The Tech Stack for Sustainability Data

Data Aggregation & Harmonization

With such varied Source Files, the data extracted feels like apples vs oranges. GLYNT.AI developed a standardized data schema to aggregate and harmonize the extracted data, across the globe. This accounts for variations in data layouts and names, and also different energy and water billing systems, such as estimated billing with true-ups, billing in advance, and consumption-based billing. GLYNT.AI brings together years of experience, and expertise in sustainability, finance and data for a robust outcome.

Data Management and Change Management Reporting

As sustainability teams move from annual reporting to building the business case for change, they must manage a much larger volume of data. Reporting on data handling (receipt logs, exception reports, chain of custody and so on) is key, as using consistent data sets year over year leads to much more reliable data. In a change management system is needed. Nothing stays the same in sustainability – sites change, vendors change, data sources change, data formats change – and all of this needs careful reporting. As a complete system, GLYNT.AI provides the reporting that sustainability managers, IT and finance needs to manage and verify data flows.

Seamless Data Flows into Software & Systems

At a customer’s request, GLYNT.AI will automatically load the sustainability data prepared by GLYNT.Ai into their preferred systems. Many customers also receive CSV files, which they store separately for internal data management. As noted, there is a constant pace of change in sustainability, so a key part of the GLYNT.AI-to-software coordination includes new assets, data overwrites, and customized fields that open up audit-trail tracking.

Purpose-Built AI

As mentioned above, GLYNT.AI built a ‘Few Shot’ machine learning (ML) system to extract data from incoming source files. Few Shot is the exact opposite of LLMs in that Few Shot needs just a 3 training samples, while LLMs need the entire internet for training data, and Few Shot uses a quickly built, highly-efficient ML model for the data source at hand, and LLMs require huge algorithms that only the richest of companies can afford to build and improve.

GLYNT.AI built our Few Shot system for rapid onboarding and low-cost operations. Then we add the steps before and after the Few Shot module in our complete data preparation pipeline.

AI-First, Not AI-Assisted

There’s a natural evolution to the adoption of AI in any company. First are the experiments by data scientists, offline to production systems, and suggestive of a path forward. Then there are initial prototypes. But as no one trusts the AI, its role is limited. Third is the adoption of production-grade AI to assist and support humans. This is like a self-driving car with a human at the wheel, or using AI copilot tools to write code. The system is largely unchanged.

AI-First flips this mentality on its head. It puts humans in service of the AI, with every step in the system and every task re-thought and re-constructed, in service of higher efficiency, high accuracy and high scalability. GLYNT.AI has re-optimized our data pipeline to automatically files to feed the ML (fingerprinting and routing) and to harvest any human support into more AI-powered, automated services. The result is a constantly improving system.

AI-First goes beyond using AI as a tool, it is re-thinking the basic structure. For GLYNT.AI, it often means pushing our AI thinking to the front of the data pipeline, such as in onboarding.

AI-Powered Migration and Onboarding

GLYNT.AI has harnessed its powerful AI to reduce the effort and cost of migrating from a current data preparation system or building a data flow from scratch.

[Note: If your company has not reported before, or wants a fresh start on sustainability data preparation, use GLYNT.AI’s Automated Discovery services. We know where to look for Source Files, and minimize the effort on your part, or that of your IT team. Our AI will sift and sort incoming files so you don’t have to.]

Migration and onboarding projects begin with a Kickoff week, in which GLYNT.AI learns from the data you’ve already prepared. Our system learns the rules your team is using, including which fields to capture, and how data is transformed and/or enriched. The Rulebook, a visual guide that keeps everyone on the same page, records all of the rules. If you haven’t prepared data before, we’ll provide you with our standard data set to get things rolling.

With rules in hand, GLYNT.AI and customers work through the remainder of the migration projects in 2-week cycles called Data Waves. Once a Data Wave is complete, it can go live with weekly, monthly or quarterly updates from those sites and vendors.

In short, GLYNT.AI uses AI to remove burdensome tasks, such as setup files, OCR template creation, low-code flow setups and so on. Focus on what humans do best, let the AI do the grunt work.

Energy & Water-Efficient AI

GLYNT.AI’s purpose-built Few Shot ML not only solves a huge sustainability data challenge – the processing of hugely varied and constantly changing Source Files – it is also energy and water efficient when compared to the energy and water consumption of LLMs for the same tasks.

Few Shot ML results uses a library of tiny, tiny training models. Each model can be trained in minutes and needs just three training samples. The model returns data at greater than 98% accuracy rates from the start and so very little retraining is needed. This hyper-efficient use of computing resources leads to energy and water usage that is less than 5% that of LLMs, and less than 20% of the emissions from standard AI tools from cloud providers for the same task.