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AI isn’t something we sprinkle on top of projects at Bobcats Coding. It’s how we work.
We’ve deliberately built AI into our delivery system: how we specify, how we build, how we test, and how we learn. So when you work with us, you're not just getting an AI-capable team, but a team whose internal mechanics are fundamentally different. And that difference shows up in speed, clarity, and reliability.
That matters because about 90% of our work comes from US-based teams who need shipping momentum they can actually trust.
In our experience, every time the industry moves into a new era, like cloud, mobile, DevOps, or modern frontend stacks, the teams who keep winning aren’t the ones who chase every new tool. They’re the ones who adapt their delivery mechanics fast enough to turn the shift into an advantage. AI is this kind of shift.
We built Bobcats to be that team: engineering partners who stay current, adapt fast, ship reliably, and treat your product like it’s ours to make it succeed.
HOW WE USE AI?
We still believe great product work comes down to a simple loop: Specify → Build → Analyze → Repeat.​
What's changed is that AI rewired the economics inside that loop.
We don’t even have to go back that far to a time when Build was the most expensive part of product development, sometimes by an order of magnitude. It cost 10x more than Specify or Analyze, so teams front-loaded everything into requirements. In the AI era, Build doesn’t have to be the bottleneck anymore. Execution can be dramatically streamlined and faster, which means we can ship more iterations, learn sooner, and de-risk the product through real usage.
That shift is what’s changing how we deliver at Bobcats Coding. Our priority is to optimize Build, so we can ship more iterations faster, with less risk. We invest in AI-assisted workflows and modern toolchains that speed up delivery without compromising production quality. And we adapt Specify and Analyze to match that pace, so every cycle feeds directly into the next.
OUR AI FIELD NOTES
Over the past few months, our engineers have been building a living internal library of the tools, processes, and workflows we use to keep that loop running faster in the AI era. We’re now open-sourcing it as our AI Field Notes. Unlike a static “best practices” page, it keeps growing from our recent client projects, internal experiments, and conversations with experts and clients day by day.
Inside you'll find:
We're not making this public to impress anyone, but rather to be useful. This is our way of being transparent about how we work and making our delivery system easier to understand, adopt, and trust.
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