Field notes from building production AI
No generic explainers. Just the architecture, data, and hard-won lessons from AI systems we've actually shipped - agents, automation, computer vision, and digital twins that run in production.
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FeaturedThe AI outbound engine: from cold volume to qualified pipeline
AI discovers and verifies accounts, personalizes at scale, and auto-classifies every reply, so reps only ever touch the deals that matter.
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Real-time digital twins for safety infrastructure
Turn distributed safety systems into one live view with early warning: sensor pipelines, a real-time twin, and anomaly detection.
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Why AI agents break in production - and the architecture that keeps them running
Most agent demos never survive real users and real data. Here's the evals-guardrails-human-in-the-loop architecture we ship instead, and where no-code falls apart.
Read the articleMore field notes are on the way - computer vision accuracy, digital-twin data pipelines, and the numbers behind our automation work. In the meantime, the case studies show these ideas in production.
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