Layer 04 · Capital
Financing Layer
Trade finance, invoice discounting and ESG-linked credit underwritten on live production data.
Manufacturing OS
Accelerating Sustainable Manufacturing through AI & Material Intelligence.
AAGM’s MaterialsIQ platform helps clean-tech manufacturers improve yield, reduce waste, trace material provenance, lower Scope 3 emissions and build climate-resilient supplier networks — all through one AI-native manufacturing intelligence layer.
Federated learning · privacy-preserving
Research partners: NTU · SERIS
AI for sustainable manufacturing · Climate impact · Energy transition
Suppliers
12,480 verified
Manufacturers
GW-scale
Yield uplift
+3.7pp
CO₂e tracked
2.1Mt
The Problem
Clean-tech manufacturing is too slow, too fragmented, too carbon-blind.
The energy transition depends on scaling solar, batteries, semiconductors and advanced materials faster. But clean-tech supply chains still run on PDFs, spreadsheets, disconnected systems and incomplete ESG data. Manufacturers cannot optimize for yield, cost, resilience and emissions at the same time without AI.
No supplier visibility
Procurement teams operate on stale spreadsheets and broker relationships — without lot-level traceability.
Data silos
MES, LIMS, ERP, and supplier data never converge. Engineers can't correlate incoming material with outgoing yield.
Slow yield optimization
It takes quarters of trial-and-error to dial in a new recipe. Competitors at GW scale move faster than you can iterate.
ESG complexity
Scope 3 disclosures, CBAM, SBTi — all manual, all error-prone, none integrated with the procurement reality.
Planet Impact
Better production and sourcing decisions for both business and the planet.
MaterialsIQ helps clean-tech manufacturers make better production and sourcing decisions for both business performance and the planet.
Cut scrap & rework
Reduce material scrap and rework by identifying material-process-yield drivers earlier.
Boost clean-tech output
Improve solar, battery and semiconductor manufacturing yield from the same production capacity.
Trace Scope 3 emissions
Track material provenance and Scope 3 emissions at lot, supplier and shipment level.
Qualify lower-carbon suppliers
Accelerate qualification of lower-risk, lower-carbon supplier alternatives.
Climate-resilient supply chains
Build climate-resilient supply chains across clean-energy manufacturing ecosystems.
MaterialsIQ Platform
Four layers of manufacturing intelligence.
Data → Intelligence → Transactions → Financing. One stack, from raw signal to financed transaction — powered by AI agents, federated learning, blockchain provenance and a verified supplier graph.
Layer 03 · Commerce
Transactions Layer
Verified procurement, RFQs, sales orders, digital material passports and blockchain-anchored provenance.
Layer 02 · AI
Intelligence Layer
Material-process-performance models, defect attribution, yield simulation and supplier scoring — with the MatiE copilot on top.
Layer 01 · Foundation
Data Layer
Unified MES, LIMS, ERP, supplier and IoT data streamed into one lot-level material ledger.
Connectors
MES · LIMS · ERP · supplier portals · IoT line telemetry · TMS/WMS — auditable from sensor to settlement.
Federation & Blockchain
RDFL federated learning trains across manufacturers without moving raw data. BLIC anchors passports, transactions and signatures on-chain.
Security & Trust
Zero-trust networking, customer-managed keys, SOC2-aligned controls and cryptographic provenance for IP-sensitive manufacturers.
Proof in the Field
Case Studies — Yield, Waste and Emissions Impact through Material Intelligence
Real factory incidents where AAGM turned hidden material and process variability into measurable financial gain and lower environmental impact.
Case 01 / 03
Solar Module Manufacturing
$480KAnnual yield loss averted
Hidden Micro-Cracks Cost a Line $480K — AI Recovered It in One Batch
10
Modules saved per batch
62%
Defect rate reduction
<24h
Time to root cause
Problem
AOI cameras silently missed micro-cracks at the pre-EL stage. Defective modules slipped through to post-EL inspection — burning labor, energy and material on every flawed unit before scrap.
AI Insight
AAGM correlated incoming film batch fingerprints (embossing pattern, glossiness, orientation) with downstream camera visibility. The platform flagged the offending batch signature before it hit the line.
Resolution
Immediate orientation flip restored AOI visibility; long-term embossing redesign eliminated the failure mode. Result: zero scrapped modules from this defect class and ~$480K/yr recovered margin.
Planet impact: fewer defective modules produced, less material scrap, lower rework energy and higher clean-energy output from the same production capacity.
Competitive advantage
A moat that compounds with every transaction.
Other platforms manage suppliers. AAGM understands why performance varies — and turns that understanding into a defensible data network.
AI + Federated Learning
Train across customer datasets without moving raw data — each manufacturer benefits from collective intelligence while keeping IP private.
Blockchain traceability
Anchored material provenance and transaction records — immutable audit trail for CBAM, SBTi, and customer disclosures.
Proprietary dataset
Material-process-performance models built with NTU & SERIS. The only dataset linking lab-grade material properties to GW-scale production yield.
Network effects
Every transaction strengthens the supplier graph. Every onboarded line refines the AI. Switching cost compounds with every datapoint.
Research Partners
Backed by world-class research institutions.
NTU Singapore
Nanyang Technological University
Leading research in materials science, AI, and clean-energy systems.
SERIS
Solar Energy Research Institute of Singapore
World-class solar R&D and PV materials characterization.
UNSW
University of New South Wales
Pioneering photovoltaics and sustainable manufacturing research.
Get started
Build the future of Manufacturing Intelligence.
Join the operators, suppliers and partners building the operating system for the global clean-tech economy.