LMA Smart Search

Transforms loan document search from 25 minutes to 3 seconds. Natural language queries return ranked results with deal intelligence: why it matters, which sections to focus on, key considerations.

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📹 DEMO VIDEO NOTE: Please disable YouTube auto-generated captions (CC button) as our video includes embedded English subtitles.

LMA Smart Search

Demo Video: https://youtu.be/nGsZOQikvAU
Pitch Deck: https://drive.google.com/drive/folders/14mO2gwhOG9DxSYo5GEHTHzWPbrz61pCr?usp=sharing

🎯 FOR LMA EDGE HACKATHON JUDGES

Quick Start (5 minutes):

  1. Download this repo as ZIP
  2. Extract and load in Chrome (chrome://extensions/ → Developer mode → Load unpacked)
  3. Click extension icon → Try the 3 example queries
  4. Watch our demo video for full walkthrough

Having issues? The demo video shows complete functionality.

Inspiration

The idea sparked during my research for this hackathon.

I visited the LMA website to understand loan market KPIs and documentation practices. I needed information about sustainability-linked loan structures—a typical banker query.

Twenty minutes later, I was still scrolling through search results. The search engine required exact keywords, returned 47 mostly irrelevant documents, and gave me no guidance on what mattered.

I realized: If I'm struggling with this, what about relationship managers under deal pressure? A banker can't spend 25 minutes searching while a client waits on the phone. Deals are lost to competitors who respond faster.

That frustration became our mission: build search that understands intent, provides deal intelligence, and respects the banker's time. Every feature in LMA Smart Search solves a pain point I experienced firsthand.

Sometimes the best products come from experiencing the problem yourself.

What it does

LMA Smart Search is a Chrome Extension that transforms how relationship managers search for LMA loan documentation. Instead of spending 20-25 minutes sifting through 47 irrelevant keyword matches, bankers can ask natural language questions and get the right document in 3 seconds—with detailed guidance on what sections to focus on and why it matters for their specific deal.

We don't just give you a document link. We give you deal intelligence: a three-part explanation of relevance, section-specific guidance on what to look for, and key considerations for compliance and negotiation. Think of it as having an expert colleague who not only finds the document but tells you exactly where to look and what to watch out for.

Key Benefits:

  • 99.8% faster search (25 min → 3 sec)
  • Natural language queries ("How does margin adjust based on ESG?" vs exact keywords)
  • Compliance-first design (offline-by-default, no deal data leaves your device)
  • Deal-focused intelligence (not just links, but actionable guidance)
  • Zero setup required (no API keys, works immediately)

Real Impact: A single saved meeting costs £1,500 in banker time. A secured $6M deal that would have gone to a faster competitor? Priceless.

🔒 Enterprise Security & Compliance:

Built with privacy-first, offline-capable architecture.

Current Demo: Fully offline—runs in your browser, no external API calls, documents never leave your device.

Production Deployment: Supports both self-hosted (on-premises, air-gapped) and secure cloud options, maintaining full banking compliance (GDPR, SOC 2, ISO 27001).

Unlike ChatGPT-style AI: you control where your data lives, zero training on your documents.

How we built it

We built LMA Smart Search using a human-AI collaboration approach that we believe represents the future of product development:

Human Orchestrators:

  • Chloe - Product vision, AI coordination, domain research
  • Driver - Video production, QA, infrastructure management

AI Collaborators:

  • Claude (阿寶) - Technical architecture and development
  • ChatGPT (曦) - Product strategy and rigorous QA
  • Perplexity (Percy) - Research and fact-checking
  • Gemini (Jimmy) - Visual design and graphics
  • Copilot (Amber) - Real-time support and terminology

Technical Stack:

  • Chrome Extension (Manifest V3) for seamless desktop integration
  • Intent-aware search engine with curated semantic index
  • Offline-first architecture for compliance and reliability
  • Pure JavaScript (no frameworks) for minimal overhead
  • Custom ranking algorithm balancing keyword and intent matching

Development Process:

  1. Deep research into LMA pain points through banking industry analysis
  2. Iterative prototyping with multiple AI assistants providing different perspectives
  3. User-centric design focusing on "Why this matters" over "Here's a link"
  4. Rigorous copyright compliance review to respect LMA's intellectual property

Unique Approach: Rather than treating AI as a coding assistant, we orchestrated 5 different AI models as a distributed team, each contributing their strengths. Every line of code, design decision, and video frame was created through this human-AI collaboration. This IS how work gets done in 2026.

Challenges we ran into

1. Balancing Intelligence with Compliance We initially wanted to use embeddings and live document retrieval, but had to balance innovation with copyright compliance. Solution: We focus on providing guidance about publicly available documents rather than redistributing LMA content. Our value is in the intelligence layer, not the documents themselves.

2. Avoiding the "Generic AI Assistant" Trap Early prototypes felt like ChatGPT with a search bar—too generic, too verbose. Challenge: How do we provide intelligence without replacing human judgment? Solution: We developed the three-part "Why this matters" framework that explains relevance, usage, and context without making decisions for the banker.

3. Making Complex Valuable to Non-Technical Judges Our judges are banking experts, not developers. Challenge: How do we demonstrate technical sophistication while keeping the demo accessible? Solution: We focused the demo on business value and user experience, with technical details reserved for the code repository.

4. The "Keyword Search is Fragile" Demo We wanted to show that traditional keyword search breaks with one typo. Challenge: How do we demonstrate this without looking like we're attacking LMA's existing system? Solution: We used it as a teaching moment—showing the problem, then the solution, with respect for the difficulty of the challenge.

5. Time Constraints with Distributed AI Team Coordinating 5 different AI models with different strengths and limitations required careful orchestration. Each AI had to understand context from previous conversations with other AIs. Solution: Chloe acted as the "AI-human interface translator," maintaining consistent vision across all collaborators.

