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<h3>An enterprise agent ~25% faster, on ~40% fewer premium tokens</h3>
<p>For an enterprise AI agent, we treated context as a strategic part of the workflow instead of handing the model everything it might need. The system now retrieves only the structures and signals relevant to the specific business question before passing the task to the reasoning model. The agent runs <em class="k">~25%</em> faster on <em class="k">~40%</em> fewer premium-model tokens, with no cut to the depth of analysis. Faster, cheaper, and more reliable, from context engineering alone.</p>
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<h3>200k lines of Java rebuilt as 50k of Python, in two weeks</h3>
<p>A client's production engine had become the bottleneck. Its dev environment went down almost every day for two to three months, and a mobile app launch slipped three or four times waiting on it. We rebuilt it from the ground up, verified against the real clients, and cut over in <em class="k">two weeks</em> with zero rollbacks and no downtime. A quarter of the code, and the daily outages stopped.</p>
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<h3>New video and image models judged in days, not a rebuild cycle</h3>
<p>We built an evaluation studio for generative video and image models, nine of them across four vendors, so identity drift, distorted motion, and quality regressions are caught on reproducible evidence before customers ever see them.</p>
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<h3>200k lines of Java rebuilt as 50k of Python, in two weeks</h3>
<p>A consumer AI video product's engine had become the bottleneck. Its dev environment went down almost every day for two to three months, and a mobile app launch slipped three or four times waiting on it. We rebuilt it from the ground up, verified against the real clients, and cut over in <em class="k">two weeks</em> with zero rollbacks and no downtime. A quarter of the code, and the daily outages stopped.</p>
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