AI carbon accounting that reads utility bills, fuel cards, travel data and supplier invoices, maps each line to a GHG Protocol category and emission factor, and shows its working. Your sustainability lead approves methods before numbers reach a report.
Sample: Category 1 emissions fall as spend-based estimates for the top 20 suppliers are replaced with supplier-specific product data. The wider band reflects remaining spend-based estimates.
AI carbon accounting is the use of document AI and machine learning to collect activity and spend data from invoices, utility bills, ERP ledgers and supplier responses, classify it into GHG Protocol scopes and categories, and apply emission factors. It outputs a greenhouse gas inventory in tonnes of CO2 equivalent, with each figure traceable to its source document, factor and method for review and assurance.
Scope 1 and 2 come from a few meters and bills. Scope 3 comes from thousands of suppliers, invoices and spend lines, usually compiled once a year in spreadsheets by one overstretched team.
Supply chain Scope 3 emissions were 26 times higher than operational emissions for companies disclosing to CDP in 2023.[1]
of corporates disclosing to CDP had set a Scope 3 target, and they were twice as likely to measure operational emissions as supply chain emissions.[1]
Scope 3 categories in the GHG Protocol, each with its own data sources and calculation methods.[4]
Data collected as invoices arrive, not once a year, so hotspots and supplier gaps show up while there is time to act.
Electricity, gas, water, district cooling and fuel data pulled from PDFs, portals and fuel cards across every site.
Ledger lines and invoices from SAP, NetSuite, Oracle or Coupa classified to Scope 3 categories and matched to factors.
One inventory with methods, factors and data quality by category, ready for your reporting tool or consultant.
Usually Scope 2 for all sites or Scope 3 Category 1 for your top spend. We agree boundaries and methods with your sustainability lead and one metric: hours to close the period or share of spend with activity data.
Twelve months of bills or ledger lines are extracted and classified. Low-confidence items go to a review queue; factors are proposed with references.
We compare results with your previous inventory or consultant's numbers and explain every material difference, line by line.
Approved methods and factors are versioned, the pipeline is scheduled monthly, and we report the metric plus a supplier-engagement priority list.
| Criterion | Spreadsheets and consultants | Carbon accounting SaaS platform | Stratgik build + run |
|---|---|---|---|
| Data collection | Manual, once a year | Integrations plus uploads your team manages | Automated extraction from bills, invoices and ERP ledgers each month |
| Scope 3 method | Mostly spend-based | Spend-based with supplier modules | Spend-based moving to supplier-specific, prioritised by data quality |
| Audit trail | Hard to reproduce | Good inside the platform | Line-level trail in your data warehouse, exportable to assurers |
| Messy source data | Absorbed by analyst time | Needs clean uploads | Built for scanned bills, mixed currencies and multi-entity ledgers |
| Reporting and dashboards | Consultant-built | Strong, ready-made | Feeds your existing platform or BI; not a replacement for good SaaS reporting |
| Best fit | First inventory, few sites | Standard data, strong internal team | Many sites, suppliers and ERPs; assurance coming |
Thresholds and timelines keep changing, but the direction is consistent: numbers need a method, a source and an audit trail. Building that data pipeline once serves every framework.
Estimate analyst time saved on extracting bills and classifying spend, plus supplier follow-up. The automation share is an assumption; the pilot measures it on a year of your data.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
One slice: Scope 1 and 2 for all sites, or Scope 3 Category 1 for top spend
Monthly data pipeline, factor updates and QA
Full Scope 3, supplier engagement and assurance support
Emission factor database licences, OCR pages, model usage and cloud hosting billed at cost. Stratgik supports data collection and calculation; applicability of CSRD, UK SRS, SB 253 or other rules and final disclosures are decided with your advisers and auditors. Taxes excluded. GBP and AED prices are indicative conversions from USD.
AI carbon accounting calculates Scope 3 by classifying spend and activity data into the 15 GHG Protocol categories and applying an emission factor to each line. It starts with spend-based factors where that is all you have, then replaces them with activity data or supplier-specific figures as they become available. Every calculation records the source document, factor, version and reviewer.
Extraction from bills and invoices can be measured precisely, and our pilot reports it on your own documents. The accuracy of the emissions figure itself depends mostly on method and emission factors: spend-based estimates are less precise than activity or supplier data. The GHG Protocol also notes supplier data is not automatically more accurate, so we score data quality by category.
It depends on size, location and listing status, and all three regimes have changed recently. California SB 253 covers entities with over $1 billion revenue doing business in the state, with Scope 3 from 2027. The EU Omnibus narrowed CSRD to companies over 1,000 employees and €450 million turnover. Confirm your position with advisers; we build the data either way.
Stratgik's pilot is a fixed $15,000 for one slice of your inventory, such as Scope 1 and 2 across sites or Scope 3 Category 1. Run is $3,000 a month for the monthly pipeline and factor updates. Scale for full Scope 3, supplier engagement and assurance support starts from $8,000 a month. Factor licences and usage are billed at cost.
Often yes. Platforms such as Watershed, Persefoni or Sphera are good at reporting and frameworks, and consultants add judgement on boundaries and targets. The bottleneck we solve is getting clean, traceable data out of bills, ledgers and supplier documents. Our pipeline can feed your existing platform or consultant rather than replacing them.
Yes. We configure extraction for UK, US and UAE utility formats, including DEWA, ADDC and district cooling bills, and handle Arabic and English documents. Invoices in AED, GBP, EUR and USD are normalised to one currency with dated exchange rates before spend-based factors are applied, and the conversion is stored in the audit trail.
We will return classified lines, proposed factors and a data-quality view, so you can see where your footprint estimate is weakest before reporting season.