VeriQuant
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🏆 LLM Agent Startup Competition 2025

VeriQuant

Advanced Mathematical Verification for Quantitative Finance

Mission: Address critical mathematical errors in quantitative finance through systematic formal verification, focusing on provable properties while acknowledging fundamental theoretical limits.

🌍 Massive Market Opportunity

$47B

Total Addressable Market

Global risk management software market by 2030

$12B

Serviceable Addressable

Quantitative finance software segment

$600M

Serviceable Obtainable

Our target market for formal verification

Market Driver: Increasing regulatory requirements (Basel III, CCAR, IFRS 17) mandate mathematical model validation, creating urgent demand for automated solutions.

📊 Market Research & Opportunity

Growing

Validation Challenges

Financial institutions face increasing difficulties with manual model validation processes

$Billions

Industry Model Failures

Documented losses from quantitative model errors across the financial sector

Research

Market Interest

Active development and research into automated verification solutions

Demand

Solution Interest

Strong interest from institutions for reliable automated verification technology

Market Research: Our ongoing research reveals significant unmet demand for mathematical verification solutions in quantitative finance.

⚠️ Critical Problems We Solve

🔥 Model Validation Failures

Manual review processes miss critical mathematical errors, leading to:

  • Billion-dollar trading losses
  • Regulatory violations and fines
  • Systemic risk exposure
  • Reputation damage

💰 Massive Compliance Costs

Financial institutions spend millions annually on:

  • Manual model validation teams
  • External audit firms
  • Regulatory compliance processes
  • Risk management overhead

⏱️ Scalability Crisis

Manual processes cannot keep pace with:

  • Increasing model complexity
  • Growing regulatory requirements
  • Real-time validation needs
  • Multi-asset coverage demands

🎯 Accuracy Limitations

Human reviewers struggle with:

  • Complex mathematical proofs
  • Edge case identification
  • Consistency across models
  • Deep technical validation

🚀 Our LLM Agent Solution

Revolutionary Approach: We combine Large Language Models with formal verification techniques to create autonomous AI agents that mathematically prove the correctness of quantitative finance models.

🧠 Mathematical Reasoning Agent

Building domain-specific LLM capabilities for mathematical reasoning in quantitative finance literature, capable of understanding complex financial models and their underlying mathematics.

✅ Formal Verification Agent

Converts models into Lean 4 theorem prover specifications, targeting verifiable mathematical properties with formal guarantees within the scope of what can be proven mathematically.

🔍 Error Detection Agent

Continuously monitors model execution, detects anomalies, and provides detailed analysis of potential mathematical inconsistencies.

📋 Compliance Agent

Ensures models meet regulatory requirements by validating against Basel III, CCAR, and IFRS 17 mathematical frameworks.

⚡ Technical Innovation & Development

🔬 LLM Research

Developing novel fine-tuning approaches for mathematical reasoning using formal verification feedback.

  • Building mathematical proof benchmarks
  • Domain-specific reasoning research
  • Working toward verification accuracy
  • Exploring production architecture

🔗 Formal Methods Research

Researching integration between LLMs and Lean 4 theorem prover for automated mathematical verification.

  • Developing proof generation
  • Building verification algorithms
  • Optimizing response times

🏗️ Platform Architecture

Designing production-ready system with enterprise integration capabilities.

  • Planning RESTful APIs
  • Researching platform connectors
  • Exploring integration options
  • Designing deployment models

📈 Development Progress

Validated approach through prototype development and technical research.

  • Prototype demonstration complete
  • Building verification benchmarks
  • Architecting enterprise platform
  • Developing formal proof capabilities

🏆 Competitive Advantage

Unique Position: We are developing the first solution combining LLM intelligence with formal mathematical verification for quantitative finance, with deep understanding of both capabilities and fundamental limitations.

Traditional Validation

Manual review processes:

  • ❌ Error-prone
  • ❌ Slow (weeks/months)
  • ❌ Expensive ($5M+ annually)
  • ❌ Not scalable
  • ❌ No mathematical guarantees

Rule-Based Systems

Legacy automated tools:

  • ⚠️ Limited scope
  • ⚠️ Brittle rule sets
  • ⚠️ High false positive rates
  • ⚠️ Cannot handle complexity
  • ⚠️ Require manual updates

VeriQuant (Prototype Ready)

LLM + Formal Verification:

  • ✅ Mathematical verification for provable properties
  • 🚀 Demonstrable capabilities within verification scope
  • 💡 Cost-effective solution targeting verifiable aspects
  • ⚡ Scalable architecture designed for formal methods
  • 🎯 Rigorous approach acknowledging theoretical boundaries
🎥 View Live Demo

💼 Future Business Model

💰 Planned Revenue Streams

  • SaaS Subscriptions: Subscription-based platform access
  • Enterprise Licenses: Large institution deployments
  • Professional Services: Implementation and customization
  • API Usage: Pay-per-verification for smaller firms

📊 Target Economics

  • Market Research: Studying enterprise pricing models
  • Strategy Development: Building go-to-market approach
  • Revenue Model: SaaS subscription focus
  • Margin Potential: High-margin software opportunity
  • Retention Focus: Mission-critical platform positioning

🎯 Development-Stage Strategy

  • Tier 1 Banks: Research partnerships
  • Asset Managers: Early feedback programs
  • Hedge Funds: Prototype validation
  • Consultancies: Exploring partnerships

💡 Competitive Development

  • Technical Approach: Novel LLM + formal verification
  • Research Focus: Mathematical reasoning capabilities
  • Development Team: Building domain expertise
  • Market Position: First-mover advantage potential

📈 Development Roadmap

Development Timeline

  • Year 1: MVP launch, design partners
  • Year 2: Early commercial validation
  • Year 3: Product-market fit development
  • Year 4: Market expansion planning
  • Year 5: Category leadership potential

Development Metrics

  • Technical Milestones: Core platform capabilities
  • Market Validation: Proving customer demand
  • Team Building: Domain expertise acquisition
  • Partnership Development: Strategic alliances
Strategic Opportunity: Building the foundational technology to create a new category in quantitative finance verification with significant long-term market potential.

