Your data stays where it is.
The most common blocker to frontier AI in a sustainability function isn't capability — it's the legal and IT question of what leaves the building. Engagements are structured so that question doesn't have to be answered up front.
We work from problem statements, not datasets
You describe the question. Demonstrating what frontier models can do with a problem of that shape doesn't require your supplier contracts, emissions ledger, or production data.
Demonstrations run on public, synthetic, or aggregate inputs
Industry benchmarks, published disclosures, geospatial and satellite data, anonymised proxies constructed to match your profile. You see the method and judge the quality, then decide whether and where to run it on real data.
If real data comes into scope, governance is mapped first
Which data classes can move and which can't, and what a compliant architecture looks like — on-premise, private deployment, or partner tooling inside your own environment. That mapping is part of the roadmap deliverable.
IT is in the room from the start
Most enterprise AI adoption stalls at exactly this point: the pilot works, legal reviews it, it dies. Governance before deployment isn't caution — it's the only route that reaches production.
Honest boundary
Some analyses genuinely require real data. Deep supplier-level Scope 3 modelling on an actual ledger needs the actual ledger. Where that's the case, the governance track in the roadmap is the path — real work, scoped and priced as such.