CUSTOMER STORY

How a Top 10 U.S. Construction Company Reported Energy, Water, Waste & Emissions by Job Site

In one year, GLYNT.AI took this construction leader from zero public sustainability disclosures to audited, job-site-level reporting across 1,000+ locations and 8 accounting systems.

Construction

150K Files Reviewed

99.5% Data Accuracy

Customer A leading US construction company operating 1,000+ job sites and 8 AP and ERP systems

Timeframe One year, from no public disclosures to audited public reporting

Product GLYNT.AI automated invoice processing and GHG Protocol-compliant reporting system

“We need to comply with expanding regulations and transform sustainability data into business wins. We could not have mounted an entirely new reporting system without GLYNT’s expertise and AI technology.”
Sustainability Director, Construction Company

What data challenges did this customer face?

This construction company needed a sustainability data system built for its industry and integrated with financial accounting at the job-site level.

Specifically, it needed to:

Build an entirely new reporting system, with no previous records or methods to build from
Capture granular cost and usage data to manage expenses by job site
Bring in the collaboration and expertise required to design a system this customized
Keep data ready to answer customer requests and pass customer site audits
Meet audit-ready data standards at Reasonable Assurance levels

How did GLYNT.AI solve these sustainability data challenges?

GLYNT.AI helped the customer locate the raw input files across the customer’s 8 AP and ERP systems, reviewed all 150,000 of them, and identified the 38,000 files relevant to sustainability. Those files were processed and integrated with business data and metadata for job-site-level reporting.

1. Built the historical record. GLYNT.AI processed three years of historical data in year one, creating a validated baseline.

2. Established an ongoing system. GLYNT.AI set up monthly reporting backed by an audit-ready approval workflow.

3. Delivered audit-ready data from day one. GLYNT.AI is independently audited annually at SOC 1 (Type II) and SOC 2 (Type II) levels, giving the customer a data system built on tested controls.

4. Positioned sustainability within a larger data strategy. The customer’s revenue and cost management already depend on sustainability data, and its upcoming AI initiative depends on that data being ready to use.

How was sustainability measured at the job site?

Sustainability data was built job site by job site, in six steps — from locating raw source files to publishing audit-ready reports.

Before GLYNT.AI, this company had never issued a public sustainability disclosure. Its internal reporting relied on samples, averages, and estimates, spread across eight disconnected AP and ERP systems with no way to verify the underlying data. In one year, that changed completely.

6 steps for sustainability reporting by construction job site

TaskDescription

Gather Primary Data Identify every source file tied to job site spending — utility bills, invoices, and usage records. When uncertain whether a file is relevant, include it for testing.

Test for Applicability Standard financial data isn't structured for sustainability reporting. Screen all candidate files to isolate the subset that contains usable sustainability data.

Process the Files Extract, aggregate, and harmonize the data into a consistent, comparable format.

Add Business Data and Metadata Attach job site detail and business context so the data can drive site-level decisions, not just compliance reporting.

Finalize the Sustainability Ledger Check for duplicates and gaps, and confirm the data matches defined reporting boundaries.

Publish Customized Reports Use the finalized ledger as the single source of truth for reporting, analytics, and AI applications.

Frequently asked questions

Learn more about how GLYNT.AI works for construction.

How do construction companies report sustainability by job site?

Construction companies report sustainability by job site by tying every utility bill, fuel invoice and usage record to the site it belongs to. GLYNT.AI finds those files across AP and ERP systems, processes them, and adds job site and business data, so energy, water, waste and emissions can be reported site by site.

Can GLYNT.AI unify data across multiple AP and ERP systems?

Yes. GLYNT.AI can handle the various system integrations, file formats and field names. A unified data model is built, and prepared data is ready for business use.

Do we need an internal developer team to implement GLYNT.AI?

No. GLYNT.AI is designed to build reporting capability without taxing limited internal developer resources.

Can GLYNT.AI process large volumes of historical data?

Yes. GLYNT.AI can review large volumes of historical invoice files using AI to identify which ones are relevant for sustainability reporting. For this customer, GLYNT.AI scanned over 150,000 files to find the 38,000 that were relevant for sustainability reporting.

Does GLYNT.AI align with GHG Protocol reporting standards?

Yes. GLYNT.AI helps establish GHG Protocol-compliant reporting boundaries before building out the underlying data.

Is GLYNT.AI's data audit-ready?

Yes. GLYNT.AI delivers audit-ready data with every update, prepared in a SOX-type compliant process, on a system independently audited at SOC 1 and SOC 2 Type II.

Can GLYNT.AI support data granularity down to the job site or business entity level?

Yes. GLYNT.AI can deliver sustainability data broken out by job site and business entity, not just company-wide totals.

Does GLYNT.AI fit into a broader data or digital transformation strategy?

Yes. GLYNT.AI’s sustainability data is built to integrate with other business data, including as part of a unified data architecture like a data lakehouse.

Ready to get detailed reporting that aligns sustainability and business?

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