Known Moves is a technology product studio and consultancy for pre-seed to Series B startups.
We ship our own products and we can help you ship yours. Our combined experience and expertise spans owning the entire engineering function from hiring to scaling, building teams, architecting systems, and driving real business value through engineering and AI research.
That means engineering innovation, research / ML systems that ship — engineering orgs that scale. We specialize in technical ownership and leadership that moves the business forward.
We've built systems for labels, studios, and maker companies—products that blend technology with craft. Hands-on implementation, infrastructure, DevOps, and security. Over a decade of experience across the music industry, creative arts, and hardware robotics.
Technical strategy & leadership
Fractional engineering leadership for startups and ventures navigating technical inflection points. Architecture decisions, team building, hiring pipelines, and technical roadmaps—with AI strategy and adoption woven in: where to invest, what to buy vs build, and how teams actually integrate new capability without the hype cycle. Agentic tooling and workflow acceleration for engineering orgs that need to move faster without losing rigor. We've scaled teams from 2 to 50+, rebuilt legacy systems, and guided companies through pivots and hypergrowth. Strategic counsel grounded in hands-on experience building and shipping.
Product engineering
Fast, focused product builds that ship. We embed with small teams from discovery through launch—prototypes, user feedback, and rapid iteration without the bloat. Whether it's 0-to-1 MVPs, studio builds, or scaling existing platforms, we bring disciplined execution and technical depth. AI-native features where they belong in the product, not bolted on. Full-stack fluency across web, mobile, APIs, and infrastructure. The kind of engineering that compounds.
AI & ML systems
Machine learning end to end—not agent demos for their own sake. Data pipelines, ETL, and labeling workflows that feed real models. Fine-tuning and training: off-the-shelf models and open weights when they fit, custom architectures and training from scratch when they don't. ML infrastructure for experiments, batch jobs, inference, evaluation, and monitoring. Agent workflows and tool orchestration where they add value, classical ML where that's the sharper tool. From proof-of-concept to production systems that ship and iterate.
