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Service · ESG & sustainability data

ESG Data Scraping Services

Reported figures with page references, not a score we invented.

ESG data scraping is the automated extraction of published sustainability disclosures — reported emissions by scope, targets and baselines, policies, assurance statements and product-level sustainability claims — from corporate reports, regulatory filings and websites, with every figure traceable to the document and page it came from. We do not produce ESG ratings or scores.

An ESG score is somebody's opinion expressed as a number, and the methodology is usually proprietary. What your analysis actually needs is the reported figure, the page it came from, and whether anyone assured it. That is what we deliver.

Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

Page-level traceability No ratings or scores Free pilot sample in 48 hours
esg_disclosures_2026-08-05.jsonl LIVE FEED
{"company":"Example Industrials plc", "company_id":"aw-co-GB-88412", "metric":"ghg_scope_1", "period":"FY2025", "value":184200,"unit":"tCO2e", "reporting_standard":"GHG_Protocol", "boundary":"operational_control", "restated":true, "restated_from":171400, "assurance":{"present":true, "level":"limited","provider_named":true}, "source_doc":"annual-report-2025.pdf", "source_page":118, "source_table":"GHG emissions summary", "extraction_confidence":0.96, "published_at":"2026-03-24"} {"company_id":"aw-co-GB-88412", "metric":"target_net_zero", "target_year":2040,"baseline_year":2019, "scopes_covered":["1","2"], "scope_3_excluded":true, "third_party_validated":false}
2 of 418,200 company-metric-period rows · run 2026-08-05figure extraction 93.1% · schema v3.7
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Managed extraction of published ESG and sustainability disclosures from reports, filings and websites
Emissions
Reported Scope 1, 2 and 3 figures with units, standard, boundary and restatement tracking
Targets
Target years, baselines, scopes covered and whether third-party validated
Assurance
Whether a figure was assured, at what level, and whether the provider is named
Traceability
Document, page and table reference on every extracted figure
Claims
Product and corporate sustainability claims captured as worded, with dates
Explicit exclusion
No ESG ratings, scores or rankings — we extract disclosures, we do not judge them
Who it's for
Sustainability teams, investors, supply chain teams, regulators and researchers
93.1%figure extraction ratefrom PDF reports
Page-leveltraceability per figureauditable
Restatementstracked, not overwrittenprior values kept
Zeroratings or scores produceddisclosures only

Key takeaways

  • What it is: Managed extraction of published ESG and sustainability disclosures from reports, filings and websites
  • Emissions: Reported Scope 1, 2 and 3 figures with units, standard, boundary and restatement tracking
  • Targets: Target years, baselines, scopes covered and whether third-party validated
  • Assurance: Whether a figure was assured, at what level, and whether the provider is named
  • Traceability: Document, page and table reference on every extracted figure
  • Claims: Product and corporate sustainability claims captured as worded, with dates

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is ESG data scraping, and why do we refuse to produce scores?

ESG data scraping is the automated extraction of sustainability information that companies publish: greenhouse gas emissions by scope, energy and water use, waste figures, workforce and diversity data, governance disclosures, targets and baselines, assurance statements, and product-level sustainability claims.

The disclosures are public, usually in PDF reports, and almost entirely unstructured. Extracting them accurately with traceability is the work. Turning them into a score is not something we will do.

Why we do not produce ESG scores

  • A score is a judgement, not a measurement. It requires weighting incommensurable things — emissions against board diversity against water use — and every weighting is an opinion.
  • Methodologies disagree fundamentally. The same company routinely receives very different scores from different providers, which tells you the scores measure the methodology as much as the company.
  • Scores hide the inputs. A single number obscures whether emissions were assured, whether Scope 3 was included, and whether figures were restated.
  • Your analysis needs the inputs anyway. Serious ESG work rebuilds from disclosed figures. Buying a score and then requesting the underlying data is the common pattern, so we sell the underlying data.

Commercial ESG ratings exist and are licensed products. If you need them, licence them. We provide the disclosed figures, traceable to source, so your own methodology can be applied and defended.

