The LLM Data Company

Paper Instruments is best understood as a specialist model lab rather than a product company you sign up for and use on Monday. The strongest evidence that the specialization claim is real is Kos-1 Lite, shipped as a leading medical model, and Kos-1 Experimental, which scaled environment-free reinforcement learning to 1 trillion parameters on Kimi K2.5 — a training-efficiency result that matters if your budget is the constraint. Curriculum, the company's autoresearch system, is the mechanism that generates tasks and rewards, and it's the piece you'd actually inherit if you bought into the domain-training pitch. Where it differs from generalist providers is verifiability: the published rubric judge training notes, DiligenceBench for long-form equity-research agents, and DRACO (built with Perplexity) for deep research evaluation give an internal eval team real scaffolding instead of a vendor benchmark you have to take on faith. The weaknesses are real too. Feather, the agent harness for knowledge work, and Ultramarine, the frontier model line, both carry a coming-soon label, and the whole company is mid-rebrand to Paper Instruments, so documentation and access paths are in transition. The only currently described shipping artifact is Paper Office, an agent-first Python library for document creation and editing, which implies an engineering audience comfortable writing code. If you don't run your own evals, most of this lab's value is unclaimable — its whole argument is accuracy you can prove internally. Fit it into a stack where a generalist model handles the breadth and a Kos-class specialist handles the narrow, high-accuracy slice, and it reads as a credible shortlist candidate. Expect to contact the vendor directly for access terms rather than self-serve through a pricing page.