AI & Sustainability Benchmark Sprint
Sustainability AI Evaluation & Benchmarking
AI can produce fluent answers about sustainability reports, ESG data, or climate claims. That does not mean the answers are correct, complete, traceable, or suitable for the decision.
Evaluation gaps
What must be tested
Building, buying, or evaluating AI for sustainability and ESG
Demos look convincing. Precision, recall, evidence quality, and failure modes are rarely defined.
Without a clear task and human agreement baseline, you cannot tell if the model improved anything that matters.
Experts need citations, uncertainty estimates, and review workflows rather than a black-box score.
Marketing and sales pressure push automation further than the science or data quality supports.
Screening vendors is useful; without an evaluation design you still do not know if *your* pipeline works.
How the Benchmark Sprint works
Step 1: Define the task
What the AI should do, for whom, and what 'good enough' means for the decision.
Step 2: Design evaluation
Data selection, annotation protocol, training and independent test sets, expert baseline, evidence requirements, metrics, and failure modes that matter.
Step 3: Test the pipeline
Run structured comparisons, capture errors, and map where human review is required.
Step 4: Recommend next steps
We recommend governance measures, quality improvements, tooling changes, or a decision not to scale yet.
Relevant experience
What we bring
Measurement-first sustainability AI for research and applied work
We run an open research initiative on AI benchmarks for sustainability report analysis, covering task design, training and test data boundaries, annotation quality, evaluation structure, and evidence quality.
Where useful, we connect evaluation work to open tools such as the OpenSustainability Analysis Framework.
For Score4More, we helped set up a collaboration with University of Zurich researchers around a real text-selection problem: does the AI retrieve information that is useful for an answer, not merely relevant to the topic? Professional analysts built the labels, and the collaboration produced the UsefulBench dataset and working paper.
Where the question requires it, we structure collaboration between researchers, company or public-interest stakeholders, domain experts, and product engineers. Scientific and domain review remain with qualified contributors.
Sprint outputs
What you get
Typical outputs from the AI & Sustainability Benchmark Sprint
Evaluation design
Task definition and how success will be measured.
Dataset and benchmark protocol
Data provenance, annotation guidance, quality controls, training and test boundaries, and a sampling approach suited to the use case.
Human-in-the-loop workflow
Where experts must review, correct, or override.
Failure-mode analysis
Where the pipeline breaks and how bad that is for your decisions.
Quality and governance recommendations
What is safe to automate and what is not.
Improvement roadmap
Concrete next steps for models, data, or process.
Core case study
UsefulBench: a benchmark built around a real pipeline problem
How a company, professional analysts, Climate+Tech, and university researchers worked together
Score4More needed to know whether its retrieval pipeline found evidence useful for an analyst's decision, rather than passages that only matched the topic.
Climate+Tech helped set up the university collaboration. Professional analysts supplied real questions and labels; researchers and engineers turned the workflow into a reproducible benchmark.
The UsefulBench dataset and working paper document the experiments, model errors, role of domain expertise, and remaining limitations.
Frequently Asked Questions
Do you only screen vendor tools?
No. Vendor screening can be part of the work, but this package evaluates whether your pipeline or a candidate pipeline is reliable enough for the task.
How is this related to your research benchmark?
The research project builds shared evaluation infrastructure. This sprint applies evaluation design to your concrete use case and constraints.
Can this support a research dataset or publication?
Yes, where the work has an appropriate research question, permissions, academic leadership, and sufficient evidence. See our applied research collaboration model for university-company roles, supervised student contributions, open releases, and publication pathways.
How is this priced?
Scope and price are agreed after a 60-minute scoping call. We do not publish fixed package prices.
Who is this for?
ESG software teams, sustainability analysts, rating and scoring organizations, consultancies, investors, research groups, and AI teams working with climate or sustainability documents.
What if we mainly need implementation?
If evaluation is not the bottleneck, see ESG Audit Support or the Boundary & Product Sprint.
Test your sustainability AI before you trust it
Book a 60-minute scoping call to see whether a Benchmark Sprint is the right next step.
We usually reply within a few working days to schedule.
Book a 60-minute scoping call
Tell us what the AI is supposed to do and what you are unsure about. We will help define an evaluation approach or recommend another package.