Inspiration

Loan agreements sit at the heart of the global lending market, yet they are still treated as static, long-form documents rather than structured, usable data. During our research into LMA workflows, we repeatedly saw the same pain points: manual data extraction, fragmented spreadsheets, late-stage compliance surprises, and limited visibility across portfolios.

What inspired LMA DocPulse was a simple but powerful question:

What if loan documents could behave like living systems instead of static PDFs?

We wanted to build something that aligned closely with LMA standards while solving real, everyday problems faced by lenders, agents, and compliance teams without requiring a full overhaul of existing processes.

What it does

LMA DocPulse is an AI-powered loan documentation and compliance platform that transforms unstructured loan agreements into standardized, actionable intelligence.

The platform allows users to upload loan documents and automatically:

Extract key commercial terms, covenants, and clauses using AI

Compare extracted data against LMA-aligned standards

Flag deviations and potential risk areas

Monitor covenant obligations and compliance status

Visualize portfolio-level risk, exposure, and trends

Query documents and portfolios using natural language

DocPulse creates a single source of truth for loan documents, compliance, and risk oversight.

How we built it

We built LMA DocPulse as a desktop-first web application focused on clarity, speed, and commercial realism.

At a high level:

Frontend: React + TypeScript with a clean, banking-style UI

Document Processing: PDF parsing and OCR for scanned agreements

AI Layer: GPT-based natural language processing to extract structured loan data and covenants

Data Storage: IndexedDB (via Dexie.js) for fast, secure local persistence

Analytics & Visualization: Interactive dashboards showing risk levels, exposure, and compliance status

Rather than aiming for a fully automated legal system, we intentionally designed DocPulse as a decision-support tool augmenting human expertise instead of replacing it.

Challenges we ran into

One of the biggest challenges was balancing technical ambition with commercial credibility.

Loan documents vary significantly in structure, language, and formatting, which makes consistent extraction difficult. We had to carefully design AI prompts and validation logic to ensure outputs were explainable and usable by non-technical users.

Another challenge was scope control. The loan lifecycle is vast, and it was tempting to build too much. We continuously asked ourselves:

Would this feature realistically help a lender tomorrow?

That question helped us stay focused on core value rather than complexity.

Accomplishments that we're proud of

Successfully extracting and standardizing key loan terms from real-world documents

Mapping extracted clauses to LMA-aligned standards

Building a clear, intuitive compliance and risk dashboard

Enabling natural-language queries over loan portfolios

Delivering a polished, enterprise-grade prototype within hackathon constraints

Most importantly, we’re proud that DocPulse feels like something a financial institution could actually deploy.

What we learned

We learned that innovation in financial markets isn’t always about radical disruption it’s often about removing friction.

Small improvements in visibility, standardization, and timing can have outsized impact

Built With

• dexie.js
• framer-motion
• html5
• indexeddb
• localstorage
• lucide-react
• openai-gpt-4-api
• react
• react-pdf
• recharts
• tailwind-css
• typescript
• vite