Ampere Financial, a U.S.-based innovator in energy transition financing, is reimagining how small and mid-sized industrial businesses decarbonize. The challenge? Critical customer data, utility bills, equipment images, rebate rules, was scattered across PDFs, images, and spreadsheets, making it hard to turn into actionable intelligence.
The problem: missed rebate opportunities and complex energy guidance
Ampere Financial’s customer advisory process was slowed by the complexity of handling diverse data sources. Utility consumption records, equipment specifications, and rebate program details were spread across PDFs, images, and CSV files, making it labor-intensive to compile a complete view of a client’s eligibility for energy efficiency incentives. The lack of a unified data pipeline meant that rebate opportunities were often overlooked, leading to missed savings for clients and reduced adoption of electrification upgrades.
Additionally, traditional dashboards required manual navigation and interpretation, limiting personalization and forcing advisors to spend hours preparing reports. Ampere needed a secure, AWS-native AI copilot for decarbonization capable of ingesting this fragmented data, performing cross-table reasoning, and delivering accurate, location-specific guidance instantly, without the need for complex technical queries.
The solution: an AI copilot for decarbonization with cross-table reasoning
To address these challenges, GoML and Ampere Financial co-developed a secure, AWS-first AI copilot for decarbonization capable of turning raw, fragmented energy data into dynamic conversations.
Real-time, natural language queries
Users can ask domain-specific questions e.g., “What rebates apply to upgrading my air compressor in ZIP code 77002?” and get contextual, accurate answers without touching SQL or spreadsheets.
ZIP code–based rebate matching
The system cross-references customer equipment data with federal, state, and local rebate rules stored in Supabase, ensuring location-specific guidance with multi-level program coverage.
Cross-table intelligence
With integrated access to multiple equipment, consumption, and rebate tables, the AI copilot for decarbonization performs reasoning across datasets to surface relevant insights.
Secure, scalable AWS architecture
The PoC leverages Amazon Bedrock (Claude Sonnet 3.7) for query generation, AWS Lambda for compute, S3 for storage, and a Supabase integration for structured data handling designed for scalability to nationwide deployment.
Conversational testing interface
A Streamlit UI enables stakeholders to test copilot capabilities quickly, ensuring zero-hallucination performance and validating rebate matching logic before production rollout.
The impact: smarter rebate usage, faster electrification decisions
Ampere Financial’s AI copilot for decarbonization delivered measurable results during the PoC phase:
- 65% faster rebate discovery: Instant ZIP code–based matching replaced manual lookups.
- 50% reduction in advisory preparation time: Conversations replaced dashboard navigation.
- 40% increase in identified rebate opportunities: Improved cross-referencing between equipment data and rebate rules.
- 30% higher client engagement: Advisors could deliver immediate, personalized recommendations in real-time.
Lessons for decarbonization tech leaders
Common pitfalls to avoid
- Relying on static dashboards for dynamic rebate logic
- Ignoring the complexity of multi-level rebate coverage
- Skipping hallucination safeguards in AI-driven recommendations
Advice for energy transition innovators
- Start with a PoC focused on speed to insight and accuracy
- Design for scale from day one with AWS-native architecture
- Build with real rebate and equipment data to ensure practical outcomes
Ready to accelerate your industrial decarbonization strategy with an AI copilot for decarbonization?
Let GoML help you transform fragmented energy data into instant, actionable guidance just like we did for Ampere Financial.
