It is not a substitute for measurement or change. Use it where it makes existing sustainability work faster and more accessible, not as a green-washing layer. AI itself has a significant energy footprint and you should be honest about it: pick efficient stacks, prefer smaller well-grounded models where they fit, and track the cost.
The clearest example: Evangelical Bank Sustainability Scoring. Evangelical Bank, a leading sustainable banking provider, came to us at the end of 2022 with an internal Vue.js prototype of a sustainability scoring tool. They wanted to enable their clients to generate ESG insights themselves rather than just being asked for ESG data in credit processes.
We took the prototype and built it into a production tool over 6-9 months of Scrum. The platform aligns client businesses with the Paris 1.5 °C climate target via XDC equivalence metrics, alongside standard CO₂ equivalence. Multi-organisation aggregation lets users roll up sub-organisations into combined sustainability reports. Stack: Vue + TypeScript front-end, Node + TypeScript back-end, Postgres database, Docker, Kubernetes, Helm, GitHub Actions, all on bank-compliant Kubernetes infrastructure. Quote on record from Christian Tewes, Strategic Transformation Officer, Evangelical Bank.
For ESG knowledge work specifically, the same retrieval-augmented generation pattern we used for the German Insurers Association (GDV) – conversational questions across tens of thousands of documents for 400+ member companies – transfers directly to CSRD, ESRS, IPCC and your internal sustainability documentation.
ESG knowledge assistants
Sustainability scoring
Drafted reporting
ESG navigation
sustainability teams find what they need in regulation and frameworks faster
Aligned with Paris 1.5 °C
XDC equivalence metrics in the Evangelical Bank scoring tool, alongside standard CO₂ equivalence
Time to first project
first version in four short sprints, the way the RAG chatbot for a leading member network was delivered
Honest about energy cost
we track and optimise AI's own energy footprint, not hide it
EU-hosted by default
n8n in Berlin, Qdrant in the EU, Azure OpenAI via Microsoft EU
1. Discovery workshop
2. Build a working prototype
3. Hand over
We bring a decade of impact-tech experience and more than 160 AI projects since 2019. Through our free AI for Impact course, more than 100,000 people have learned how to use AI for the common good. We do not run inspiration days. We run scoping sessions and build engagements that ship, the way we have delivered AI for the organisations below:
AI itself uses energy – do not pretend otherwise. Training a large model can consume tens of megawatts of electricity over many days; inference can be the majority of the total ML footprint. Pick efficient stacks. Track the cost. Optimise it.
AI does not measure or change anything by itself. Use it to navigate documentation, draft reports for human review, and support scoring decisions. Real measurement and real change still need humans and instruments.
Green-washing with AI is worse than no AI. If a tool's only purpose is to generate plausible-sounding language about sustainability, it is part of the problem, not the solution.
Doesn't AI use a lot of energy?
Where is AI most useful for sustainability work?
Can AI help with reporting?
What about green-washing?
Does N3XTCODER work on AI climate solutions?
How long does a sustainability AI project take?
Tell us about your sustainability documentation or scoring need. We will reply with a proposal and a date.
Simon Stegemann
Co-Founder and CEO
