Intelligence, built into every layer of sustainability
AI-Native Supply Chain Sustainability
Managing Scope 3 emissions, especially supply chain emissions, has historically been the hardest part of any sustainability program. There are typically 3 core issues:
Managing hundreds or thousands of suppliers
Inconsistent or unavailable suppliers’ emissions data
Vendor fatigue from multiple surveys and unclear expectations
This quarter, we re-architected the supply chain workflow to be intelligence-native by design, moving beyond outdated survey-based data collection.
We have one overarching goal:
For enterprise customers, you should not have to chase suppliers for sustainability data.
For suppliers, you should not have to keep responding to the same information requests across countless formats and platforms.
To facilitate that, our AI engine, SustainSense, operates at the core of ingestion, interpretation, and standardization of suppliers’ data.
Core Principle
Eliminate the burden of forced data exchange.
Replace rigid formats with intelligence at the core, bringing structure, flexibility, and clarity for every stakeholder.
Architectural Shift
Old Approach
Survey
→↓
Wait
→↓
Follow Up
→↓
No Response
→↓
Estimate
→↓
Validate
→↓
Analyse
New Approach
Ingest Supplier List
→↓
Find Data in SustainSense
→↓
Find Public Report
→↓
Extract Data
→↓
Analyse
SustainSense at the Core
Instead of starting with supplier outreach, the workflow now starts with intelligence.
Upload your supplier list.
The system resolves supplier identities across subsidiaries and naming variations.
It matches vendors against a live intelligence layer of 120,000+ companies and growing.
Public disclosures are automatically ingested, interpreted, and standardised.
If emissions are disclosed, they are extracted and normalised.
If not, calibrated supplier-specific models generate structured estimates.
No survey dependency at baseline.
What makes this possible is the intelligence embedded underneath. Here is a breakdown of the layers now built into the supply chain module:
1. Multimodal Document Understanding
This quarter, we expanded our core technology to meet data where it already exists. The platform can read, interpret, and structure sustainability information directly from source documents — completely eliminating the need for you to chase suppliers for data that likely already exists in another format, another template, or in fragmented disclosures.
01 — Ingestion Engine
Multi-Source, Multi-Format Intelligence
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Processes unstructured and semi-structured inputs — including PDFs, spreadsheets, scanned utility documents, sustainability reports, and web-sourced disclosures
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Applies proprietary document understanding models — to interpret tables, footnotes, contextual text, and embedded calculations
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Extracts both quantitative metrics and narrative signals from free-form disclosures
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Requires no predefined templates or formatting from suppliers
02 — Normalization Framework
Automated Semantic & Quantitative Standardization
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Performs dynamic unit harmonisation across emissions, energy, water, and operational metrics (tonnes CO₂e, MWh, GJ, etc.)
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Reconciles currencies, inflation adjustments, and reporting boundaries across geographies
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Aligns disclosures to GHG Protocol structures using rule-based + model-driven contextual mapping
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Detects inconsistencies, anomalies, and structural gaps before downstream analysis mapping
03 — Scope Attribution Engine
Model-Driven Category Intelligence
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Maps extracted data points to Scope 1, 2, and relevant Scope 3 categories using contextual classification models
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Surfaces confidence scores and traceability references instead of deterministic assumptions
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Maintains audit-ready lineage from raw source document to structured output
2. Organisation Identity Intelligence
AI-Driven Entity Resolution at Scale
In large supply chains, entity identity becomes a non-trivial data problem.
At scale, even minor inconsistencies create systemic risks: duplicate counting, fragmented emissions attribution, or missed exposure.
To address this, Sprih deployed a multi-layered entity resolution architecture purpose-built for supply chain sustainability.
Incorporates subsidiary-parent relationship mapping and acquisition lineage tracking
Uses contextual validation from disclosures, registration identifiers, and public corporate datasets
Performs fuzzy matching with adaptive thresholds tuned for sustainability reporting risk tolerance
Each match returns:
A calibrated confidence score
Traceable reasoning signals
Explicit ambiguity flags when the system detects potential collisions
No silent merges. No unchecked assumptions.