The three things that make an emissions figure meaningful

  • Boundary. Operational control, financial control or equity share produce materially different totals for the same company.
  • Restatement. Companies restate prior figures, often substantially. A comparison against an unrestated prior year is not a comparison.
  • Assurance. A limited-assurance figure and an unassured one are different evidence. We capture presence, level and whether the provider is named.

All three are delivered as fields, because a tonnage figure without them cannot support any conclusion.

What we collect

Six categories of ESG disclosure data

Emissions extraction is the largest use case. Claims capture is the fastest growing, driven by greenwashing regulation.

Emissions disclosures

Reported figures with the qualifiers that matter.

  • Scope 1, 2 and 3 by category
  • Units and reporting standard
  • Consolidation boundary
  • Intensity metrics and denominators
  • Restatement tracking with prior values

Targets & commitments

What is promised, precisely.

  • Target year and baseline year
  • Scopes covered and exclusions
  • Interim milestone targets
  • Third-party validation status
  • Target revision detection

Assurance & verification

The evidential quality layer.

  • Assurance presence and level
  • Whether the provider is named
  • Which metrics are in assurance scope
  • Assurance standard referenced
  • Year-on-year assurance changes

Social & workforce

Reported people metrics.

  • Headcount and workforce composition
  • Diversity figures as reported
  • Safety metrics and incident rates
  • Pay gap disclosures where published
  • Training and turnover figures

Governance disclosures

Structure as published.

  • Board composition and independence
  • Committee structure
  • Executive remuneration linkage to ESG
  • Policy existence and publication dates
  • Governance disclosure changes

Product & marketing claims

Where greenwashing risk sits.

  • Product sustainability claims as worded
  • Certification and label references
  • Recycled content claims
  • Carbon neutral and offset claims
  • Claim wording change detection
Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

  • Document, page and table reference on every extracted figure
  • Restated and original figures kept as separate records, not overwritten
  • Units retained exactly as published, with conversion factors supplied separately
  • Assurance presence, level and scope captured as evidential quality signals
  • Scope 3 category coverage recorded rather than summed into one total
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • ESG ratings, scores or rankings of any kind
  • Verification of whether disclosed figures are accurate
  • Assessment of whether a sustainability claim is substantiated
  • Estimated figures for companies that publish nothing
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

ESG data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 130+ metrics and is agreed during scoping.

Deliverable schema — v3.7 core fields (full dictionary: 130+ metrics)
Field Type What it captures Refresh
company / company_id string Company as published and normalised identity for longitudinal joins Per report
metric / period enum / string Normalised metric identifier and the reporting period it covers Per report
value / unit decimal / string Reported figure and its unit exactly as published, not converted silently Per report
reporting_standard / boundary enum Standard followed and consolidation boundary, both of which change the total Per report
restated / restated_from boolean / decimal Whether the figure restates a prior disclosure, and the prior value Per report
assurance object Presence, level, whether the provider is named and which metrics were in scope Per report
target_year / baseline_year / scopes_covered int / array Target structure including which scopes are and are not covered Per report
third_party_validated boolean Whether a target is externally validated, where the company states it Per report
source_doc / source_page / source_table string / int Exact provenance for every figure, so any number is auditable Per report
extraction_confidence decimal Confidence in the extraction, with low-confidence figures flagged not dropped Per report
claim_text / claim_first_seen string / date Sustainability claims as worded, with first-observed date for change tracking Weekly

Units are retained exactly as published rather than silently converted. Where conversion is needed we supply factors separately, because a silent unit conversion is the most common way emissions comparisons break.

Coverage

Sources and disclosure regimes we collect from

Disclosure requirements differ by jurisdiction and are changing. Coverage is built to your company universe.

Annual reportsStandalone sustainability reportsCSRD-aligned disclosuresISSB-aligned disclosuresTCFD-aligned reportingGRI-referenced reportsSEC filings with climate disclosureRegulatory filing portalsCorporate ESG webpagesInvestor presentation ESG sectionsSupplier codes of conductModern slavery statementsProduct sustainability pagesCertification body public registersPublic target validation registersAssurance statements

We extract what companies publish. We do not verify whether disclosed figures are accurate, and we do not assess whether a claim is substantiated — both are assurance functions, not extraction functions. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market Why demand concentrates here
European Union & United Kingdom CSRD-aligned reporting created the deepest structured disclosure base, which makes extraction most complete here.
United States Climate disclosure in filings plus voluntary sustainability reporting, with substantial variation in boundary and assurance practice.
Japan & Australia Strong ISSB and TCFD-aligned reporting adoption with good assurance disclosure.
India & GCC Rapidly expanding mandatory sustainability reporting with growing disclosure depth year on year.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →

Who buys this data

Which teams buy ESG disclosure data

Sustainability and investor teams dominate, with supply chain and regulatory analysts growing.

