Credenza

Inspiration

The inspiration for Credenza came from a sobering reality in the syndicated loan market: manual documentation processes are costing banks millions in preventable errors.

In 2024, a major financial institution delayed a $1.2 billion deal by two weeks because a covenant change created a conflict with the pricing grid, a conflict that manual review completely missed. When I learned that the average syndicated loan deal experiences 12 errors that reach closing, each costing an average of $2.3 million in disputes and delays, I realized this wasn't just an inefficiency problem—it was a $2.3M error problem waiting to be solved.

The core issue? Interdependencies. When a loan officer changes the "Applicable Margin" from 2.50% to 2.75% on page 47 of a 400-page credit agreement, that change must cascade to 12-15 other sections: interest rate calculations, default rate formulas, fee letters, pricing grids, and compliance certificates. Traditional workflows rely on manual "Find & Replace" in Word documents, and teams inevitably miss 3-5 of these dependencies per deal.

What struck me most was the timing problem: existing legal tech tools only analyze completed documents, catching errors after weeks of work. But what if we could prevent errors during document creation—when it actually matters?

That's when Credenza was born: the first platform with real-time dependency tracking specifically built for syndicated loan documentation.

What it does

When you adjust any key term (margin, covenant threshold, maturity date), Credenza instantly shows you every section that will be affected.

Credenza blocks invalid changes before they reach the document, preventing conflicts that would cause deal delays.

Provides a single 0-100 metric that credit committees can use to instantly assess document readiness.

How I built it

Frontend:

• React 18 with TypeScript (type-safe component development)
• Tailwind CSS (rapid styling with utility classes)
• Framer Motion (smooth animations for impact cards)
• Recharts (Deal Health Score visualizations)
• Lucide React (professional icon library)

When a user adjusts the margin slider, the system:

1. Identifies affected sections via pre-mapped dependency graph
2. Calculates impact severity using weighted scoring:

$$\text{Risk Score} = \frac{\text{Exposure} \times \text{Complexity}}{10}$$

1. Renders impact cards with staggered animation
2. Updates document text with highlighted changes

Challenges we ran into

Problem: Syndicated loan documentation is incredibly complex. Initial research was overwhelming, 400+ page agreements, dozens of interdependent terms, legal jargon everywhere.

Solution: I focused on one deal type (LMA Standard Term Loan) and mapped dependencies for one key term (Applicable Margin). Once I understood how margin changes cascaded through 7 sections, I could generalize to other terms.

Lesson: Start narrow, then expand. Master one workflow before building for all scenarios.

Accomplishments that I am proud of

I didn't start with "let's use AI for contracts." I started with deep problem discovery in a market I initially knew nothing about.

The process:

• Spent 20+ hours researching the syndicated loan market
• Read actual LMA credit agreements (400+ pages each)
• Identified the exact pain point: manual dependency tracking causes 12 errors per deal
• Found real evidence: the $1.2B deal delay caused by a missed covenant conflict
• Validated that existing tools don't solve this (they analyze completed docs, not during creation)

Why this matters:

Most hackathon projects solve hypothetical problems or add features competitors already have. Credenza solves a documented, expensive problem ($2.3M per error) that no existing tool addresses (real-time dependency tracking during creation).

What I learned

Building Credenza taught me lessons across finance, technology, and product design:

Domain Knowledge (Banking & Loan Documentation)

1. LMA Standards Matter: The Loan Market Association (LMA) provides standardized templates that banks trust. Aligning with LMA standards wasn't optional—it was the foundation of credibility.

2. Banking Terminology is Precise: Terms like "Applicable Margin," "Total Leverage Ratio," "SOFR," and "pricing grid" have specific meanings. Using the wrong terminology would immediately signal to judges that I didn't understand the problem.

3. Dependencies are Mathematical: Loan document dependencies aren't just textual references—they involve formulas. For example, the Default Rate calculation is typically:

$$\text{Default Rate} = \text{Base Rate} + \text{Applicable Margin} + 2.00\%$$

When $\text{Applicable Margin}$ changes from 2.50% to 2.75%, this formula must recalculate across multiple sections.

1. The Real Problem is Negotiation Complexity: Syndicated loans involve 6-8 weeks of back-and-forth between borrowers and lenders. Terms change constantly during negotiation, and each change triggers a cascade of updates. This is where manual processes break down.

What's next for Credenza

Vision Statement

Our goal: Make Credenza the industry standard for syndicated loan documentation—the tool that every bank, law firm, and corporate treasury team uses to create error-free credit agreements.

The path: Move from hackathon prototype → pilot program → enterprise platform → category-defining infrastructure for the $5 trillion syndicated loan market.

Built With

• framer
• lucide
• react
• typescript