your teams without bottlenecks? And do your engineers have what they need to do their best work? Each question matters on its own. Together, they give you the full picture.
Most importantly, it helps you answer the now what.
We don’t just tell you your AI adoption is low or that your cycle time is high. We show you where work is getting stuck, which teams need support, and what patterns are emerging across your organization. We connect metrics with survey responses so you understand the human context behind the numbers.
And then we help you actually do something about it — through signals, agents, working agreements, and notifications that fit into your existing workflows.
With Swarmia, you’ll be able to see what’s changing, adapt your practices based on data, and make sure your AI investments are actually paying off. Not just producing more code, but delivering more value to your customers.
Most importantly, it helps you answer the now what.
We don’t just tell you your AI adoption is low or that your cycle time is high. We show you where work is getting stuck, which teams need support, and what patterns are emerging across your organization. We connect metrics with survey responses so you understand the human context behind the numbers.
And then we help you actually do something about it — through signals, agents, working agreements, and notifications that fit into your existing workflows.
With Swarmia, you’ll be able to see what’s changing, adapt your practices based on data, and make sure your AI investments are actually paying off. Not just producing more code, but delivering more value to your customers.
What we promise
The opportunity in front of engineering organizations right now is huge. Teams that change how they work — not just what AI tools they use — are shipping faster, shipping more, and shipping the right things.
We help you capture that opportunity. We show you what’s working, what’s not, and what to do about it. We help you measure the real impact of your AI investments — not just adoption rates, but whether that adoption is translating into better outcomes.
And we get the data right. Because if you can’t trust it, none of this matters.
The gap between good and great software organizations is growing. We’re here to make sure you’re on the right side of it.
We help you capture that opportunity. We show you what’s working, what’s not, and what to do about it. We help you measure the real impact of your AI investments — not just adoption rates, but whether that adoption is translating into better outcomes.
And we get the data right. Because if you can’t trust it, none of this matters.
The gap between good and great software organizations is growing. We’re here to make sure you’re on the right side of it.
Want to learn more about running an effective engineering organization?
We’ve got you.