Most “AI for sustainability” conversations stay at the level of measurement: AI to track emissions, AI to flag supply-chain risk, AI to score ESG ratings. That work is real and valuable, but it’s not where the leverage is. The leverage is upstream, in the research, synthesis, and systems-change work that determines what to measure in the first place.
This is what we do: help sustainability and environmental consulting teams use AI as a serious research collaborator on the kinds of questions that previously required months of manual literature review and stakeholder analysis. The goal isn’t speed for its own sake. It’s the ability to ask harder questions and trace them through more sources than a small team could ever cover by hand.
Who this is for
You’ll get the most from working with us if you’re:
- A sustainability or ESG team responsible for research-heavy deliverables (materiality assessments, regulatory analyses, impact reports, stakeholder mapping) and feeling the bottleneck of human synthesis time
- A regenerative-economy researcher or practitioner working on circular economies, agroecology, climate adaptation, or related systems-change disciplines
- An environmental consulting firm looking to upgrade research and analysis capabilities without expanding headcount
- A research institution or NGO doing policy work, literature reviews, or stakeholder analysis at a pace that’s no longer sustainable
We’re less of a fit if you’re looking for AI-driven ESG reporting automation (different domain, different tool stack) or if your interest is primarily AI-for-marketing or AI-for-operations. Sustainability research is what we do.
How we work
Three engagement types, each scoped to a different starting point:
1. Workflow assessment
A practical review of how your team currently does research, where the synthesis bottlenecks are, and which AI tools (Claude, GPT, NotebookLM, etc.) would meaningfully change the work. We don’t recommend tooling we wouldn’t use ourselves. Output: a workflow blueprint, recommended tool stack, and a working pilot on one of your real research projects.
2. Embedded research project
We embed alongside your team on a specific research project (a materiality assessment, a literature synthesis, a regulatory landscape, a methodology deep-dive) and build the AI workflow as we go. You get the deliverable; your team gets the method, fully transferred.
3. Capability transfer
For organizations that have already started using AI for research and want to systematize. Includes practitioner training, internal documentation, and ongoing review of methodology decisions. Particularly suited to teams handling sensitive or high-stakes research where a “just use AI” answer is insufficient.
Our approach
Three principles guide how we work:
Tool-agnostic. We use Claude, GPT, Gemini, NotebookLM, and increasingly purpose-built research tools. We have opinions about what each is good for, but we don’t sell tools. We sell better research workflows. If a workflow is better executed with no AI at all, we’ll tell you.
Practitioner-first. Every workflow we recommend is one we use ourselves on real sustainability research. We’ve published our methodology for using Claude in circular-economy research synthesis. The workflows we build for clients are the same workflows we use for our own work.
Rigor over speed. AI lets you do more research faster, but the failure mode is doing more sloppy research faster. Our workflows include explicit checkpoints for citation verification, cross-source triangulation, and human judgment. The cost of a confidently wrong sustainability recommendation is much higher than the cost of taking another week.
Where we’ve applied this
- Circular-economy research synthesis: see our published methodology in Claude for Sustainability Research: A Practitioner’s Workflow
- AI in education research: multi-month analysis of generative AI usage data across educator workflows; see our published findings
- Regenerative-economy field projects: fishing-net recycling, bamboo-bike supply chains, and other circular-economy interventions across Spain, Indonesia, and Africa; see our project portfolio
What makes this different
If you compare this to a generic AI consulting or sustainability consulting engagement, two things stand out:
- We’ve actually done sustainability research using AI. Most AI consultants understand the tools but not the domain. Most sustainability consultants understand the domain but not what AI changes about the work. We’ve spent the last several years working at this intersection, both as practitioners and as builders of AI products.
- We don’t sell technology adoption. We sell better research. The difference shows up in what we won’t recommend: tools that look impressive in a demo but don’t survive contact with a working researcher, automation that erodes critical judgment, vendor lock-in that costs you flexibility three years from now.
Get in touch
If this sounds like the kind of engagement you’d benefit from, or if you’re not sure but want to talk through where AI could move the needle on a specific project, get in touch.