Head of Sustainability

Large corporates
The problem

Peer benchmarking requires comparable disclosed figures, and reports publish them inconsistently across boundaries and standards.

What we deliver

Peer emissions and target data with boundary, standard, restatement and assurance captured, traceable to source pages.

Metric that moves

Peer benchmark quality

ESG Analyst

Asset managers and owners
The problem

Ratings disagree and hide their inputs, so you need the disclosed figures to apply your own methodology.

What we deliver

Reported figures with full qualifiers and page-level traceability across your investment universe, with no score applied.

Metric that moves

Methodology defensibility

Supply Chain Sustainability Lead

Corporates
The problem

Supplier emissions and target claims need collecting from hundreds of supplier reports, manually today.

What we deliver

Supplier-level emissions, targets and assurance status extracted from their published reports, refreshed as reports publish.

Metric that moves

Scope 3 data coverage

Regulatory / Compliance Analyst

Regulators and consumer bodies
The problem

Assessing claim substantiation across a market requires collecting claims as worded, with dates.

What we deliver

Product and corporate sustainability claims captured verbatim with first-seen dates and change detection.

Metric that moves

Market surveillance coverage

Reporting Manager

Corporates
The problem

You need to know how peers disclose in order to decide your own disclosure boundaries and detail.

What we deliver

Peer disclosure practice comparison — which metrics, which boundaries, what assurance level, what is omitted.

Metric that moves

Disclosure completeness

Academic Researcher

Universities and institutes
The problem

ESG research needs reproducible, source-traceable disclosure data rather than vendor scores.

What we deliver

Documented extraction with page-level provenance and stated methodology, reproducible and auditable.

Metric that moves

Reproducibility

Use cases

How ESG disclosure data gets used

Four patterns, with the outcome each is judged on.

Peer emissions benchmarking with qualifiers intact

Reported emissions are extracted with unit, standard, boundary, restatement status and assurance level, so peer comparison holds rather than comparing figures computed on different bases.

Outcome: Benchmarking that survives scrutiny because every figure carries its qualifiers and page reference.

Target quality assessment

Target years, baselines, scopes covered and exclusions are captured, revealing which commitments exclude Scope 3 and which are externally validated.

Outcome: Target credibility assessed on structure rather than on headline year.

Supplier ESG data collection at scale

Supplier published reports are processed for emissions, targets and assurance status, building the Scope 3 supplier picture that manual collection cannot cover.

Outcome: Supplier ESG coverage expanded without proportional analyst headcount.

Claims monitoring for substantiation and greenwashing risk

Product and corporate sustainability claims are captured verbatim with first-seen dates and change detection, so claim evolution and quiet withdrawals are visible.

Outcome: Claim inventory with dated wording, which is what a substantiation review requires.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Asset manager · EU

Vendor ESG scores disagreed and hid their inputs

Situation

Two providers gave materially different scores for the same holdings, and neither exposed the disclosed figures behind them, blocking the firm's own methodology.

What we ran

Extraction of reported emissions, targets and assurance status with units, boundaries and page-level references, with no score applied.

Result

The firm applied its own methodology to disclosed figures and could evidence every input to its investment committee.

Corporate · UK

Peer benchmarking compared figures computed on different bases

Situation

Sustainability reporting benchmarked peer emissions without accounting for consolidation boundary or restatement, producing comparisons that did not hold.

What we ran

Extraction with boundary, reporting standard, restatement flags and prior values retained as separate records, plus assurance level.

Result

Benchmarking moved onto comparable bases, and several apparent peer advantages proved to be boundary differences.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

  • Real extraction from your actual sources
  • Returned inside two business days
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build ESG extraction in-house or hire it as a service?

