OECD 2025 · ESRS · IFRS S2 · GHG Protocol · TNFD

ESG evidence,
ready for verification.

ESG ratings diverge because the underlying metrics diverge. We bridge that gap by turning operational data, documents, ERP records, compliance controls, and consultant inputs into a traceable evidence layer: 371 tagged ESG parameters mapped to source systems, standards, automation level, and report-ready audit trails.

Explore Environmental → Why ESG ratings disagree
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Environmental parameters
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Social parameters
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Governance parameters
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Standards mapped
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High-automation parameters in the evidence model

Services that feed the evidence layer

The value bridge journey

A guided path from fragmented ESG data to consultant-ready, auditable reporting evidence.

What we bridge

From raw operational reality to verified ESG reporting.

The value is not limited to energy monitoring. Energy is one high-confidence source, but the platform also supports compliance automation, policy and permit extraction, HR and supplier evidence, governance controls, standards mapping, exception review, and report pack generation.

01

Source capture

SCADA, IoT, digital twins, ERP, HRIS, procurement, maintenance systems, and approved manual inputs.

02

Compliance automation

AI document ingestion for permits, policies, certificates, supplier files, utility bills, contracts, board packs, and audit evidence.

03

Consultant workspace

Structured review queues, traceable assumptions, gaps, exceptions, evidence confidence, and standard-by-standard mapping.

04

Verified report pack

Auditable ESG datasets with source lineage, supporting files, calculation basis, and draft disclosures ready for assurance review.

Evidence journey — from source data to verified reporting
A service-flow view showing how energy, compliance, enterprise, and consultant inputs become auditable ESG outputs.
Walkthrough pages

The journey from ESG uncertainty to verified reporting.

Each section builds one layer of the narrative: market problem, evidence schema, source capture, automation coverage, and report-ready standards mapping.

01

Landscape

Why ESG ratings diverge and why source-level evidence matters.

 02

Schema Explorer

How many parameters are collected and the surface-level route used to collect them.

 03

Environmental

Energy, emissions, water, circularity, biodiversity, and environmental controls.

 04

Social

Workforce, value-chain workers, communities, consumers, and policy evidence.

 05

Governance

Business conduct, board, risk, tax, cyber, AI governance, and controls.

 06

Coverage

Automation footprint, consultant review needs, and auditable deliverables.

 07

Standards

The reporting frameworks supported by the parameter and evidence model.
Schema Explorer

What gets collected, and how

A category-level map of the ESG evidence schema: how many parameters are collected in each area and which collection routes support them before line-item evidence review.

SCADA

Operational telemetry and site systems

Twin

Computed metrics and scenarios

AI Docs

Compliance/document extraction

Enterprise

ERP, HR, finance, procurement, risk systems

Review

Consultant review, surveys, assumptions, sign-off

The Landscape

Why ESG ratings disagree

The OECD's 2025 study analysed ≈2,000 metrics from 8 leading rating providers covering >80% of the market. The headline: ratings diverge because metric selection diverges. The same topic can be measured with 1 metric by one provider and 113 by another.

Fig 1 · Metric coverage across 23 topics
Sunburst — slice size = share of total metrics. Governance dominates; Biodiversity, Taxation, Competition under-represented.
PurposeShows that ESG ratings do not weight topics evenly. This establishes why a transparent source taxonomy is needed before automation or reporting can be trusted.
Fig 2 · Coverage range across providers
Min/avg/max metrics per topic — Corporate Governance from 4 to 113 across 8 providers.
PurposeShows that rating providers can measure the same topic with very different metric depth. The bridge value is a reusable evidence base that can serve multiple rating and reporting demands.
Fig 3 · Metric characteristic mix
68% input-based (policies + activities). Only 30% outputs. 2% capture business environment.
PurposeShows why ESG cannot be solved by meters alone. Policies, activities, outputs, and business context all need structured capture and review.
Fig 4 · Policies / Activities / Outputs per topic
Stacked share — note Climate Resilience is 51% business-environment; DEI and Energy are output-heavy.
PurposeShows which topics depend on policy evidence, operational activity, measured outputs, or external context. This explains why the platform combines telemetry, documents, enterprise systems, and consultant review.
Fig 5 · Qualitative vs Quantitative
Overall 72% qualitative. Even GHG & Energy plateau at ~49% quantitative.
PurposeShows that report readiness requires both quantitative data and qualitative evidence. AI compliance ingestion and review workflows are as important as operational data capture.
Fig 8 · Two approaches per topic
Scatter: share quantitative (x) vs share output-based (y). Pollution / Energy / GHG sit top-right; Biodiversity / Climate Resilience bottom-left.
PurposeShows which topics are naturally automation-friendly and which need more interpretive evidence. This supports the automation-level split in the schema.
Fig 9 · Supply-chain metric availability
Only 7% of all metrics. GHG (Scope 3) carries most weight; over half the topics have ≤2 supply-chain metrics.
PurposeShows the supply-chain evidence gap. Supplier files, procurement data, product carbon footprints, freight records, and contracts become critical inputs for auditable ESG reporting.
Pillar · Environmental

The 265 environmental parameters we automate

Parameter distribution by category
Donut of E1-E7 weighting.
Capture method × automation level
How much of the environmental schema can be supported by each collection route?
Schema tree — Environmental
A visual map of the taxonomy. Use hover for details and the category cards below for clean drill-down.
Pillar · Social

Social parameters

Distribution across S1-S4
Schema tree — Social
A quiet structure map; detailed social evidence is opened through the category cards below.
Pillar · Governance

Governance parameters

Distribution across G1-G5
Schema tree — Governance
A quiet structure map; governance controls and evidence are opened through the category cards below.
Our Coverage

What we automate vs. what stays manual

Across the 371 tracked parameters, here's the automation profile of our digital-twin + SCADA + AI document ingestion + ERP-integration stack. The value is broader than energy: it creates a controlled evidence layer for compliance automation, consultant review, assurance readiness, and verified reporting.

Consultants get Source lineage

Every parameter keeps a path back to the originating system, document, calculation, or reviewer decision.

Compliance teams get Smart automation

Policies, permits, certificates, supplier evidence, controls, and exceptions are extracted and organized.

Auditors get Evidence packs

Supporting files, units, standards tags, confidence level, and review notes are preserved for assurance.

Leadership gets Report-ready data

Structured, defensible ESG outputs that can be used to generate verified disclosures and performance plans.

Automation level across the dataset
Capture method footprint
Heatmap — Category × Capture method (parameter counts)
Reference

Industry standards we map to

Every parameter in the schema is tagged to the disclosure standards that require it, so the same evidence base can support multiple reporting frameworks. Last verified May 2026.