Identity resolution becomes structured, auditable, and scalable — even across thousands of suppliers and multi-layered corporate groups.
3. Dynamic Emissions Modeling
Company-Specific Estimation Architecture
When suppliers do not disclose emissions, most platforms default to generic spend-based emission factors. These approaches apply broad industry averages to financial spend — often producing high-level approximations that lack company-level specificity and can materially distort Scope 3 baselines.
Sprih takes a fundamentally different approach.
Instead of relying solely on spend multipliers, the platform generates supplier-specific emissions models using a multi-variable estimation framework.
Incorporates any partial disclosures available at the entity, subsidiary, or sector level
Calibrates estimates using industry-specific emission intensity distributions rather than single-point averages
Applies adaptive modelling that adjusts as new data signals become available
The result is not a generic proxy.
It is a dynamic, company-level emissions estimate designed to behave like a real disclosure — structured, defensible, and context-aware.
Estimates evolve with better data. Precision improves over time. Assumptions remain transparent and auditable.
This transforms non-disclosure from a blind spot into a measurable, model-driven input.
What does this mean for you and your supplier
You move from collecting data → to building intelligence across your supply chain.
Now you can:
Unlock deep, structured visibility across your entire supplier ecosystem
Break free from repetitive supplier surveys and fragmented data exchanges, hence reduced work load on your suppliers
Generate granular, decision-ready insights for supplier risk and decarbonization strategy
Seamlessly align procurement and sustainability within a single strategic lens
Scale across thousands of suppliers — without scaling operational complexity
Build long-term supplier partnerships grounded in shared climate ambition
This isn’t about reporting Scope 3.
It’s about redesigning how supply chains think, act, and decarbonize.
Intensity Dashboards— Emissions in Context
Total emissions alone are a lagging indicator. What drives decisions is emissions relative to business activity.
Our enhanced Intensity Dashboards transform raw carbon data into normalized, decision-grade metrics — aligning sustainability performance directly with revenue, production, throughput, and growth.
Instead of asking “Are emissions up?”, you can now ask:
Are emissions per unit produced declining?
Is revenue growing faster than carbon intensity?
Are efficiency gains offsetting expansion?
Are we structurally aligned with our net-zero glidepath?
What Makes It Critical
Sustainability maturity depends on moving from compliance reporting to operational control.
Intensity Dashboards enable:
Alignment between sustainability and finance
Production-level emission transparency for customers
Investor-grade performance benchmarking
Procurement-driven Scope 3 intensity monitoring
Integration into capital allocation and expansion planning
As customer requirements increasingly demand product-level and production-level emissions disclosure, intensity metrics become the foundation of commercial credibility.
Impact
Align sustainability with finance and operations
Deliver production-level emissions data customers now expect
Benchmark facilities, suppliers, and business units fairly
Turn decarbonization from reporting into measurable efficiency improvement
This is not just a dashboard upgrade. It embeds carbon performance into operational decision-making — making emissions intensity a core business metric, not just a disclosure number.
GLEC-Aligned Logistics Emissions Engine — Now Live
Logistics emissions are structurally complex. A single shipment can span road, rail, sea, and air — with varying fuel types, asset ownership structures, load factors, and energy sources across each leg.
This quarter, we’ve we went deeper with our GLEC-aligned logistics emissions engine, purpose-built to handle multi-modal freight accounting with precision and audit-grade traceability.
What’s New
1. Native Multi-Leg Shipment Modeling The engine now models shipments as structured transport chains rather than isolated legs.
Supports Air, Sea, Rail, Road, and last-mile delivery
Handles intermodal transitions within a single shipment ID
Maintains leg-level granularity for calculation and reporting
2. Automated Scope Attribution Logic Emission boundaries are now programmatically assigned based on fleet ownership and operational control.
