Practices
Five practices. One engineering posture.
We work across the AI/ML problems where production engineering is the bottleneck, not the model. Each practice has its own buyer, its own SLAs, and its own stack — but the choices that decide whether the work ships are the same across all of them.
Safety & Computer Vision
Lead practiceProduction-grade computer-vision platforms for safety, security, and operational intelligence — deployed on the client's infrastructure.
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Agentic Systems
Agent runtimes, multi-agent workflows, and MCP-integrated tool use — engineered for enterprises that require them in production.
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VFX & Animation Pipeline
Generative-AI tooling that integrates cleanly with existing VFX and animation pipelines — Nuke, Houdini, Maya, Blender — without disrupting the production.
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AI-Native Film Production
End-to-end AI-augmented film production across pre-production, production, and post — with IP provenance, talent consent, and audience-integrity guardrails engineered into the pipeline.
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Custom ML & Research
Bespoke ML engineering and applied research — for problems where an off-the-shelf model does not exist, the evaluation criteria do not exist, or the work must begin from the data.
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What ties the practices together
Engineered for production. Measured against defined criteria. Bounded by published responsibility.
Across all five practices, no project enters production without a documented measurement framework. Every practice publishes the categories of work we will not undertake. We deploy on the client's infrastructure where data sensitivity or latency requires it, and on cloud where elastic scale is the appropriate fit. The engineering principles are consistent; the buyer, the technology stack, and the service-level objective vary per practice.
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