About Us
A small group in Texas working in the narrow space between what the research says is possible and what actually survives production.
Our Mission
The gap between what's possible in AI research and what's deployed in production is enormous. Our mission is to close that gap.
We believe the best AI systems come from deep understanding of both the technology and the problem domain. That's why we combine academic rigor with startup speed, reading the papers and shipping the code.
Our Roots
Based in Texas, we build close to a fast-growing community of engineers and founders. We combine academic research roots with hands-on industry engineering experience.
This gives us a unique perspective: we understand both the frontier of research and the realities of enterprise deployment. We speak both languages fluently.
How We Work
Every solution we build is informed by the latest advances in machine learning research. We read the papers, run the experiments, and implement what works.
Research without deployment is just a hobby. We engineer systems that work reliably at scale, with proper monitoring, fallbacks, and maintainability.
We don't push technology for technology's sake. We start with business problems and work backwards to the right technical solutions.
We work as an extension of your team, not as outsiders. Knowledge transfer and capability building are part of every engagement.
Track record
Core-banking architecture — a high-performance transaction engine and a knowledge-graph data model, built for throughput and ledger correctness.
BIM metadata extraction at factory scale, GPU-accelerated factory-layout visualization, and the first retrieval system shipped into a real technical corpus.
The engineering above predates the studio; in 2024 it became one. Real-time WebGPU tooling, constraint-aware CAD, and agentic research systems under a single name.
Fifteen long-form articles on embodied AI, agent systems and real-time graphics — the reasoning behind the work, published rather than pitched.
How we think
We publish the reasoning behind what we build — the tradeoffs, the failure modes, and the parts that are still unsolved. It is a better sample of how we'd approach your problem than any capability list.
Let's build
If you're building something in applied AI, embodied autonomy, real-time 3D, or the systems underneath, tell us about it — including the parts that aren't working yet.