Behind the Build.

In the first episode of Behind the Build, the team traces Informer GO back to its true starting point: a simple chat. CEO Brad Leupen recalls how a personal experiment during morning R&D time, a native desktop chat client, quietly grew over the course of a year into something far bigger.

Brad Leupen (CEO), Andrea Vega (VP of Informer Engineering), Madhavi W. Chandra (Chief Product & Strategy Officer), and Kyle Watson (VP of Sales & Marketing) walk through how that early “chat with your data” idea evolved in stages: a helpful chat bubble, answering questions about governed data, AI-generated “Magic Visuals,” full “Magic Reports,” and ultimately Informer Apps, enterprise applications built and instantly hosted from a simple conversation. The team also shares why AI was built into the Informer platform itself rather than bolted on as an afterthought, and how real customer use pushed Informer GO from a simple chat tool into an app- and report-forward delivery platform.

In this episode of Behind the Build, the team tackles the age-old iceberg problem: the answers are already in your data, they’re just hidden. Brad Leupen (CEO), Andrea Vega (VP of Informer Engineering), Madhavi W. Chandra (Chief Product & Strategy Officer), and Kyle Watson (VP of Sales & Marketing) explore what happens when you’re no longer limited to moving at the speed of humans. Imagine hiring a data science team that runs 24/7 in the background, surfacing “diamonds in the rough,” possibilities you can then explore, test, and refine in rapid cycles.

The conversation also digs into who this power is really for: every department, not just IT or power users, now has the ability to reclaim time from manual processes and act on data they never had access to before. The team discusses how removing traditional friction from app and dashboard creation, going from idea to reality without a heavy build process, puts that power directly in the hands of the people closest to the problem, all within a platform that stays governed and responsible. They also share how this shift is even clearing out their own product backlog, since users no longer have to wait on feature requests; they can just build it themselves.

In this episode of Behind the Build, the conversation shifts from what AI can build to how it’s built responsibly. Brad Leupen (CEO), Andrea Vega (VP of Informer Engineering), Madhavi W. Chandra (Chief Product & Strategy Officer), and Kyle Watson (VP of Sales & Marketing) break down the questions every organization should be asking before adopting an AI solution: what’s the actual capability, how secure is your data, what governance is in place, and are you locked into a single model?

The team explains why data governance isn’t an afterthought at Informer, it’s the backbone that makes AI trustworthy. AI is only ever privy to the data a user would natively have access to, with no leakage, no exfiltration, and full transparency into what query produced a given response. That same governance layer extends to how Magic Reports and Informer Apps get vetted and disseminated across an organization, preserving ownership and approval every step of the way.

As the team puts it, AI can be daunting, it can hallucinate, it can go wrong, but not when the right guardrails, intention, and governance are in place. That’s what makes it safe: decades of data expertise, SOC 2-backed security, and a platform built so users can focus on solving their problem instead of worrying about the pitfalls of data exposure.

In this episode of Behind the Build, the team shares what happens the moment a customer’s pain finally goes away, and why it never stops there. When subject matter experts and domain experts get to work in real time with AI to build the solutions they’ve been wishing for for years, something clicks. They describe it the same way every time: it was fun to build. And once they experience that, they get hooked, moving straight on to the next problem they’ve been living with.

The conversation highlights two very different types of users who both light up when they see what’s possible: AI power users who immediately start imagining what they could build next, and AI newcomers who don’t know where to start until they see a real example, like a campus companion or teaching and learning tool, and realize, “I could have this right now.” The team walks through a standout customer story: a multi-site organization’s painful, manual yearly audit, once a slog through spreadsheets, is now a sustainable, reusable Informer Apps solution, freeing up time to tackle the next challenge.

They close with a look at Cal Lutheran, a customer who’s gone from piloting the platform to building a Board of Regents management application and a major campus-wide student resource initiative, now treating Informer Apps as a cornerstone technology for their university’s future.

In the final episode of Behind the Build, the team turns the camera on themselves, showing how every corner of their own organization, from client services to engineering to product, is building solutions with the very platform they created.

What started as a simple Kanban app to replace an awkward mix of Jira, GitHub, Slack, and Zendesk quickly snowballed: pull in Slack notifications, add a quick search for “what release had that feature,” build a weekly retro board with a kudos section, then bring in a full range of emoji reactions just for fun.

Their weekly one-on-ones used to mean pulling up the CRM and going through it together. Now they built an app for that too, one that automatically surfaces what the data says is important, making both the meeting and the team’s data entry better.

The team reflects on what’s surprised them most: how quickly staff adopted the technology to solve their own everyday problems, and how there doesn’t seem to be a ceiling on what can be built. The more people use it, the more ambitious their ideas become.

With analysts and consulting partners now calling their approach genuinely novel, the team closes the series with real pride in how far they’ve come and real excitement for what’s next.