Accomplishments that we're proud of

🎯 Solving a Real Problem We didn't build a solution looking for a problem. Every feature addresses a pain point we identified through research. The 25-minute → 3-second metric isn't marketing—it's based on actual banker workflows.

💡 The "Why This Matters" Innovation Most search tools stop at "here's the document." We invented a three-part framework (Relevance Summary + Section Guidance + Key Considerations) that provides deal intelligence, not just document links. This is what transforms search into insight.

🤖 Pioneering Human-AI Collaboration We proved that 2 humans can orchestrate 5 AI assistants to create production-quality work. Every line of code, every design decision, every video frame—created through deliberate human-AI collaboration. We're not just users of AI; we're demonstrating how the future of work actually works.

⚖️ Rigorous Copyright Compliance We navigated the complex challenge of providing value-added services on top of licensed content without violating IP rights. Our approach—focusing on guidance rather than content redistribution—could be a model for other startups in regulated industries.

📊 Quantifiable Impact We didn't just say "it's faster." We calculated:

  • £750 in document download costs (3 × £250)
  • £1,500 in wasted meeting time (6 people × £250/hr)
  • $6M in deal protection
  • Immeasurable reputational value

🎨 Professional-Grade Execution Despite being a hackathon project, we delivered:

  • Production-quality UI design
  • Comprehensive documentation
  • Professional demo video
  • Complete pitch deck
  • Market-ready positioning

We built this as if it were going to market tomorrow—because we believe it should.

What we learned

About the Problem Space:

  • Bankers don't need faster keyword search—they need search that understands intent
  • The real cost isn't time, it's deals lost when competitors respond faster
  • Compliance concerns aren't obstacles; they're design constraints that force better solutions
  • "Finding the document" is table stakes; "knowing what to do with it" is the real value

About Product Design:

  • Non-technical users don't care about embeddings or algorithms—they care about outcomes
  • "Deal intelligence" resonates more than "semantic search" with banking audiences
  • The three-part explanation framework (Why + How + Context) is surprisingly generalizable
  • Offline-first isn't just good for compliance; it's good for user trust

About AI Collaboration:

  • Different AI models have different strengths—Claude excels at architecture, ChatGPT at critique
  • The orchestrator role (Chloe) is crucial—AI collaboration needs a human "conductor"
  • Treating AIs as team members rather than tools yields dramatically better results
  • The future of work isn't "humans vs AI" or even "humans + AI"—it's "humans orchestrating AI teams"

About Hackathons:

  • Research matters more than code—we spent 40% of our time understanding the problem
  • Story matters more than features—judges need to feel the pain before they care about the solution
  • Constraints breed creativity—copyright compliance forced us to focus on intelligence, not content
  • Execution matters—a polished MVP beats a half-finished grand vision

Personal Growth:

  • Chloe discovered she's an "AI-human interface translator"—a role that didn't exist before
  • We proved that non-developers can create technical products through AI orchestration
  • The team learned that diverse perspectives (5 different AI models) prevent groupthink
  • We validated that relationship-building with AI assistants (naming them, understanding their styles) actually improves output quality

What's next for LMA Smart Search

Immediate Next Steps (Post-Hackathon):

  1. User Validation (Weeks 1-4) • Beta test with 10 relationship managers at Tier 1 banks • Measure actual time savings and deal impact • Iterate based on real-world feedback

  2. Technical Evolution (Months 1-3) • Implement embeddings-based semantic search (upgrade from curated intent index) • Expand document coverage from 9 to full LMA library (557 documents) • Add vector similarity scoring for more nuanced ranking • Integrate with internal document management systems

  3. Commercial Pilot (Months 3-6) • Secure partnership with one major bank for pilot deployment • Work with LMA on licensing model for document metadata • Develop enterprise deployment package • Build admin dashboard for compliance and usage tracking

Long-Term Vision (Year 1+):

Expand Beyond LMA Documents:

  • Internal deal templates and precedent agreements
  • Regulatory updates from FCA, ECB, etc.
  • Market intelligence and comparable deals
  • Become the single entry point for all loan market intelligence

Intelligence Features:

  • "Show me deals similar to this one" (comparative analysis)
  • "What's changed since last quarter?" (regulatory updates)
  • "What are competitors doing?" (market intelligence)
  • Predictive guidance: "Based on similar deals, watch out for X"

Enterprise Integration:

  • Salesforce integration for deal workflow
  • Microsoft Teams for collaboration
  • Bloomberg Terminal for market data
  • Single sign-on and compliance controls

Market Expansion:

  • APAC loan markets (adapt for regional practices)
  • U.S. CLO and syndicated loan markets
  • Adjacent markets: corporate bonds, structured finance

Business Model:

  • Freemium: Basic search free, advanced features paid
  • Enterprise licenses: $10K-$50K per bank annually
  • Usage-based pricing: Pay per search or per user
  • Strategic partnership with LMA as distribution channel

Metrics We'll Track:

  • Time saved per search (target: maintain 3-second average)
  • Deal conversion rate improvement (target: +15%)
  • User adoption and retention (target: 80% daily active users)
  • Net Promoter Score (target: 50+)

Ultimate Goal: Make LMA Smart Search the default way relationship managers interact with loan market intelligence—not just a tool they use, but the interface they can't work without.

Why We'll Succeed:

  • Real problem validated by banking professionals
  • Defensible through execution quality and relationship building
  • Scalable architecture ready for enterprise deployment
  • Team with proven ability to orchestrate human-AI collaboration
  • First-mover advantage in AI-powered loan market tools

We're not building a feature. We're building the future infrastructure of loan market intelligence.

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