The Center for Data Innovation recently spoke with Mike CYK, co-founder of StarfetchX, a Malaysia-based company providing an AI-powered carbon-accounting platform that measures and analyzes emissions across corporate operations and supply chains. CYK explained how the platform integrates business activity data with global emissions databases to calculate emissions, identify hotspots, and produce compliance-ready reports.
David Kertai: What does StarfetchX offer?
Mike CYK: Many companies today face growing pressure from regulators, investors, and customers to measure and reduce their environmental impact from greenhouse gas emissions. But carbon accounting remains a fragmented, manual, and backward-looking process. Emissions data is often scattered across utilities, procurement, logistics, and finance systems, and many tools only summarize past emissions instead of helping organizations reduce them in real-time.
To address this, StarfetchX offers an AI-driven carbon-accounting and sustainability-compliance platform that automates data collection, emissions calculation, and reporting. The system converts raw business activity data into emissions across all three scopes: Scope 1, which covers direct emissions from company operations; Scope 2, which includes emissions from purchased electricity and energy; and Scope 3, which covers indirect emissions from suppliers, transportation, and business travel. By connecting data across business systems and using global emissions databases, the platform helps organizations measure impact more accurately and identify opportunities to reduce emissions.
Kertai: How does your platform calculate emissions and display the findings?
CYK: The platform calculates emissions by converting business activity data into standardized environmental metrics using emission factors, which are coefficients that translate activities like fuel use or electricity consumption into greenhouse gas emissions. It then automatically matches each data point with certified global emissions databases to generate carbon-dioxide-equivalent values.
Once processed, the results appear in a real-time dashboard that breaks emissions down by facility, business unit, or scope. The dashboard also shows trends over time and highlights areas where emissions are increasing, helping users identify environmental hotspots and track progress toward reduction goals.
Kertai: What types of operational data does your AI system analyze when assessing carbon emissions?
CYK: Our AI system analyzes operational data that reflects a company’s day-to-day activities. This includes utility bills, fuel-consumption records, travel expenses, waste-management data, procurement records, and supply-chain logistics information. Together, these datasets allow the platform to calculate both direct operational emissions and indirect emissions generated across the broader value chain.
The AI system also connects information that is often stored in separate systems and converts it into a unified emissions profile. It can automatically identify missing information, flag unusual data patterns, and validate incoming records in real-time. This reduces the amount of manual work required from sustainability and finance teams while improving consistency across reporting.
Kertai: How does your AI-powered reporting system ensure reports align with sustainability and regulatory requirements?
CYK: The platform’s reporting system is designed around major international sustainability-reporting standards and regulatory frameworks. The system automatically organizes emissions data into the formats required for disclosures, generates compliance-ready summaries, and structures reports according to regional or industry-specific requirements. This removes much of the manual work traditionally involved in sustainability reporting.
To ensure accuracy, the platform relies on standardized scientific methodologies and government-approved emissions databases that are updated regularly. The system also applies automated validation checks and anomaly detection to identify inconsistent or incomplete data before reports are finalized. As a result, organizations can generate audit-ready disclosures more quickly and with greater confidence.
Kertai: Could you share a real-world example?
CYK: One recent example involved a rubber and plastics manufacturer seeking to reduce energy use across its production lines. By consolidating utility-billing data and production records into a centralized system, the platform allowed the company to track emissions dynamically instead of relying on annual reports generated months after operations occurred. Managers could immediately identify which production lines consumed the most energy during peak operating periods and adjust schedules to improve efficiency.
The platform’s visual dashboards also allowed executives to compare emissions across facilities, identify carbon hotspots, and evaluate which equipment upgrades would produce the greatest environmental and financial benefits. This helped the company make faster and more informed operational decisions while maintaining compliance with sustainability-reporting requirements.
