CreditPulse: The Agentic Covenant Monitor

Inspiration

The global syndicated loan market moves trillions of dollars annually, yet its operational backbone is currently facing a massive "Visibility Gap."

While trade execution has gone digital, Loan Administration (Agency Services) remains trapped in an analog era known as the "PDF Trap." Critical financial data is locked in unstructured compliance certificates, and banks spend thousands of hours manually keying this data into spreadsheets.

The scariest part? Lenders often discover a borrower is in distress 45–90 days after the quarter ends. In today’s volatile market, this latency is dangerous. We realized that banks are managing risk through a rear-view mirror.

We asked: What if an AI could not only read these documents but act as an intelligent agent—predicting breaches before they happen and standardizing the data for the entire industry?

What it does

CreditPulse is an Agentic AI Platform that modernizes the "Keeping Loans on Track" lifecycle. It transforms loan administration from a reactive, manual process into a proactive, predictive workflow.

Unlike standard "Chat with PDF" tools, CreditPulse performs three specific, high-value tasks:

1. Smart Ingestion & Interoperability: It ingests unstructured borrower PDFs and extracts granular financial data (EBITDA, Net Debt, Cash Flow) with near-perfect accuracy. Crucially, it automatically maps this data to the LMA Common Domain Model (CDM) JSON format, making the data instantly interoperable across the banking ecosystem.
2. Predictive Risk Sentinel: It acts as an Early Warning System. By analyzing historical trends and real-time data, our "AI Forecast Engine" projects key ratios (like Leverage Ratio) 3 months into the future. If a borrower is trending toward a "Danger Zone," the system alerts the banker months before a default occurs.
3. Agentic Waiver Workflow: When a breach is detected (or predicted), CreditPulse solves it. The AI agent automatically drafts a formal, LMA-standard Waiver Request letter, saving the Facility Agent hours of legal drafting time.

How we built it

We prioritized a stack that ensures speed, type safety, and data security—essential for fintech.

• Frontend: Built with Next.js 14 (App Router) for server-side performance. We used TypeScript to ensure strict handling of financial data types. The UI is styled with Tailwind CSS and Shadcn/UI to create a clean, Bloomberg-style professional aesthetic.
• The Brain (AI): We utilized OpenAI GPT-4o via API. We chose this model specifically for its superior reasoning capabilities and its JSON Mode, which was critical for enforcing the strict CDM Schema output.
• Backend & Database: We used Supabase (PostgreSQL). It provided us with an all-in-one solution for secure Authentication, Database management, and File Storage for the PDF documents.
• Visualization: We implemented Recharts to render the predictive "dotted trend lines" that visualize the risk forecast.

Challenges we faced

• The "Unstructured" Nightmare: No two borrowers submit compliance certificates in the same format. Some are scanned images; others are complex tables. Tuning the AI to ignore "noise" and extract only the specific covenants defined in the credit agreement was our biggest engineering hurdle.
• The Interoperability Puzzle: Understanding the LMA Common Domain Model (CDM) and mapping unstructured data into that rigid JSON structure required deep domain research into loan standards.
• Hallucination vs. Accuracy: In banking, being 99% right is 100% wrong. We had to ensure the AI output was verifiable by building a "Click-to-Verify" interface.

Accomplishments that make us proud

• Proactive vs. Reactive: We successfully built a predictive engine that visualizes future risk, shifting the paradigm from "reporting" to "forecasting."
• True "Agentic" Capability: The system doesn't just flag a problem; it drafts the solution (the Waiver Letter). This "Actionable AI" is a massive leap forward in operational efficiency.
• CDM Compliance: We aren't just building a tool; we are building infrastructure. By outputting data in the CDM standard, CreditPulse is ready to integrate with platforms like LMA.Automate from day one.

What's next for CreditPulse

• Phase 2: Integration with Open Banking APIs to validate the numbers in the PDF against real-time bank account cash flows.
• Phase 3: Blockchain Integration. We plan to record the "Covenant Status" (Pass/Fail) on a private ledger to provide instant, immutable transparency to all members of a loan syndicate without emailing spreadsheets.

Built With

• css
• gemini
• next.js
• postgresql
• recharts
• shadcn/ui
• supabase
• tailwind
• typescript