Expert human review of AI-assisted sustainability work.
AI is accelerating sustainability reporting. We make sure what it produces is accurate, defensible, and aligned with the standards and frameworks your stakeholders expect — and ready for assurance — before it’s published or submitted.
WHY DOES AI-ASSISTED SUSTAINABILITY CONTENT NEED EXPERT REVIEW?
AI can accelerate sustainability reporting, but fluent output is not always reliable. Expert review helps ensure every figure, claim, citation, and framework reference is accurate, defensible, and aligned with the evidence.
Data accuracy
Catch outdated figures, mislabeled metrics, and inconsistencies before publication.
Source integrity
Verify citations, quotations, and references to ensure every source is real and traceable.
Standards alignment
Confirm reporting requirements are interpreted and applied correctly.
Claim defensibility
Keep commitments and performance statements within what the evidence can support.
Stakeholder confidence
Reduce credibility risk with investors, raters, regulators, and other stakeholders.
The cautionary tale.
In 2025, a Big Four accounting firm was forced to partially refund a government client after an AI-assisted report it had delivered as independent, expert-checked work was found to contain fabricated academic references and a made-up quote from a court judgment. The report was part of a contract worth roughly US$290,000. The takeaway isn’t “don’t use AI” — it’s that AI-assisted work needs an expert check before it goes out the door.
Why work with G&A
In sustainability, a fluent error isn’t a typo. It’s a number that doesn’t reconcile, a claim you can’t defend, and a credibility problem with investors, raters, and regulators. That’s the gap an expert review closes.
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We don’t provide the assurance — we get your AI-assisted work ready for it. A clean pre-assurance review means fewer surprises and a faster, smoother process when it reaches your assurance provider.
We check sustainability claims, data, and evidence against the standards and frameworks that matter — GRI, SASB, ISSB, ESRS, TCFD. AI tools — even the ones now built specifically for sustainability — can flag patterns and inconsistencies, but they can’t weigh whether a Scope 3 figure is actually defensible, judge whether a claim will read as overstatement, or stand behind the answer. That judgment, and that accountability, is what we add.
Informed by 20 years of disclosure advisory and 15 years of S&P 500 / Russell 1000 reporting research — we know what “good” looks like.
We’ve spent two decades researching, writing, and advising on the disclosures we’re now reviewing — so we check your AI’s work not against a generic checklist, but the way the people who receive it will: investors, raters, customers, and regulators.
How the review works
Scope the AI use. What AI was used for, where the output will appear, and who the audience is.
Collect the inputs. The AI output, prompts where available, source documents, data tables, prior reports, and relevant standards and framework requirements.
Review content and evidence. Accuracy, consistency, standards and framework alignment, source traceability, and communication risk.
Trace and score. Map each material claim to an approved source (Evidence Traceability Matrix) and classify every finding by severity and type (Risk Scorecard).
Deliver and install controls. Redlines, an issue log, prioritized recommendations, and repeatable review gates for your next AI-assisted cycle.
A Deeper Dive into Our Process
Why review AI-assisted work
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Catch what a fast read misses. Hallucinated figures, invented citations, mislabeled metrics, stale numbers carried forward, and claims that overstate the evidence — the errors AI produces confidently and fluently.
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Defensibility. Every material claim traced back to an approved source, so the work stands up to scrutiny from raters, investors, and regulators.
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Assurability. Clean, sourced, consistent disclosures move faster and more cheaply through your assurance process — fewer findings, fewer surprises, less back-and-forth.
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Quality controls that stick. Repeatable review gates and an approved-use approach so your next AI-assisted cycle starts stronger, not from scratch.
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Confident speed. Keep the efficiency of AI without carrying the risk.
Ways to engage
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AI Output Quality Check: For teams using AI casually for early drafts and summaries. A focused review of selected work product: redlines, an issue log, a source spot-check, and top remediation recommendations.
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AI-Assisted Disclosure Review: For teams using AI for reporting, disclosure, ratings responses, or investor materials. The full engagement: AI-use intake, complete work-product review, Evidence Traceability Matrix, standards and framework-alignment and claim-substantiation review, greenwashing and credibility assessment, and a final QA memo.
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Managed AI Review & Controls: For teams using AI in every reporting cycle. An ongoing engagement: recurring reviews across cycles, plus human-in-the-loop controls, an approved-use policy, and reviewer checklists — so quality holds as your team scales its AI use and the standards evolve. We maintain the review capability with you, cycle after cycle.
What a finding looks like
A single review typically surfaces dozens of findings. Here is one, anonymized:
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Claim reviewed: “We reduced our carbon emissions by 32% since our baseline year.”
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What our review found: The disclosure doesn’t contain, anywhere, the context this claim needs — which scopes it covers (1, 2, and/or 3), the baseline year, the boundary, or whether prior years were restated. And on review, the number itself was wrong in three compounding ways: the 32% was actually the reduction in electricity use, not carbon emissions; it had been carried forward from a report two years old; and the current figure was 48%. One confident sentence — three material errors, none of them visible on a fast read.
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Risk: (High) Our recommendation: correct the metric and the current figure, add the scope, baseline year, and boundary, and reconcile every number against source data before publishing.
Understanding the Mission
An expert human review of sustainability content your team produced with AI — reports, framework mappings, ratings responses, investor and board materials — checked for accuracy, evidence, standards alignment, and credibility before it’s published or submitted.
AI accelerates the work but can mislabel a metric, carry a stale figure forward, invent a source, or misapply a standard — all in fluent, confident language that’s hard to catch on a fast read. Sustainability content carries higher stakes than generic copy: investor, regulatory, and reputational.
Factual accuracy, source traceability, standards and framework alignment, data consistency, claim substantiation, greenwashing risk, and whether the language is credible for investors, raters, and regulators.
GRI, SASB, ISSB / IFRS S1-S2, ESRS / CSRD, TCFD, TNFD, and stakeholder responses including CDP, S&P CSA, EcoVadis, and GRESB.
No — we’re the step before assurance. We review and strengthen your AI-assisted work so it’s ready for your assurance provider, which makes that process faster and smoother. We don’t provide the assurance ourselves.
Our review is human-led. Where we use AI internally to support it, client content is handled under strict confidentiality, and confidential or non-public information is never used in any way that breaches that confidentiality.
Send us a sample of the AI-assisted work you’re preparing, and we’ll scope the right level of review.
Let's start a conversation.
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July 1, 2025
