Sustainability OS

From reporting to steering

That shifts the work. Away from assembling reports — today sixty to seventy percent of the time goes there — toward steering. The system requests missing actions, metrics and evidence from the right people and flags when a target goes off track; the chasing stops. Instead, your team plays opportunities into the organisation, steers initiatives and supports implementation. A new level: from compliance to steering.

A system that adapts to you

And without anyone having to move. You don't have to force your work into a software's rigid input mask — on the contrary, the OS adapts to your way of working and your company's reality, not the other way around. Your specialist systems stay where they are; the OS sits one layer above and connects.

Finance, HR, EHS and Procurement come together in one place and ask the system themselves — instead of sending you the next email. Knowledge no longer sits in individual heads; it belongs to the team. Topic owners contribute actions, concepts and metrics through a guided process.

A reliable foundation

The core is the structure: every metric, every target, every requirement is captured exactly once — with a reference to its source, owner and evidence. Every query draws on the same information. The system invents nothing.

That sounds unremarkable, but it's the decisive point. Structured context isn't a nice-to-have — it decides whether AI helps or hallucinates. Only on such a solid foundation can you reliably work on with AI and build deeper workflows on top.

And the report? It falls out the back: the same truth feeds ESRS, CDP, ISSB as one view; you answer the next rating from what's already there. Reporting and assurance readiness become a state you can query at any time — no year-end fire drill.

AI you can put your name under

Your team already uses AI for reporting — or the board is asking why not. So ESRS requirements end up in ChatGPT: mapping data points, estimating Scope 3, drafting disclosure text. The result sounds brilliant — fluent, confident, plausible. That's exactly the trap. Somewhere in that polished text sits an invented emission factor, a number nobody ever measured, a reference to an ESRS paragraph that doesn't exist. You don't see it — hallucinations look like facts. And the text goes into an audited disclosure.

The OS doesn't collect data and doesn't store evidence — both stay in your specialist tools. It's the layer above that links every reported figure to its trail: back to the source, to the control performed, to the owner. The AI works only along these trails. It phrases what's anchored — a figure anchored nowhere it cannot state as fact; the spot stays visibly open and goes to a human. For the auditor that means: for every figure, the trail back to its source — no surprises.

You no longer comb through the whole text for invisible errors — you look at a small, marked area. The AI does the legwork, the system keeps it honest.

What you gain

• One shared picture the whole team can query itself — instead of everything through one or two heads.
• Real-time control — deviations show up early, not at year-end.
• Assurance confidence that's built in from the start, not at the auditor.
• Reporting into every framework, without collecting the same numbers again.
• A foundation AI works on reliably — instead of confidently inventing.
• Knowledge that stays in-house — independent of individuals and free of vendor lock-in.

Who this is for

For in-house sustainability functions that are subject to CSRD and complex enough that "documents in folders" breaks down: several frameworks and ratings in parallel, many topics, many functions involved.

Especially relevant when you type the same numbers into different questionnaires again and again, data provenance is hard to prove, or reporting quality hangs on one or two people.

How we work together

Final scope after a short conversation.