Here are some examples of outcomes:
• A large North American beverage company processed more than 1,300 utility invoices in two days, that would have taken three weeks manually.
• An American tech company uploaded a full year of utility bills in 30 minutes for automatic ingestion.
• A North American tech company came to us with data they considered too messy to upload, assuming manual transformation would have taken days. Our AI data cleaning agents helped them finish in 30 minutes.
• A large European manufacturing company with tens of thousands of employees finished their SB 261( California climate risk disclosure) report in < 2 days with AI-powered reporting.
• An American consumer goods company used AI-powered Product Footprints to model the cost-saving impacts of moving to bio-based materials, building a connection between the sustainability and finance teams internally.
Q. HOW DO YOU DECIDE WHERE AI ADDS THE MOST VALUE VERSUS WHERE TRADITIONAL DATA MODELING OR HUMAN EXPERTISE SHOULD LEAD?
» We look for a mix of impact and risk. AI is great when:
• The task is repetitive at scale, like mapping thousands of line items consistently or doing research to understand how a product is manufactured to produce an LCA.
• The work is bottlenecked by human time, like first-pass report drafting or gap analysis.
• The output can be reviewed and validated before it becomes“ official.”
We avoid or constrain AI when:
• The system should be deterministic, such as pulling certain factual target data where you do not want the model“ deciding” anything.
• The cost of a mistake is too high unless we can build strong guardrails and evaluation around it.
88 May 2026