Owned / controlled fleet → Scope 1
Third-party carriers → Scope 3
EV fleets → electricity correctly reflected under Scope 2
This eliminates manual scope reclassification and reduces reporting errors.
3. Advanced Emission Factor Architecture We’ve expanded factor handling to support full lifecycle accounting:
Tank-to-Wheel (TTW)
Well-to-Tank (WTT)
Well-to-Wheel (WTW)
Factors are applied contextually based on transport mode, fuel type, geography, and operational assumptions — ensuring alignment with GLEC methodology.
4. EV & Alternative Fuel Intelligence Specialized logic ensures:
Electricity emissions are separated from fuel combustion
Grid-based emission factors are applied appropriately
Hybrid and alternative fuel configurations are handled without double counting
Why This Matters
Freight emissions are often miscalculated due to mode transitions, inconsistent scope treatment, and simplified emission factors. This upgrade transforms logistics accounting from spreadsheet aggregation to structured, intelligence-native modeling.
The result:
Higher precision across complex freight networks
Audit-ready transparency
Reduced manual reconciliation effort
True GLEC-aligned reporting confidence
Logistics emissions are no longer estimated in bulk — they’re modeled leg by leg, with ownership and energy boundaries built into the core.
FAQs
How can I collect Scope 3 emissions data without constantly chasing suppliers?
An intelligence-first approach reduces survey dependency by ingesting publicly available disclosures, resolving supplier identities, and extracting emissions data automatically. Instead of starting with questionnaires, you start with existing data—engaging suppliers only where gaps truly exist.
What should I do if my suppliers don’t respond to sustainability surveys?
Non-response is often a capacity issue, not resistance. Platforms that can extract data from public reports or generate supplier-specific emissions estimates help reduce reliance on repeated outreach, minimizing vendor fatigue while maintaining visibility.
How can I standardize supplier data that comes in different formats?
AI-driven normalization frameworks can harmonize units, currencies, reporting boundaries, and methodologies automatically. This allows you to convert fragmented PDFs, spreadsheets, and disclosures into structured, comparable datasets aligned with GHG Protocol standards.
How do I handle suppliers that don’t disclose emissions at all?
Instead of defaulting to generic spend-based averages, advanced systems generate supplier-specific emissions models using financial intensity, geography, operational signals, and sector benchmarks. These models evolve as better data becomes available, improving precision over time.
How can I resolve duplicate or inconsistent supplier names in large supply chains?
Entity resolution engines match suppliers across subsidiaries, naming variations, and corporate hierarchies using AI-driven logic. Each match includes confidence scoring and traceable reasoning, reducing double counting and fragmented attribution.
How do I measure whether my emissions are improving relative to business growth?
Intensity metrics—such as emissions per unit produced or per revenue—provide a clearer performance signal than total emissions alone. Intensity dashboards help align sustainability progress with financial and operational outcomes.
How can logistics emissions be calculated accurately across multiple transport modes?
Multi-leg shipment modeling allows emissions to be calculated across air, sea, rail, and road within a single shipment structure. Emission factors are applied per leg, aligned with GLEC methodology, and traceable for audit purposes.
How do I assign Scope 1, 2, and 3 correctly for freight emissions?
Automated scope attribution logic classifies emissions based on ownership and operational control—owned fleets as Scope 1, third-party carriers as Scope 3, and EV electricity under Scope 2—reducing manual reclassification errors.
How can I reduce supplier reporting burden without losing visibility?
By leveraging shared intelligence and document-based ingestion, organizations can gather structured sustainability data without forcing suppliers into repetitive surveys. This preserves supplier relationships while maintaining compliance and Scope 3 coverage.
How do I scale sustainability tracking across thousands of suppliers?
Scalability requires automation across ingestion, normalization, identity resolution, and modeling. When intelligence is embedded at each layer, supply chain sustainability programs can expand without proportionally increasing operational complexity.
