Environments, libraries and frameworks for applied ML and foundation models.
We're an AI research lab for applied ML and foundation models. We reverse engineer, interpret and build environments, libraries and frameworks. Our dream is to accelerate ML development with precision and intent for all, enabling safer and better ML across physics, life sciences, industrial, frontier and critical verticals.
Backed by
awesome programs.
Our work
1/
We're heading towards a future where foundation models play a significant role across physics, life sciences, industrial, and frontier verticals, and it's critical we find ways to develop and train them better and safer. We're building aq, developer environment for foundation models built with frameworks, libraries, tools and autonomous agents helping design, test, and refine models. aq is built for precision and intent. Think Next.js for JS, but for ML, handling architectural, experiments, and building AI the way it should be built, deliberately, not by guessing.
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Fine-tunes fail in ways a loss curve does not explain. AQIT is Aquin Interp Tooling: an open-source CLI and Python SDK to declare a recipe, train, gate, inspect, and patch on your own GPU, with results staying local. Same objects in both surfaces. Dense transformers, MoE, and embeddings. Attribution, sparse autoencoders, simulation, live training watch, evals, and benchmarks, so you can see why a run failed and fix it without guessing.
3/
We work with frontier labs, research groups, universities, and companies training their own models, from classical ML to foundation models and everything in between.
Training SAEs on a LLM to sense conflict, ambiguity, and tone, and choose the right response.
Labs we worked with
Latest research
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