👥 Building Our Team

🧑‍💼 Leadership Development

  • Founder/CEO: Building quantitative finance and AI expertise
  • CTO Role: Seeking formal verification and LLM specialists
  • Head of Product: Planning enterprise platform development
  • Head of Sales: Future enterprise sales leadership

🔬 Technical Team Building

  • ML Engineers: Recruiting for LLM and mathematical reasoning
  • Formal Methods Experts: Seeking theorem proving specialists
  • Quant Developers: Building financial modeling expertise
  • DevOps Engineers: Planning cloud infrastructure team

🎓 Advisory Board Development

  • Financial Risk Experts: Seeking former regulatory leaders
  • Formal Verification Pioneers: Academic partnerships
  • Financial Technology Leaders: Industry advisors
  • Venture Capital Partners: Strategic investor guidance

🎯 Hiring Strategy

  • Target Team Size: 15-25 people by end of Year 1
  • Engineering Focus: 60% of headcount
  • Business Development: 25% of headcount
  • Operations: 15% of headcount

🎥 Product Development & Progress

🔴 Prototype Demo

See our early VeriQuant prototype in development:

  • Mathematical verification concepts
  • Proof-of-concept demonstrations
  • Early algorithm development
  • Technical architecture planning
🚀 Interactive Demo 📹 Video Demo

📊 Current Development Status

  • Market Research: Surveying financial institutions
  • Technical Development: Building proof-of-concept platform
  • Regulatory Research: Understanding compliance frameworks
  • Partnership Exploration: Institutions expressing early interest
  • Current Focus: MVP development and team building
Development Stage: Building breakthrough technology with strong early validation and clear path to market disruption.

💰 Seed Funding Request

Seeking $2-5M Seed Round to Build MVP

💵 Use of Funds

  • Product Development (50%): Core MVP and research team
  • Team Building (30%): Key technical hires
  • Market Research (15%): Customer validation and partnerships
  • Operations (5%): Basic infrastructure and legal

🎯 Next 18 Months

  • MVP Development: Working prototype for demonstrations
  • Team Growth: 8-12 engineers and domain experts
  • Pilot Programs: 3-5 research partnerships
  • Technical Milestones: Core verification capabilities
  • Series A Preparation: Traction for next funding round
Investment Opportunity: Early-stage investment in breakthrough technology with significant long-term market potential in quantitative finance.

⚖️ Risk Analysis & Mitigation

🎯 Market Risks

  • Regulatory Changes: Mitigated by direct regulator engagement
  • Economic Downturn: Our solution reduces costs, increases demand
  • Competition: 2-3 year technical lead provides protection

⚡ Technical Risks

  • LLM Accuracy: Formal verification provides mathematical guarantees for verifiable properties
  • Scalability: Cloud-native architecture handles growth
  • Integration: Standard APIs ensure compatibility

👥 Development Risks

  • Talent Acquisition: Building competitive compensation strategy
  • Technology Development: Complex technical challenges ahead
  • Market Validation: Proving demand through research partnerships

🛡️ Risk Mitigation

  • Technical Expertise: Building team with right domain knowledge
  • Market Research: Validating demand before full development
  • Strategic Partnerships: Early customer relationships

⏰ Why Now? Perfect Market Timing

🏛️ Regulatory Tailwinds

  • Basel III Implementation: Increased model validation requirements
  • CCAR Stress Testing: Annual mathematical proof requirements
  • IFRS 17 Adoption: New insurance valuation standards
  • Climate Risk Regulations: New model validation needs

🤖 Technology Convergence

  • LLM Maturity: GPT-4 class models enable mathematical reasoning
  • Formal Verification: Lean 4 makes proofs accessible
  • Cloud Computing: Scalable infrastructure available
  • API Integration: Modern financial systems support automation

💥 Real-World Pain Points

  • 2008 Financial Crisis: Model risk led to trillion-dollar losses
  • London Whale (2012): JPMorgan lost $6.2B from VaR model errors
  • Credit Suisse (2021): Archegos collapse from inadequate risk models
  • FTX (2022): Risk management failures exposed systemic issues

✅ Technology Validation

  • DeepMind AlphaProof: Mathematical theorem proving breakthrough
  • Microsoft Lean 4: Formal verification in production systems
  • Academic Research: Growing validation of LLM mathematical reasoning
  • RegTech Growth: $12.3B market solving compliance challenges
Unique Window: The convergence of regulatory pressure, technological capability, and proven market demand creates a once-in-a-decade opportunity for disruption.

🚀 Join the Revolution

LLM Agent Startup Competition 2025

VeriQuant is transforming quantitative finance through mathematically rigorous AI agents.
Be part of the future where verifiable properties of financial models are systematically proven.

📧 Contact Us

Ready to invest or partner?

jason@veriquant.co

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Thank you for your time and consideration. Questions?