EnergySync AI Platform Whitepaper

1. Introduction

Utility costs and energy contracts are a universal concern for multi-site businesses, yet inefficiencies abound:

• Billing Errors: 1-5% of utility invoices contain overcharges, a small percentage that balloons across thousands of locations.
• Contract Mismanagement: Missed renewals lock clients into higher rates, especially costly for large portfolios.
• Scalability Gaps: Tools that work for a few sites fail at the scale of hundreds or thousands.

EnergySync AI solves this for brokers serving hundreds of clients—like ours, with customers ranging from McDonald's (thousands of sites) to small chains (a few locations). It audits utility documents, tracks contract terms, and leverages RAG for strategic insights, all while scaling seamlessly.

2. System Overview

EnergySync AI is a cloud-based platform with a multi-agent design, processing documents from cloud drives (e.g., Google Drive, Microsoft Teams) or uploads. It integrates:

• Audit Module: Detects errors and extracts data.
• Contract Module: Manages terms and RFPs.
• RAG Engine: Powers natural language queries.

Key Features

• Universal Processing: Handles electricity, gas, water, and more.
• Error Detection: Identifies overcharges and anomalies.
• Contract Tracking: Alerts and automates procurement.
• Pay-Per-Use: Transparent costs (e.g., $0.25/doc).
• Scalability: Adapts from 1 to 10,000+ locations.

3. Methodology

3.1 Multi-Agent Architecture

Our system deploys specialized AI agents:

• Extractor Agent: Parses documents for usage, rates, and contract dates.
• Validator Agent: Flags discrepancies (e.g., billed vs. contracted rates).
• Tracker Agent: Monitors expirations and triggers RFPs.
• Query Agent: Answers queries via RAG (e.g., "Which McDonald's sites have expiring contracts?").

Agents scale dynamically, processing a single invoice or thousands in parallel.

3.2 Audit System

The audit process:

1. Ingestion: Syncs drives or accepts uploads.
2. Extraction: Pulls fields (e.g., 50,000 kWh, $0.10/kWh).
3. Validation: Rate mismatches, usage spikes, ID consistency.
4. Reporting: Logs findings with per-doc costs.

3.3 Contract Tracking & Procurement

• Term Extraction: Captures contract details.
• Expiry Alerts: Notifies at 90/60/30 days.
• RFP Automation: Generates bids for 1 site or 1,000.
• Bid Analysis: Compares supplier offers.

3.4 Retrieval-Augmented Generation (RAG)

Our intelligent knowledge system works behind the scenes to deliver personalized insights while maintaining data privacy:

• Data Store: Embeds data in a vector database.
• Client-Specific Architecture: Isolates knowledge bases by user, client, and user-client combinations.
• Query Engine: Retrieves and generates answers (e.g., "List small clients with water overcharges").
• Hybrid Search: Falls back to web search if information isn't found in knowledge base.
• Streaming Responses: Delivers answers in real-time with progressive generation.
• Scalability: Handles queries across diverse portfolios with context-specific knowledge.

3.5 Personalized and Protected Knowledge

Our platform ensures your data stays private, secure, and only accessible to the right people:

• User-Specific Access: Each broker has a private knowledge hub for their documents and insights.
• Client-Specific Security: Client documents are isolated into their own secure areas.
• Broker-Client Relationships: Brokers can access specialized knowledge for their specific client relationships.
• Smart Information Retrieval: The system searches the most relevant information first before broadening its search.
• Controlled Access: Security controls ensure appropriate access to knowledge within the organization.

4. Technical Implementation

• OCR/NLP: Google Vision and fine-tuned BERT.
• RAG: Sentence-BERT embeddings, multi-tenant vector stores, streaming LLM interface.
• Backend: Next.js, TypeScript, PostgreSQL, Vercel hosting.
• Cost Model: $0.10-$1.00/doc, tiered for volume.

5. Use Case: Diverse Client Portfolio

5.1 Scenario

• Portfolio: Hundreds of clients, e.g.:
  • McDonald's: 3,000 locations (electricity, gas, water).
  • Carl's Jr.: 1,030 locations.
  • Local Chain: 5 locations.
• Documents: Varies from 15 to 9,000 monthly invoices + contracts.
• Goal: Audit bills and manage renewals across scales.

5.2 Execution

• Large Client (McDonald's):
  • Audits 9,000 invoices ($2,250 at $0.25/doc), finds $15,000 in errors.
  • Tracks 150 expiring contracts, secures $50,000/year savings via RFPs.
  • Query: "Which Northeast sites overpaid?" → "50 sites, $5,000."
• Mid-Size Client (Carl's Jr.):
  • Audits 3,090 invoices ($772.50), saves $5,000.
  • Tracks 50 expirations, saves $24,000/year.
• Small Client (5 Sites):
  • Audits 15 invoices ($3.75), saves $100.
  • Tracks 1 contract, saves $500/year.

5.3 Results

• Savings: $100 (small) to $65,000 (large).
• Cost: $3.75 to $2,250.
• ROI: 25x-37x, consistent across scales.

6. Benefits

• Flexibility: Serves clients with 5 or 5,000 sites.
• Efficiency: Cuts audit and renewal time by 90%.
• Savings: Delivers ROI for all portfolio sizes.
• Transparency: Pay-per-use ties costs to value.

7. Conclusion

EnergySync AI empowers energy brokers to manage diverse clients—from McDonald's to small chains—with a unified platform. Auditing ensures immediate savings; contract tracking drives long-term gains. For our hundreds of customers, it's a scalable, proven solution.

8. Next Steps

• Pilot: Test with a mix (e.g., 5 McDonald's, 10 Carl's Jr., 2 small clients).
• Refinement: Adjust RFP templates per client type.
• Launch: Deploy across all clients by Q4 2025.