We turned a three-week
review into an afternoon.
Carbon credits are only as credible as the documents behind them. We built the system that reads those documents the way an expert would, at a fraction of the time, and shows its evidence for every call.
- REVIEW TIME
- ≈−70%
- LANGUAGES
- JA / EN
- PIPELINE
- 5-STAGE
- STATUS
- IN PRODUCTION
Expert judgement doesn't scale, until you teach it to.
Every carbon credit rests on a long, technical, often bilingual document that a human expert has to read end to end and grade against methodologies that keep changing. It's slow, costly, and almost impossible to keep perfectly consistent from one reviewer to the next.
A leading energy enterprise needed that judgement at volume, without lowering the bar, and with a verdict they could put in front of a regulator. That's the brief we took on.
Read, see, describe, judge, defend.
Read
OCR pulls clean text from dense, multi-format project documents, the part humans dread.
See
Figure extraction isolates the charts and tables where the real evidence actually hides.
Describe
AI alt-text captures what each figure says, so nothing slips past the evaluator.
Judge
An LLM scores against bilingual methodologies, criterion by criterion, the same way every time.
Defend
A single verdict, with the evidence trail behind it, ready for scrutiny.
An internal tool that became a national conversation.
In talks to integrate it into national infrastructure
Featured for the technology behind it
Running live, with us as the ongoing engineering partner
Evaluates in Japanese and English, with full evidence trails
We didn't hand it over and walk away. We're still the engineering partner behind it as it moves toward national scale, because the interesting problems start after launch.
Sitting on a compliance problem like this?
The same approach applies to emissions, ESG disclosure and any high-stakes document review, for Japan's GX economy and Europe's Green Deal alike.
Talk it through with us →