Product Strategy · Solution Architecture · AI Integration · Full-Stack Development · Reporting Engine Design
As greenhouse gas reporting moves from voluntary best practice to regulatory requirement across industries and geographies, organisations face a growing challenge: understanding their emissions footprint accurately enough to report on it, act on it, and demonstrate compliance to auditors and stakeholders.
Most businesses approaching this problem for the first time encounter the same friction — they have operational data scattered across departments, no standardised way to translate that data into emissions figures, and no clear view of where their highest-impact reduction opportunities lie. The tools that exist are either too generic to be useful or too complex to be adopted without specialist support.
EasyGHG was built to solve that problem. Raiis designed and developed the platform from the ground up — a dynamic data ingestion and AI-powered reporting system that takes a company's operational inputs and transforms them into structured, compliance-ready carbon intelligence.
The core challenge EasyGHG addresses is one of translation. Organisations generate vast amounts of operational data — energy consumption, fleet usage, supply chain activity, facility management records. However, translating that raw data into accurate greenhouse gas figures requires expertise most businesses do not have in-house.
The manual alternative — engaging consultants to assess emissions annually — is expensive, slow, and produces a point-in-time snapshot rather than an ongoing view. It also creates a dependency: the moment the engagement ends, the organisation loses visibility until the next assessment cycle begins.
Beyond the measurement problem, there is the action problem. Even organisations that successfully measure their emissions often lack a clear, prioritised roadmap for reducing them. Generic best-practice advice does not account for the specific structure, industry, or operational profile of each business. The platform needed to solve both sides: make measurement accessible and continuous, and make the path to reduction specific and actionable.
The objective was to build a platform that could serve as both a measurement tool and an advisory system — removing the dependency on external consultants for routine emissions tracking while providing AI-generated guidance tailored to each organisation's specific situation.
The platform needed to:
Raiis architected and built EasyGHG as a multi-tenant data platform with an AI advisory layer at its core.
The platform guides organisations through a structured data input process via a centralised management dashboard — capturing the operational information needed to calculate emissions across the relevant categories for their industry and size. The interface is designed to be completed by an operations or sustainability manager without requiring specialist knowledge, with contextual guidance built into the input flow.
Once data is submitted, the platform's processing engine calculates the organisation's emissions profile, benchmarks it against relevant industry standards, and passes the results to an AI model configured to identify reduction opportunities specific to that organisation's operational structure. Rather than producing generic recommendations, the system analyses where the majority of emissions originate and generates a prioritised roadmap — identifying which changes will have the most material impact given the organisation's specific profile.
The reporting engine then compiles the results into structured, compliance-ready documents — formatted to meet regulatory reporting requirements and designed to be audit-ready without additional processing. Reports include the underlying data inputs, calculation methodology, benchmarking context, and the AI-generated reduction roadmap.
The platform is built for ongoing use rather than one-time assessment. As operational data changes, the system recalculates the emissions profile and updates the reduction roadmap accordingly — giving organisations a continuously current view of their carbon position rather than an annual snapshot.
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