The sustainability data problem isn't too little or too much. It's both, at the same time.

Ask a sustainability manager what their week looks like and you'll usually hear one of two very different points of view, depending on which part of their portfolio you're asking about.

For the large, well-covered companies in their scope, there's almost too much to work with: annual reports, sustainability reports, codes of conduct, third-party ratings, press coverage. A thorough analysis means reading all of it, reconciling it, and figuring out what actually changed from last year's documentation. No team has the hours to do that properly for every company they track, so most don't. They skim, they sample, or they fall back on whatever a colleague read last quarter.

For everyone else, the smaller suppliers, the ones two or three tiers down a supply chain, the problem is the opposite. There's barely anything public to work with. No sustainability report, no rating, nothing indexed anywhere. The only way to get an answer is to email the supplier directly, then wait, chase, and hope the response is honest and complete.

Neither of these is a "not enough AI" problem. It's an organizational one: the effort required to do this well doesn't scale with the size of the team asking the question.

Why generic AI tools don't close either gap

A general-purpose AI model doesn't fix the too-much-data problem. It just reads faster, with no guarantee it read correctly, and no way to check its work without going back and re-reading the source yourself anyway. It doesn't touch the too-little-data problem at all, because if nothing was written down, there's nothing for a language model to summarize. Point a chatbot at a blank page and you get either silence or, worse, a confident-sounding guess.

What actually helps isn't a smarter model. It's a system that knows what to look for, checks its own work before you ever see it, and is honest about the difference between "we found nothing" and "we found something and verified it."

What we built differently

Three things matter more than the AI model underneath, and they're where we've put the real engineering effort.

The first is having a point of view instead of trying to extract everything. PlanetAI isn't built to pull every fact off a page. It's built around the specific questions teams actually ask about competitors, customers, and partners: what did they commit to, what have they actually done, how do third parties rate them, has anything changed. That framing is what turns a pile of reports into something a team can act on in minutes instead of days, because the system already knows what "relevant" means for this use case.

The second is verification. Every claim gets checked more than once: against the same source read differently, and against independent sources like ratings, news, and filings, before it's treated as "reliable." A number that only appears once, that contradicts another source, or that the system can't re-confirm doesn't get presented as fact. That's the gap between an AI having summarized something and a team being able to stand behind it.

The third is traceability, by default rather than as an afterthought. Every claim in PlanetAI links back to the exact document and page (PDF or website) it came from. Nobody on the receiving end has to open a search engine and go hunting to confirm a number is real; the receipt is already attached. That's what makes the output usable by the human still doing the work, building a deck for leadership or walking into a partnership conversation. The goal was never to remove the expert from the process. It's to give them a tool that actually makes sense of the source material for them.

And for the companies with almost nothing public

The same discipline applies at the thin end of the data spectrum. Rather than assuming volume, the system goes looking across every public trace that exists and says plainly when the honest answer is "very little is available, and here's exactly what we could confirm." That's a less exciting answer than a confident paragraph a general chatbot might produce, but it's the one a team can actually rely on when deciding whether to escalate a request directly to a supplier.

What this changes for a team

The point was never to replace judgment. It's to stop burning it on document hunting and cross-checking. One pilot customer described the shift simply: sustainability analysis that used to take months of manual work now takes minutes, with a source attached to every figure. That's not a faster chatbot. It's the difference between a team that spends its time reading and one that spends its time deciding.