Why the right AI for sustainability data isn’t the AI everyone’s raving about…
But when it came to building the GLYNT.AI system for sustainability data? We went a different direction entirely — and built on geometric AI instead.
Why Did We Build Something Different from LLMs?
Our system runs on what’s called “geometric AI” — a type of non-LLM machine learning also known as few-shot learning. It’s purpose-built for one thing: preparing sustainability data with the same rigor you’d expect from financial data.
That means we can guarantee 99.5% accuracy. And we routinely beat that target.
Could you use an LLM to read a legal contract and flag problematic clauses? Absolutely — that’s exactly what LLMs are great at. But for structured, auditable sustainability data at scale? A purpose-built system wins every time.
How Does GLYNT.AI Reduce Emissions and Control Costs?
1. Lower Emissions
Instead of one massive model that requires enormous amounts of computing power to train, GLYNT.AI uses a library of tiny, specialized models. When a file comes into our system, it’s automatically routed to the right model — a technique called model forwarding. The result is that our emissions are less than 5% of what an LLM would produce for the same task.
For sustainability teams, that’s not a footnote. That’s the whole point.
2. Stable Budgets
Here’s something we’re hearing more and more: companies are burning through their entire 2026 AI budgets in just the first few months of the year. With LLMs, costs are measured in tokens — essentially, pieces of words, numbers, and punctuation. And while cost per token has come down, the number of tokens used per project keeps climbing. It’s too easy to add just one more prompt, and suddenly you’re way over budget.
GLYNT’s costs don’t work that way. Because we’re not token-based, your budget is predictable. And with a 4-month payback, that’s predictable savings too.
A Quick Comparison: LLMs vs GLYNT.AI
| Task in Sustainability Data Preparation | Description | LLMs | GLYNT.AI |
|---|---|---|---|
| Data Extraction — Emissions | OCR + AI to capture and organize data from a file | 100* | 5* |
| Data Extraction — Accuracy | OCR + AI to capture and organize data from a file | 65%** | 99.5% |
| Risk of Unreliable Cost | AI projects that expand token use, blowing corporate budgets | High risk | No risk |
| Repeatability | Get the same results time and time again | High risk | No risk |
| Data Quality Assurance (QA) | Build a data QA system to check the outputs | Must do | Built-in, non-AI code |
| Data Enrichment | Add business data and metadata for improved business outcomes | More tokens, more time | Built-in, non-AI code |
| Compliance and Audits | Deliver data with complete data lineage and verifications | More tokens, more time | Built-in, non-AI code |
*LLM emissions for the task are indexed to 100. See the GLYNT.AI Guide to AI with Lower Emissions for details.
**Source: average accuracy rate of the top 10 LLMs scored at ocrarena.ai. Accuracy across those 10 ranges from 39% to 76%.
Which AI is Right for Sustainability Data?
Use LLMs for what they’re great at: one-off research, writing narratives, and open-ended exploration. Use GLYNT.AI and its geometric AI for what it’s built for: accurate, auditable, cost-stable sustainability data preparation.
Sustainability teams have a real opportunity here — to lead the way in using the right AI for the right job. That’s not just smart. For teams focused on environmental impact, it’s the only way to go.
Curious how GLYNT.AI fits your data workflow? Talk to GLYNT.AI
