Case study 02 · Vertical AI SaaS
ChatESG®: specialist intelligence made operational
An AI-native ESG workspace that turns complex frameworks and business evidence into practical action.
- Platform type
- Vertical AI SaaS
- Primary audiences
- Executives, ESG teams, advisers, procurement teams and SMEs
- Capability demonstrated
- Secure knowledge ingestion, domain workflows, AI assistance, reports and subscription product design
The challenge
ESG work is knowledge-heavy, evidence-dependent and increasingly time-sensitive. Smaller organisations may not have a dedicated sustainability team, while advisers and internal leads spend significant time moving between standards, spreadsheets, policies, supplier information and report drafts. Generic chat tools can produce fluent answers, but they do not automatically understand the organisation, preserve evidence or structure the work required for governance and disclosure.
The platform response
ChatESG® was developed as a specialist operating workspace rather than a standalone chatbot. Users can upload organisational material or provide website context once, review the extracted profile and use that approved context across a suite of ESG workflows. The system combines guided assessments, generation tools, evidence capture, learning resources and reporting inside one product experience.
- One-upload context ingestion for PDF, Word, Excel, PowerPoint, CSV, website and pasted-text sources.
- A smart document library using retrieval-augmented generation to ground answers in approved organisational material.
- AI ESG assistant, ASRS quick-check, maturity assessment, materiality workflow and action-plan builder.
- Modern slavery screening, responsible procurement, supplier ESG questionnaires and policy generation.
- SDG tracking, social impact and SROI workflows, analytics, professional reports and an ESG Academy.
- Subscription journeys, free assessment entry points, support, trust, privacy and responsible-AI content.
AI-native by design
AI is embedded where it reduces interpretation and drafting effort: extracting context, identifying gaps, explaining results, drafting policies and reports, and suggesting next actions. The core trust pattern is grounded context plus structured workflow. Outputs are most useful when they can be traced back to organisational material, reviewed by a person and stored as part of an evidence-led process.
Business value
ChatESG® demonstrates how deep professional knowledge can be productised without collapsing it into a generic chatbot. It creates multiple entry points for different users, makes specialist methods repeatable and supports a blended model in which software enables self-service while complex decisions can escalate to advisory support. The platform is designed to improve consistency and reduce drafting time; quantified efficiency claims will wait for product analytics or approved customer studies.
