ESG.01
CSRD Reporting
ESRS datapoint mapping, evidence extraction and narrative drafts for CSRD statements, with the datapoint list kept configurable as the Omnibus simplification changes it. Materiality stays yours.
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ESG reporting,
with the evidence attached.
Every figure traceable to its source. Sustainability data gathered from invoices, meters, ERP and suppliers, mapped to ESRS and the GHG Protocol, with every figure traceable to its source. Your team owns the disclosure; your auditor owns the assurance.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI for ESG reporting means using language models and data pipelines to collect sustainability evidence from documents and systems, map it to a reporting standard such as ESRS, and estimate emissions with the method and source of every figure recorded. People decide what is material and what is disclosed; the auditor gives the assurance.
Where it breaks today
01
The data is in PDFs and inboxes
Utility bills, fuel cards, travel bookings, supplier questionnaires and policy documents. Most sustainability data arrives unstructured and is retyped into spreadsheets by hand.
02
Scope 3 is mostly estimates
Value-chain emissions depend on suppliers who do not answer, spend data coded for finance rather than carbon, and emission factors that change between years.
03
Nobody can show the working
When the assurance provider asks where a number came from, the answer is a chain of spreadsheets and one person’s memory. That is the point at which reporting stalls.
OUR APPROACH
AI for sustainability reporting that an auditor can follow
The rules have been moving. The EU’s Omnibus simplification has narrowed which companies the Corporate Sustainability Reporting Directive (CSRD) applies to and is reducing the European Sustainability Reporting Standards (ESRS) datapoints, while ISSB standards, the GHG Protocol and customer questionnaires keep asking for much of the same data. Whether and when you are in scope is a question for your advisers. What does not change is that every reported figure needs a source, a method and an owner.
That is what we build: the pipeline that reads documents and system exports, maps each datapoint to the standard you report against, records which emission factor and which assumption produced each number, and keeps that record intact from first invoice to final disclosure. Generative AI drafts narrative sections from the underlying data, for your team to edit. It does not decide what is material, and it does not sign anything.
HOW IT DEPLOYS
PHASE 01
Map the datapoints
We take your materiality outcome and list each required datapoint, its current source, its owner and how it is calculated today.
PHASE 02
Connect the sources
ERP (SAP, Oracle, Dynamics), utility and fuel data, travel platforms and supplier responses feed one store, with documents parsed and linked to the figures they support.
PHASE 03
Hand over the trail
The output goes to the reporting tool you already use, such as Workiva or a sustainability platform, with the evidence trail your assurance provider will sample.
What we build
01
CSRD and ESRS data collection
Datapoint mapping, document extraction and narrative drafts for the ESRS topics your materiality assessment selected, with gaps listed rather than guessed.
SEE CSRD REPORTING →02
Scope 3 emissions estimation
Spend classified into GHG Protocol categories, supplier-specific data used where it exists, and each estimate labelled with its method and factor source.
SEE SUPPLY-CHAIN EMISSIONS →03
ESG evidence trails
Lineage from each reported figure back to the source document, the transformation applied and the person who approved it, ready for limited assurance sampling.
SEE EVIDENCE TRAILS →04
Secure data pipelines
The ingestion layer under all of it: validated, versioned and logged, so a figure can be reproduced from the same inputs a year later.
SEE SECURE PIPELINES →Controls we implement
SOURCE LINKED TO EVERY FIGURE
METHOD AND FACTOR RECORDED
VERSIONED INPUTS
NAMED APPROVER PER DATAPOINT
NO TRAINING ON YOUR DATA
These are engineering controls we build into the architecture, not certifications. QQuantum.ai provides no assurance opinion; the disclosure is your organisation’s and the assurance is your auditor’s — see our trust centre.
Climate and esg use cases
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ESG.02
Supply-Chain Emissions
Scope 3 estimated from spend, activity and supplier data across GHG Protocol categories, each figure labelled with its data tier and emission factor source so estimates never pass as measurements.
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ESG.03
ESG Evidence Trails
Lineage, change logs and named approvals from every reported sustainability figure back to its source, so an assurance provider can sample it. The assurance opinion stays the auditor’s.
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Questions
Questions about Climate & ESG Reporting.
How is AI used in ESG reporting?
Mainly in three places. Reading unstructured evidence such as utility bills, supplier questionnaires and policies into structured data. Mapping that data to a framework such as ESRS, GRI or ISSB and spotting missing or inconsistent values. And drafting narrative disclosures from the underlying figures for a person to edit. The judgement calls, materiality above all, and the final disclosure stay with your team.
Can AI do carbon accounting?
AI can do much of the laborious part of carbon accounting: classifying spend into emission categories, reading activity data from documents, matching it to emission factors and flagging anomalies. The method still has to follow the GHG Protocol, and the choices within it (boundaries, factors, estimation approach) have to be made and documented by people. A model that produces a total without showing that working is not usable for reporting.
Does the EU Omnibus change CSRD reporting?
Yes. The Omnibus simplification narrowed which companies the CSRD applies to, changed the timetable and is reducing the number of ESRS datapoints. Because the detail depends on your size, structure and where you operate, we do not state thresholds or dates here; your advisers should confirm your position. The systems we build follow whichever datapoint list applies to you and can change with it.
Will an AI-built ESG report pass assurance?
Assurance is given by your auditor or assurance provider, not by software, and not by us. What a system can do is make the evidence easy to examine: each figure linked to its source, the calculation and factor recorded, and a named person who approved it. That is what an assurance provider samples, so a clear trail shortens the process; it does not decide the outcome.
Which systems does it connect to?
Whatever holds the data today: ERP systems such as SAP, Oracle or Microsoft Dynamics, utility portals, fuel-card and travel exports, HR systems for workforce metrics, and supplier responses. Output goes into the reporting or disclosure tool you already use, such as Workiva or a sustainability platform, rather than replacing it.
