ghg.services

AI-assisted carbon accounting · In development

Emissions data
you can trace.

We’re building a platform that connects source data, AI-assisted collection and transparent calculations. Join us as a pilot partner to shape it around real operational data.

Become a pilot partner Explore the workflow

Seeking pilot partners in the UAE, Saudi Arabia, the EU and the US.

3
Scopes
15
Scope 3 categories
Concept
Stage
Session log Concept · sample data

Fictional conversation. Illustrative data and calculations.

Target workflow: employees interact through a chat; analysts inspect the data and calculations here.

At the end of each tab the terminal offers an Excel export: click it and press y, or download the sample workbook (.xlsx) directly. Fictional data.

How it works

Turn conversations into structured emissions data.

Much of Scope 3 data lives in people’s heads and in scattered files. We start by asking the right people the right questions, then make every step of the path to a number inspectable.

  1. Fig 01

    Interview

    An AI interviewer asks employees and data owners structured questions in plain language, and follows up when an answer is incomplete.

  2. Fig 02

    Structure

    Answers become structured fields. A missing value stays marked as missing. Nothing is guessed.

  3. Fig 03

    Calculate

    A deterministic engine multiplies activity data by emission factors. The AI never does the maths.

  4. Fig 04

    Review

    Every result keeps its units, factor source and version, assumptions and review status. An analyst signs it off.

Principles

Built to be checked.

Traceable

Every number has a source.

Each result is broken down into activity, unit, factor, method, assumption and evidence. If something is an assumption, it says so.

Separated

AI collects. Rules calculate.

Models help gather and structure data. The calculation itself is deterministic and reproducible. A reviewer, not a model, signs off.

Honest

Clear about our stage.

We are at concept stage. Demonstrations use sample data and say so. We do not claim certifications, results or customers we do not have.

Scope 3 data collection is structured around the 15 categories defined in the GHG Protocol Corporate Value Chain (Scope 3) Standard.

Pilot programme

Shape it with us.

We are in discussions with prospective pilot partners and are looking for organisations that can work with us on real operational data.

Seeking pilot partners in the UAE, Saudi Arabia, the EU and the US.

Become a pilot partner

welcome@ghg.services

Proposed pilot format · details agreed per partner
Who it suits
Organisations with energy, fuel, travel, procurement or logistics data and a named data owner. Large operational sites such as airports are our first focus.
What you get
A data map, a first inventory for the agreed categories, a calculation ledger with evidence, and a reduction workshop.
Built together
Priority Scope 3 categories, import templates and the review workflow, shaped around your real data.
What we ask
Access to data, one or two process owners, regular working sessions and honest feedback.
Terms
Scope, timing and commercial terms are agreed with each partner. A case study is published only with your written approval.
Data handling
NDA before any data is shared. Where data is processed and which AI providers are involved is agreed in writing before any interviews take place.

Company

The team behind ghg.services

Maksim Irishkin

CEO, co-founder

Technology and innovation manager with a background in rare, rare-earth and precious metals extraction, and earlier experience running innovation projects and working with startups in mining and metals. He leads ghg.services’ strategy and partnerships, bringing industrial experience to the way emissions data is collected and verified in resource-intensive operations.

Vladimir Ageykin

CTO, co-founder

Software architect with 15+ years of experience delivering 30+ production applications across GenAI, financial systems and enterprise platforms. He has led engineering teams building LLM-based products and data-heavy platforms on Python and cloud infrastructure. At ghg.services he leads the technology: AI-assisted data collection with a transparent, deterministic calculation engine.

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