Softinn is building foundation models for computer use, starting with synthetic data pipelines

Summary

{
  "long_summary": "Noah Löfquist is building Softinn to train foundation models that operate a computer the way a human would — navigating interfaces, clicking, typing — rather than relying on structured API calls. The pitch is that a huge swathe of enterprise software either has broken APIs or no useful API at all, and the only reliable way to interact with it is through the screen.\n\nThe near-term target is low-complexity, high-frequency tasks: cleaning up an email inbox, scheduling a calendar, hunting down receipts. Cognitively simple for a human, but tedious enough that enterprises will pay to automate them. Löfquist frames these as the wedge before the model can handle genuinely complex work.\n\n**Data as the core bottleneck**\n\nThe biggest constraint isn't compute or algorithms — it's training data. Good computer-use data is scarce, so Softinn built synthetic data pipelines early. For their first model release in April 2025, virtually the entire dataset was synthetically generated. The longer-term plan is to blend synthetic data with human-labeled examples and data captured from live product usage.\n\n**Go-to-market**\n\nThe initial commercial model is API-first: sell access to a strong computer-use model to enterprises and startups building products across different verticals. Consumer applications are on the roadmap but treated as a later-stage bet. Löfquist notes that no computer-use product — not ChatGPT's Operator, not Anthropic's Computer Use — has yet achieved the kind of breakout adoption that basic ChatGPT did, which suggests the market is still early and the category is still being defined.\n\nThe forward-looking architecture Löfquist describes has models fluidly switching between computer-use mode and structured API calls depending on what the task requires — not a binary choice between the two paradigms."
}