Overview
Scaler’s AI Suite assists at every stage of ESG data management: input, validation, analysis, forecasting, and decision-making. Each feature leverages asset-level and portfolio-level data to improve accuracy, reduce manual work, and provide clients with actionable insights.
Luc van de Boom, CIO, gives a tour of Scaler’s AI features:
- AI Sustainability Advisor: Lumi → min 3:50 & 10:00
- AI Analysis (Graph Insights) → min 6:13
- AI Roadmap Measure Suggestions → min 7:47
- AI Data Quality Report → min 11:10
- AI Forecasts → min 12:44
- Bill Scraping & Verification → min 13:50
1. AI Sustainability Advisor: Meet Lumi
Purpose:
To enable users to query their data with natural language prompts as well as access detailed information regarding tools, features and updates from Scaler Knowledge Base directly within the platform.
Location:
- Home tab
- Analytics Portal → Asset → Overview table → Lumi search bar, top of table
Example Prompts:
- “Draft a sustainability report comparing 2023 and 2024 data.”
- “Which assets have the highest transition risk?”
- “How does Scaler calculate data coverage?”
Technical Function:
Lumi references your portfolio dataset and internal Scaler documentation to generate context-aware responses.
Responses are stored temporarily and never leave Scaler’s secure AWS environment.
2. AI Analysis (Graph Insights)
Purpose:
To interpret charts and automatically generate explanations, insights, and next steps.
Location:
Analytics Portal → Portfolio → Chart → “AI Analysis” button, bottom right of the chart
Behavior:
- Runs AI analysis over underlying datasets of the selected graph
- Produces a short, contextual summary
- Suggests focus areas (e.g., “Asset X has highest EUI; consider HVAC efficiency audit”)
Supported Graphs:
- Energy Use Intensity
- Energy Total Consumption
- Energy Data Coverage
- GHG Emissions Intensity
3. AI Roadmap Measure Suggestions
Purpose:
To identify and recommend efficiency measures automatically.
Location:
Data Collection Portal → Roadmaps → “Add measure +” button
Data Inputs Used:
- Energy, fuel, and heating consumption
- Solar production
- Building age and type
- Location and climate zone
- Historical roadmap entries
Outputs:
- AI-suggested measures with predicted EUI/GHG reduction
- Cost and payback estimates
- Alternative options (e.g., insulation, window replacement, heat pumps)
Actions:
- Apply suggestions directly to the Measures library
- Generate a group of measures for an asset
- Generate a carbon saving measure
- Request partner advisory for verification or deeper analysis
4. AI Data Quality Report
Purpose:
To automatically flag anomalies, outliers, and inconsistencies in asset data before audits or reporting.
Location:
Analytics → Asset → Data Outliers → “AI Analysis” button, top of table
Features:
- Scans dataset at the portfolio level
- Flags high-risk assets with severity level and suggests actions
- Provides reason and risk type (e.g., meter malfunction, inconsistent readings)
- Direct request function for third-party data verification from our partners
Best Use Case:
Run before generating reports to ensure data integrity and audit readiness.
5. AI Forecasts
Purpose:
To fill missing or incomplete data gaps at the meter level.
Location:
Data Collection Portal → Asset → Meters & Consumption → Meter Consumption tab
How It Works:
- Identifies meters missing consumption data
- Uses historical values to project likely future usage
- Populates suggested values directly in the meter entry view
- Users can accept or edit before submission
- Available across energy, water and waste
Example:
If a meter’s last 3 years show a consistent trend, the AI forecast auto-fills next year’s expected consumption for validation.
Bill Scraping & Verification
Purpose:
To automate invoice data extraction and reduce manual input directly within Scaler.
Location:
Data Collection Portal → Asset → Meters & Consumption → Meter Consumption tab → paperclip icon
Process:
- At a meter consumption entry, upload a PDF invoice.
- AI extracts fields (meter ID, billing period, consumption, cost).
- The extracted data appears in the corresponding fields.
- Optionally, connect to Scaler’s verification partners for independent validation.
Advantages:
- Saves time and reduces error risk
- Ensures consistent, auditable data entries
- Integrates seamlessly with API or manual uploads
Security and Privacy
- All AI features operate fully within Scaler’s AWS environment in the client’s region.
- No external API calls are made to external AI systems.
- All data remains regionalized and compliant with applicable data privacy standards (GDPR, SOC 2, ISAE 3000, and ISO 27001).
Best Practices
- Review AI-suggested forecasts and measures before approval.
- Use AI Data Quality Reports monthly to monitor outliers.
- Leverage the Advisor for quick internal queries.
- Enable third-party verification for audit-prepared portfolios.