TEA Commons was born from a simple observation: after 1,500+ hours of coaching early-stage climate teams, we saw the same pattern over and over. Brilliant scientists with breakthrough technologies were making critical commercialization decisions without understanding their economics — not because they didn't want to, but because the tools and data they needed didn't exist in an accessible form.
Teams spent 2–3 months hunting for quality assumption data that a seasoned practitioner could validate in a few minutes. But practitioners are scarce, coaching and consulting is expensive, and the teams without strong networks or institutional support are left to figure it out alone. At scale, this creates a system where data efforts are duplicated endlessly, economic rigor is distributed inequitably, and TEAs are built on assumptions that no one fully trusts — undermining the very models that should be guiding critical R&D decisions.
We believe AI and validated data can finally change this equation. By grounding large language models in a comprehensive, expert-validated industrial data repository, we can deliver personalized, expert-level TEA support at a fraction of the cost — making it accessible to every early-stage climate team in the world.
