As carbon offset projects have never been more closely scrutinised, verification timelines have stretched to years. The resulting backlog with auditors is now one of the biggest brakes on growth. Can Artificial Intelligence tackle this problem from the inside out?
Following intense scrutiny over quality and integrity in recent years, standards and Verification & Validation Bodies (VVBs) have rightfully increased their rigour. But this necessary step has created a new bottleneck: time.
The problem is a paradox and has left every player in the value chain frustrated: how can the market increase quality and scale at the same time?
For project developers, this bottleneck is an existential threat. “We often operate for years without revenue,” one developer told us. “How can we expect the market to grow when certification takes years? Every week counts, and these delays mean some good projects simply cease operations.”
For auditors on the front lines, the pressure comes from all sides. “We’ve made the system so complex that it’s not helping anybody,” says Kaviraj Singh, CEO of Earthood, one of the world’s largest VVBs. “We keep adding requirements, and the end result is the same: everybody is complaining about everybody.”
This complexity isn’t just about dense methodologies. For auditors, the problem starts with the raw material they work with. “We see just broad inconsistency with how suppliers produce data,” says a partner at at another VVB, a leading verifier in the carbon removal space. “It’s kind of the wild west.”
The strain of this dual challenge, complex rules and chaotic data, is showing up across the value chain. The burden of ensuring quality is falling back on the standards themselves. “We’re finding we’re spending most of our time having to do really deep dive reviews because the VVBs are missing a lot of stuff,” one standard’s CEO said candidly. “We’re getting to the point where we’re thinking, what’s the point of the VVBs?”
This shared frustration has crystallised a clear ambition. As Singh puts it: “If I am doing ten projects today, I want to do twenty tomorrow with the same resources, improving my quality… using technology.”
Automating the “audit trail”
This is the challenge Genvision, a startup in the digital verification space, was built to solve. Genvision is tackling the bottleneck with an AI-powered validation and compliance engine designed to act as a co-pilot for developers, auditors, and standards.
“The question that keeps us awake at night is: how do we keep or even increase quality, but also make the process faster, not slower?” said Emiel Cockx, founder of Genvision. “This is feedback we’ve received from everyone. They are all under immense pressure to be more rigorous and more efficient.”
Genvision’s tool ingests all project documentation—PDDs, monitoring reports, and calculation spreadsheets—and automatically cross-references them against the methodology rules. “ It’s finding inconsistencies where a project document says one thing, but an underlying calculation sheet says another. These are the rabbit holes that consume days or weeks of an auditor’s time.”
Genvision admits many players in the space are still skeptical of the added value an AI compliance tool can bring to their workflow and untrusting of its output. However, they point out that their AI engine is not a “black box.” It provides full traceability for every finding, linking it directly to the source document and methodology clause, allowing the human expert to retain full authority.
Bridging the gap between quality and scale
Genvision argues this “augment, not replace” approach is the only way to break the paradox.
“We’re taking the 80% of repetitive, manual checklist work off their plate,” Emiel adds. “This allows a senior auditor to focus their valuable time on the 20% of complex, nuanced issues that truly deserve their judgement—like assessing stakeholder engagement or the plausibility of a baseline.”
The result, Genvision claims, is a double win: the AI provides a comprehensive, fully-documented compliance check that increases integrity, while the human expert can complete their work in a fraction of the time.
“We can’t expect this market to scale to the trillions needed if projects are stuck in review,” concludes Emiel. “Using AI for compliance is a great way to bridge the gap between the quality we demand and the scale our planet needs.” As confidence builds, today’s compliance constraints start to look like a place where technology can finally unlock integrity at scale.
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