What is GLYNT.AI’s Sustainability Data Layer?

In software, “data layer” refers to the technology that sits between incoming raw files and the finished, structured data that comes out the other side. GLYNT’s data layer is where the actual preparation work happens: data collection, extraction, testing, enrichment, before sustainability data reaches reporting tools, internal systems, or AI models.

A unified data layer runs on prepare once, audit once, report everywhere. The same clean dataset can feed every downstream report without being rebuilt each time.

No. Day-to-day, the system runs on its own, no special training is required to use it. During setup, we’ll ask your team the key questions: What do you use your sustainability data for? Which fields you need? Where does your raw input files comes from? GLYNT runs a kickoff workshop and a structured onboarding process to answer these questions. If you don’t know the answers upfront, we’ll add discovery exercises that help find them.

This depends on your current setup. Typically GLYNT and the customer collaborate during onboarding to tailor the GLYNT.AI system to your needs. For example, at one customer with a one-person sustainability team, GLYNT.AI handled all the integration and data mapping. For another customer with more complex IT systems, GLYNT worked with the customer’s IT team to specify the integrations and data flows.

These two different terms describe the GLYNT.AI data system from two angles. “Sustainability data layer” describes where it sits: the term comes from enterprise tech-stack architecture, the same way you’d describe a data layer or application layer in any system. “Finance-grade sustainability data” describes what it produces: sustainability data prepared with the same rigor as financial data, so you can trust it the way you trust your financial numbers.