Our ARR grew 13x in the last 12 months, and we did it with a deliberately small team. Now we're looking for an ambitious software engineer to join us as we start to scale up. We don't care much about credentials; we care about what you can build, how fast you learn, and how you collaborate. Adam and I started Evidence because we were frustrated with the state of BI tools. We hated using them, and while code was automating other parts of our work, BI was still stuck in proprietary drag-and-drop interfaces. So we built an entirely code-based alternative and launched it as an open source project. Today, Evidence is used by thousands of organizations around the world, including Fortune 500 companies, global investment managers, and national governments. And increasingly, it’s what AI coding agents reach for when they need to build analytics. Our commercial product, Evidence Studio - which includes SSO, access control, AI agents, email schedules, Slackbot, and a ton more - is growing rapidly. We're seeing more companies replace tools like Tableau and Power BI with Evidence. But our ambition has always been much larger than building another BI tool. Our goal is to reshape how organizations work with data and communicate what they know, and AI is opening up an entirely new opportunity to do that. Our next phase is all about AI. We want to help data teams build self-driving analytics systems for their organizations - systems that proactively surface insights and improve themselves, while giving anyone the freedom to explore data, get answers, and create. We're building the context, tools, and feedback loops that let AI do real data work in a reliable, governed way. Given our small team, the people who join us have an outsized impact. You'll work directly with customers and the founders, make major product and technical decisions, and help shape where Evidence goes next. We're looking for someone who loves solving open-ended problems and cares deeply about product quality. If that sounds like you, DM me. And if someone great comes to mind, I'd love an introduction.

Data work is behind software engineering in AI adoption - but not for long. In my recent talk at AI Council, I explored what BI looks like in a world where AI is exceptionally capable (video in comments). The most interesting AI systems in software today are built around closed feedback loops: agents observe usage, make changes, validate outcomes, and improve over time. The same pattern is coming to analytics. The key prerequisite is treating analytics as code. When reports and analysis are defined in code, agents can read, write, review, and improve them just like software. That's how we'll move from AI-assisted workflows to systems that continuously improve themselves.

A sneak peek of our upcoming Insights feature in Evidence. Have an agent build an ad hoc analysis for you, then save it and share it with your team. The agent has context from all your reports, so it can reuse definitions and visualizations. Or it can point you to a report that already answers your question. Each Insight is saved with the original prompt and any methodology that went into creating the work, making it auditable and reusable.

AI + BI is great. But AI + BI + CI is where things get really interesting. Here’s what that looks like in practice. You start with a Linear issue. You assign an agent. It generates an Evidence report, opens a pull request, and sends you a link. The code is simple and readable. It’s just markdown. You can scan it and immediately understand what’s happening. The Evidence GitHub bot runs validation tests and adds a preview link so you can see the report before it goes live. You can merge with full confidence that nothing has broken. This is the CI portion that most AI platforms are missing. It means you get you the speed of vibe code-able reporting, but with the governance and reliability of production software.

With usage analytics data in Evidence, it's becoming possible to automate the full reporting lifecycle. Today you can build a report to recommend actions based on user data. The next step is plugging in AI to proactively recommend actions for you. That's where we're heading and it's getting very exciting.

Love getting messages like this. We consider customer feedback to be an extension of our product experience. The faster we can fix issues, the more delightful the experience of working with us. It's an unbeatable feeling to see it working for customers.