PDF table extraction with page-level traceability across hundreds of reports is specialist, repetitive work.

In-house build vs self-serve tool vs Actowiz managed service
Consideration In-house scraping team Generic proxy / DIY tool Actowiz managed feed
Time to first usable data 6–12 weeks of engineering before anything is trustworthy Days, but output needs manual cleanup before use Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes Your engineers, at the cost of their roadmap You do — tools report failures, they don't resolve them We do, same business day, inside the retainer
Data quality assurance Whatever your team has time to build None beyond HTTP success Schema validation plus sampled human QA on every run
Compliance documentation Rarely produced, then requested urgently by legal Not provided; terms risk sits with you Sources, method and lawful basis documented for review
Accountability Distributed across a team with other priorities A support ticket queue A named engineer and an account owner
True annual cost Engineer salaries, proxies, hosting, ongoing maintenance Low licence fee plus significant hidden analyst time One fixed monthly retainer, quoted after scoping

Why restatements break more ESG analysis than any other issue

Emissions restatement is routine, frequently substantial, and the single most common cause of broken ESG trend analysis. It also receives almost no attention in most datasets.

Why companies restate

  • Boundary changes. Acquisitions, disposals and changes in consolidation approach alter the reporting perimeter.
  • Methodology improvements. Better activity data or updated emission factors change historical figures.
  • Error correction. Straightforward mistakes discovered in later cycles.
  • Standard changes. Adopting a new standard can require recalculating prior years.
  • Baseline rebasing. Target baselines are recalculated, which changes the reduction percentage claimed against them.

What goes wrong without restatement tracking

A dataset that overwrites prior values with restated ones shows a smooth trend that never existed. A dataset that keeps only originally reported values compares figures computed on different bases. Both produce confident, wrong reduction percentages.

How we handle it

Every figure carries restated and, where identifiable, restated_from with the prior value. We keep both the original and the restated disclosure as separate records with their own source references, so you can construct either an as-reported series or a restated series — and see where they diverge.

That divergence is itself informative. A company restating substantially in the same year it announces a reduction is worth a closer look, and only restatement tracking makes that visible.

Extracting figures from PDF reports, and why the page reference is the point

Sustainability disclosures live in PDF reports running to hundreds of pages, with figures in tables, footnotes and narrative text. Extraction is the technical core of this service, and traceability is what makes the output usable.

Why extraction is hard here

  • Table structures vary wildly. Multi-level headers, merged cells, footnote markers inside values, and figures split across pages.
  • Units are inconsistent. tCO2e, ktCO2e, MtCO2e and occasionally imperial units, sometimes within one report.
  • Scope 3 categories differ. Companies report different subsets of the fifteen categories, with different labels.
  • Qualifiers sit in footnotes. Boundary, exclusions and assurance scope are often disclosed only in small print beneath a table.
  • Narrative figures. Some numbers appear only in prose, not in any table.

Why we attach document, page and table

ESG figures get challenged — by investors, auditors, regulators and journalists. A tonnage number without provenance is an assertion. The same number with a document name, page and table caption is evidence someone can verify in thirty seconds.

Our extraction confidence runs at about 93%, and low-confidence figures are flagged rather than dropped, so an analyst can check the specific page rather than discovering a gap. Units are retained exactly as published, with conversion factors supplied separately, because silent unit conversion is the most common way emissions comparisons break.

For monitoring controversies and reported incidents alongside disclosures, this pairs with our news data service, which uses the same company entity resolution.

How it works

How an ESG data engagement goes live in 5 to 10 business days

Company universe and metric scope are agreed first, since report count and metric breadth drive extraction effort.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We extract from publicly published corporate reports, regulatory filings and company websites. We do not verify whether disclosed figures are accurate, assess whether claims are substantiated, or produce ESG ratings, scores or rankings. Every extracted figure carries a document and page reference for independent verification.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment What we hold ourselves to
Pilot turnaround A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live Production collection running within 5–10 business days of sign-off.
Delivery punctuality 99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation A named engineer and an account owner, not a shared ticket queue.
Change requests Field additions and source changes handled inside the retainer, not re-quoted.
Exit Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Consolidation boundary
Whether emissions are reported on an operational control, financial control or equity share basis. The same company produces materially different totals under each, so comparison without boundary is invalid.
Restatement
A revision of a previously reported figure, common in emissions reporting. Overwriting prior values produces a smooth trend that never existed; we keep both as separate records.
Assurance level
Whether a disclosed figure received limited or reasonable assurance, and whether the provider is named. It is the evidential quality signal available from public disclosure.
FAQ

ESG data scraping: frequently asked questions

What sustainability, investment and compliance teams ask during evaluation.

