ESG Data and AI: Building Sustainability Features That Last
Sustainability data is messy, incomplete and increasingly scrutinised. AI helps make sense of it, if the product is honest about what the data can say.
Customers, investors and regulators increasingly want to know the environmental and social footprint of what they buy and fund. That has created demand for product features that surface ESG information: sustainability scores on products, supplier comparisons, impact dashboards. AI is well suited to part of this work, especially turning scattered, unstructured information into something comparable.
It is also an area where trust is fragile. A score that cannot be explained, or a claim that overstates the evidence, can damage the brand it was meant to help. Here is how we approach building sustainability features with AI.
It helps to separate three questions that are often blurred together: what data exists, how trustworthy it is, and how it should be presented. AI can help with the first two. The third is a product decision that should be made deliberately, with input from people who understand both the domain and the audience.
The data problem comes first
ESG information arrives in every format imaginable: supplier spreadsheets, certification documents, sustainability reports, product labels, public databases and self-declared claims. Coverage is uneven, definitions differ and some of it is out of date. Before any model is involved, a product needs to know where each data point came from and how reliable it is.
- Record the source and date of every data point, and keep them visible to users who want to check.
- Distinguish verified data, such as third-party certifications, from self-declared and estimated values.
- Normalise units and definitions explicitly, and document the choices.
- Treat missing data as missing, not as zero or average.
Provenance is not just good practice; it is what lets you correct mistakes. When a supplier updates a certification or a source turns out to be wrong, you need to find every score and comparison that used the old value and recalculate it. Without lineage, that becomes a manual investigation.
Where AI helps
- Extraction. Reading certificates, reports and product pages and pulling out structured fields such as materials, origin and certifications.
- Recognition. Identifying a product from a photo or scan so the right record can be retrieved.
- Matching. Linking the same product or supplier across sources that name it differently.
- Explanation. Turning a structured score into a plain-language summary a shopper or analyst can understand.
Notice what is not on the list: inventing values. A model should never fill a gap with a plausible-sounding number. If an estimate is used, it must come from a documented method and be labelled as an estimate.
Keep humans in the extraction loop
Extraction models make mistakes, especially on poorly scanned documents and unusual layouts. Route low-confidence extractions to a reviewer, and sample high-confidence ones for spot checks. Track error rates by document type and source; a single supplier format that is consistently misread can skew many scores at once.
Example: Sumus
Sumus is an AI product-discovery engine for fashion. A shopper scans a garment and sees its ingredients, quality, safety and availability alongside an ESG footprint, then compares sustainability across brands. We built the AI tool behind it: the scanning and recognition pipeline, the ESG scoring engine and the brand-comparison layer. The full story is in our Sumus case study.
Users rarely need every detail, but they need to know the details exist. A short line such as how this score is calculated, linking to the method and sources, does more for credibility than a longer marketing description of the product's sustainability credentials.
Scoring that people can trust
A single number is attractive for users and dangerous for credibility. A few principles keep scores defensible:
- Publish the method: which factors count, how they are weighted and where the data comes from.
- Show the breakdown behind the headline score, not just the total.
- Show confidence: a score based on verified data should look different from one based on estimates.
- Keep the scoring logic deterministic and versioned. Use models to gather and structure inputs, not to decide the score itself.
- Recalculate when data or method changes, and keep history so past scores can be explained.
Explanations generated by a model need the same care as the score. Constrain them to the data behind the score and the published method, so the summary cannot claim more than the evidence supports. A template with model-filled fields is often safer than free generation for this reason.
Avoiding greenwashing by accident
Rules on environmental claims are tightening in several markets, including the EU and the UK, with growing scrutiny of vague terms like eco-friendly or green without evidence. Products that display sustainability information should avoid generic claims, make comparisons on a like-for-like basis and keep the evidence behind every statement. Check the current requirements in each market with legal advice; the engineering task is to make sure every claim in the interface can be traced back to its data.
Be cautious with comparisons. Two products scored on different amounts of verified data are not directly comparable, even if both scores are shown on the same scale. Indicate data coverage next to any comparison, and consider withholding a comparison entirely when coverage is too thin to support it.
Architecture in brief
A typical setup separates ingestion (connectors and document processing), a normalised data store with provenance, a versioned scoring service, and the user-facing application. AI components sit mainly in ingestion and explanation. That separation means you can improve extraction or swap a model without changing how scores are calculated, and audit each layer independently.
Measuring whether it helps
Decide early what success means for the product. For a consumer tool it might be how often users compare options or change their choice after seeing a score. For a business tool it might be time saved collecting supplier data or the share of a catalogue with verified information. These measures keep the team focused on usefulness rather than on producing more scores.
Building a sustainability product?
We build AI products that handle messy real-world data carefully. Learn about our custom AI development service, or contact us to talk about your ESG or sustainability feature.
