We're a video production company. We make retail advertising. That's been the core of VIA for years, and it still is.
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But finding solutions for our clients has always been part of the job too. Over the last decade, that led us to build a digital asset management platform — something that started as a simple tool and has grown into a pretty involved suite of retail-specific software built around our clients' actual needs. Hundreds of daily users across multiple retail brands use it to manage and distribute tens of thousands of marketing assets.
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Today we shipped a major update to that platform — a completely rebuilt organization system with new navigation, search, and admin controls across every client. But the update itself isn't really the story. It's how we built it.

We Used AI to Build It

Not as a novelty. Not as a proof of concept. We leaned into AI-assisted development as a core part of how this project got done — and we saved an estimated 100 development hours in the process.
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To be clear — AI didn't just do everything. Our software development team was in it the whole way. In the last month alone we worked through over 50 tickets — testing, finding bugs, fixing migrations, iterating on UX. All of that still happened. What changed is how we worked. AI made every step faster, but it also shifted where we spent our time. Instead of grinding through boilerplate, we focused more on the decisions that actually matter — how something should work, how it feels to use, whether the data holds up. It was a totally new workflow, and we were learning the whole time.
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What makes this meaningful isn't just the time savings. It's that this wasn't a small, safe test. This ended up being one of the more challenging projects we could've tackled — the kind of thing where you need the UX dialed in just right and a backend that actually holds up under real usage. It wasn't planned as the proving ground, but that's what it became. And the result is better than what we would've gotten with the old method.
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We learned fast. We stress-tested this approach against a live production platform with real users, documented what we found, and came away with a much clearer picture of how to apply these tools responsibly.

What This Means for Our Clients

We're not a tech company. We're a production company that builds technology. That distinction matters because it means we're not chasing AI for the sake of it — we're applying it to solve real problems for the people we work with.
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For our clients, this translates directly into better tools at a lower cost. The platforms we build are used daily by teams that need them to work — and those teams are often on structured budgets where every dollar has to count. AI-assisted development lets us be more responsive to what our clients actually need without the overhead that used to come with custom software. We can iterate faster, take on more ambitious builds, and keep the tools affordable and accessible.
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We're now building out development workflows based on what we learned from this project. The goal is straightforward: deliver more value, faster, without passing inflated development costs down to the people who depend on these tools.

The Bigger Picture

There's a lot of noise around AI right now. Everyone's talking about it, most people are experimenting with it, and a lot of companies are still figuring out where it actually fits.
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We chose to go deep on a hard problem and learn by doing. That's always been how we operate — whether it's a video project or a software build, we'd rather put in the work and come away with something real than wait for someone else to figure it out first.
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This launch is the result of that approach, and it's just the beginning.

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