No, deliberately. A score requires weighting incommensurable things — emissions against board diversity against water use — and every weighting is an opinion rather than a measurement. The same company routinely gets very different scores from different providers, which tells you the score measures the methodology as much as the company.

We extract the disclosed figures with their qualifiers and page references so you can apply and defend your own methodology. Commercial ratings exist as licensed products; if you need them, licence them.

Because overwriting prior values with restated ones produces a smooth trend that never existed, and keeping only originals compares figures computed on different bases. Both produce confident, wrong reduction percentages.

We keep both as separate records with their own source references and a restated_from value where identifiable, so you can build an as-reported or restated series and see where they diverge. That divergence is often the interesting finding.

About 93%, with low-confidence figures flagged rather than dropped so an analyst can check the specific page. Every figure carries document, page and table caption references.

Extraction is genuinely hard here: multi-level table headers, footnote markers inside values, figures split across pages, inconsistent units within a single report, and qualifiers disclosed only in small print beneath tables. We report the rate rather than a rounded claim because the residual is where the analyst effort goes.

No. Verification is an assurance function requiring access to underlying company data, which we do not have and would not claim to. We extract what is published, exactly as published.

What we do capture is whether a figure was assured, at what level, whether the provider is named, and which metrics were in assurance scope. That is the evidential quality signal available from public disclosure, and it is more useful than an unverifiable accuracy claim from us.

We capture claims as worded with first-seen dates and change detection, including quiet withdrawals and rewordings. We do not assess whether a claim is substantiated.

Substantiation requires evidence about the underlying activity, which is not in the claim. What we provide is a dated claim inventory — exactly what a substantiation review or regulatory assessment starts from. Concluding on substantiation is your assessment or a regulator's, not our pipeline's.

By capturing which of the fifteen categories a company reports, with their labels as published, rather than forcing them into a single total. Companies report different subsets with different labels, and summing incomparable subsets produces a meaningless figure.

Where a company reports a Scope 3 total without category breakdown, we record that and flag the absence. A total covering four categories is not comparable to one covering eleven, and the field structure makes that visible.

Yes, where suppliers publish. Coverage is thinner because smaller and private companies disclose less, and some publish nothing at all.

We give you a realistic coverage estimate for your supplier list during scoping rather than promising universal coverage. For suppliers that publish nothing, no extraction service can help — that gap needs a supplier engagement programme, not a data vendor.

Report-driven rather than on a fixed cadence, since sustainability reports publish annually with some interim disclosures. We monitor for new report publication and process on release.

Claims monitoring on websites and product pages runs weekly, because claim wording changes without any report cycle and quiet withdrawals happen between reports.

We quote individually. The drivers are company universe size, metric breadth, whether claims monitoring is included, and how much of the extraction requires PDF table work versus structured filings.

A focused peer group with core emissions and target metrics sits at the lighter end. Large universes with broad metric coverage and continuous claims monitoring sits higher. One scoping call, a free pilot on your own company list within 48 hours, then a fixed quote. Request a quote.

See real ESG extraction for your own company list

Send us companies and metrics. We return extracted figures with units, boundaries, assurance status and page references within 48 hours.

Free pilot, no card, no obligation. No scores, no ratings — disclosures with provenance.
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Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
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270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
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380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
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We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

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Amazon
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Zillow
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Latest Insights & Resources

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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

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Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

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Transparent plans from $500/mo. Find the right fit for your budget and scale.
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Tell us what data you need — we'll scope it for free and share a sample within hours.
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    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
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Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

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Free 500-row sample · No credit card · Response within 2 hours