Investors · alphabench

alphabench is revolutionizing quantitative research by creating the first autonomous AI assistant purpose-built for systematic investing. We are building the infrastructure that will power the next generation of quantitative strategies, democratizing institutional-grade research capabilities for individual researchers, teams, and institutions worldwide.

Investment thesis

The quantitative finance industry manages over $3 trillion globally, yet researchers still spend 80% of their time on data wrangling, infrastructure setup, and manual workflow orchestration instead of generating alpha. alphabench solves this fundamental inefficiency by providing an autonomous research assistant that transforms natural language ideas into complete, production-ready quantitative strategies.

Market opportunity: $50B+ addressable market across quantitative research, systematic trading, and financial data analytics, with growing adoption of AI across financial services.

Unique positioning: First-to-market autonomous quant research platform that combines conversational AI with deep domain expertise in systematic investing, backtesting, and risk management.

Why now?

Market dynamics

AI adoption acceleration: Financial institutions are rapidly integrating AI into their workflows, with 73% of hedge funds planning to increase AI investment in 2024-2025
Talent shortage: Severe shortage of quantitative researchers who can bridge finance, statistics, and engineering
Data complexity: Exponential growth in alternative data sources requiring sophisticated engineering to monetize
Regulatory pressure: Increasing demands for transparency, reproducibility, and risk management in systematic strategies

Technology enablers

Large language models: Advanced reasoning capabilities now enable AI to understand complex financial concepts and generate sophisticated research workflows
Cloud infrastructure: Scalable compute and storage make institutional-grade research accessible to smaller teams
Open finance: Growing ecosystem of APIs and data providers democratizing access to market data

Business model and revenue streams

Primary revenue streams

Individual SaaS subscriptions: $40 per user per month for individual researchers and small teams
Enterprise deployments: $200+ per user per month for dedicated deployment servers where companies can onboard and offload their quantitative workflows
Data partnerships: Revenue sharing with premium data providers and execution venues (15-25% revenue share)

Go-to-market strategy

Phase 1 (Current): Direct sales to individual quantitative researchers and small research teams
Phase 2 (3-6 months): Enterprise partnerships with hedge funds, asset managers, and family offices
Phase 3 (6-12 months): Platform partnerships with brokers, data vendors, and financial technology providers

Competitive advantages

Technical moat

Domain-specific AI: Proprietary models trained on quantitative finance workflows, not general-purpose automation
End-to-end platform: Only solution that covers the complete research lifecycle from data to deployment
Reproducibility engine: Built-in versioning, lineage tracking, and audit trails that competitors lack

Data and network effects

Knowledge accumulation: Each research workflow improves our models and expands our capability set
Ecosystem lock-in: Integration with data providers, brokers, and execution systems creates switching costs
Community-driven development: Open architecture allows users to contribute tools and methodologies

Execution advantages

Founder-market fit: Team combines deep quantitative finance expertise with AI/ML engineering excellence
Early traction: Strong product-market fit evidenced by rapid user adoption and enterprise pilot programs
Capital efficiency: Asset-light business model with high gross margins and scalable infrastructure

Financial projections and metrics

Key performance indicators

Annual Recurring Revenue (ARR): Target $500K by end of Year 1, $5M by Year 3
Customer acquisition cost: $200-$500 for individual users, $2K-$5K for enterprise accounts
Lifetime value: $2K+ for individual users, $50K+ for enterprise accounts
Gross margin: 85%+ at scale due to software-driven delivery model

Growth trajectory

Year 1: 500+ individual users, 10+ enterprise accounts, $500K ARR
Year 2: 2,000+ individual users, 50+ enterprise accounts, $2M ARR
Year 3: 5,000+ individual users, 100+ enterprise accounts, $5M ARR
Year 4: 10,000+ individual users, 200+ enterprise accounts, $10M ARR

Capital requirements

Seed/Pre-A: $1-3M for product development and initial go-to-market
Series A: $5-8M for enterprise sales scaling and platform expansion
Total capital to profitability: $10-15M over 2-3 years

Product roadmap and innovation pipeline

Near-term developments (6-12 months)

Enhanced execution: Direct integration with prime brokers and execution management systems
Alternative data: Expanded coverage of satellite imagery, earnings call transcripts, and social sentiment
Risk management: Real-time portfolio monitoring with automated hedging and rebalancing
Collaboration tools: Multi-user workspaces with role-based permissions and sharing controls

Medium-term vision (1-3 years)

Autonomous portfolio management: AI agents that can independently research, develop, and deploy trading strategies
Regulatory compliance: Built-in GDPR, MiFID II, and SEC reporting with automated documentation
Cross-asset expansion: Options market making, fixed income relative value, and commodity strategies
Institutional infrastructure: On-premises deployment, custom data lake integration, and white-label solutions

Long-term ambitions (3-5 years)

Financial AI operating system: Platform that powers systematic investing across all asset classes and time horizons
Democratization at scale: Making institutional-quality research accessible to individual investors and advisors
Global expansion: Localized versions for Asian, European, and emerging markets with regional data and regulations

Team and governance

Leadership team

Our founding team combines deep expertise in quantitative finance, artificial intelligence, and enterprise software, with previous experience at leading hedge funds, technology companies, and academic institutions.

Advisory board

We have assembled advisors from top-tier hedge funds, venture capital firms, and technology companies who provide strategic guidance on product development, go-to-market strategy, and fundraising.

Governance principles

Transparency: Regular investor updates with detailed metrics and forward-looking guidance
Alignment: Founder and employee equity incentives tied to long-term value creation
Accountability: Board-level oversight of key decisions and quarterly business reviews

Risk factors and mitigation

Market risks

Regulatory changes: Proactive engagement with regulators and built-in compliance frameworks
Technology disruption: Continuous R&D investment and strategic partnerships with AI research institutions
Competitive response: Strong IP protection and network effects that increase switching costs

Execution risks

Talent acquisition: Competitive compensation packages and equity participation for key hires
Customer concentration: Diversified customer base across industries, geographies, and use cases
Technical scalability: Cloud-native architecture designed for global scale from inception

Investment terms and next steps

We are raising a Series A round to accelerate product development, expand our go-to-market efforts, and establish strategic partnerships with leading financial institutions. We seek investors who bring not only capital but also industry expertise, customer relationships, and strategic guidance.

Ideal investor profile:

Tier 1 venture capital firms with enterprise software and financial technology expertise
Strategic investors from asset management, hedge funds, or financial data companies
Angel investors with quantitative finance backgrounds and strong industry networks

We invite qualified investors to engage with our team for detailed diligence materials, product demonstrations, and discussions about partnership opportunities.

Vision for the future

alphabench represents more than a software platform — we are building the infrastructure for the future of systematic investing. Our vision is a world where any researcher with a hypothesis can rapidly test, validate, and deploy sophisticated quantitative strategies without the traditional barriers of data access, technical complexity, or infrastructure costs.

By democratizing access to institutional-grade research capabilities, we believe alphabench will unlock unprecedented innovation in quantitative finance, leading to more efficient markets, better risk management, and superior investment outcomes for all market participants.

The quantitative revolution is just beginning. Join us in building the future of systematic investing.