Sequence - Loan Life Cycles

Inspiration

Managing loan agreements is a compliance nightmare. Every day, loan administrators manually sift through hundreds of pages of legal documents, tracking dozens of deadlines and financial covenants across multiple deals. One missed deadline or overlooked covenant breach can cost millions in penalties.

We asked ourselves: "Why, in the age of AI, are we still Ctrl+F'ing through PDFs?"

The LMA Edge hackathon gave us the perfect opportunity to build what loan administrators actually need—a platform that transforms static legal documents into dynamic, queryable data.

What it does

Sequence is an AI-powered loan management platform that:

Parses Documents Instantly - Upload a PDF or Markdown file, and our Cartographer engine extracts every article, section, and clause while preserving the document hierarchy.
Extracts Obligations Automatically - Using GPT-5 mini, we identify every obligation, reporting requirement, financial covenant, and deadline buried in the legalese.
Enables Natural Language Queries - Ask "What covenants are breached?" and get instant answers with citations to the exact clause.
Monitors Compliance in Real-Time - Track covenant status (compliant, at-risk, breached) across your entire portfolio with a visual dashboard.
Visualizes Deadlines - See every upcoming deadline on a calendar view with color-coded status indicators.

How we built it

Frontend: Next.js 15 with App Router, React, and Tailwind CSS for a modern, responsive interface.

Backend: Convex for serverless functions, real-time subscriptions, and a fully-typed database.

AI/ML Pipeline:

Reducto API for PDF parsing and structure extraction
OpenAI GPT-5 mini for obligation extraction and natural language understanding
Vector embeddings (text-embedding-3-small) for semantic search
Custom RAG pipeline ensuring all AI responses are grounded in document text

Key Architecture Decisions:

Chunked processing to handle documents of any size without timeouts
Incremental extraction with live progress updates
Citation-backed AI responses to prevent hallucinations

Challenges we ran into

Document Scale - Loan agreements can be 200+ pages with 700+ clauses. We had to implement chunked processing and chain scheduling to stay within serverless time limits.
AI Accuracy - Early versions would hallucinate covenant values. We solved this with a strict RAG pipeline that forces every answer to cite specific document sections.
Real-time UX - Users need to see extraction progress live. Convex's reactive queries made this possible, but required careful state management.
Date Resolution - Loan documents use complex relative dates ("within 45 days after fiscal quarter end"). We built a temporal resolution engine to calculate actual due dates.

Accomplishments we're proud of

End-to-end pipeline - From PDF upload to queryable data in seconds
Zero hallucination AI - Every answer cites the source clause
Beautiful UX - Clean, modern interface that loan admins will actually want to use
Real-time everything - Live processing status, instant search, reactive dashboards

What we learned

LLM prompting for structured extraction is an art—specificity matters
Vector search quality depends heavily on chunk strategy
Real-time database sync changes everything for UX
Legal documents have surprisingly consistent patterns once you understand them

What's next for Sequence

Integration APIs - Connect to Bloomberg Terminal, Loan IQ, and other enterprise systems
Automated Alerts - Email/Slack notifications for upcoming deadlines and covenant breaches
Waiver Drafting - AI-generated waiver request letters for covenant breaches
Multi-document Analysis - Compare covenants across deals and identify portfolio-wide risks
Mobile App - Compliance monitoring on the go