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AI & Machine Learning

AI Company Valuation

When most of your value is in models, data, and talent, you need a valuation approach built for the AI era, not retrofitted from traditional methods.

50+

AI company valuations

$2B+

Enterprise value assessed

100%

Audit-ready reports

The Challenge

Why AI companies are different

Traditional valuation methods were built for asset-heavy businesses with predictable cash flows. AI companies require a fundamentally different lens.

Intangible Value Dominance

Most of an AI company's value lies in proprietary models, training data, and algorithmic IP. These are assets that traditional methods struggle to capture.

Data Asset Valuation

Proprietary datasets are often the moat. Valuing data quality, exclusivity, and defensibility requires specialized frameworks.

Team & Talent Premium

AI talent commands significant premiums. Key-person risk and team composition materially impact enterprise value.

Rapid Market Evolution

AI markets shift quickly. Comparable transactions from 12 months ago may already be outdated.

Our Approach

A framework built for AI value drivers

We combine deep technology understanding with rigorous valuation methodology to capture what actually drives value in AI businesses.

1

Technology Assessment

We evaluate your model architecture, training infrastructure, and technical differentiation against the competitive landscape.

  • Model performance benchmarking
  • Infrastructure and compute analysis
  • Technical moat assessment
  • Build vs. buy defensibility
2

Data Asset Analysis

Proprietary data is often the real value driver. We assess data quality, exclusivity, and strategic importance.

  • Data exclusivity and sourcing
  • Quality and labeling standards
  • Regulatory compliance (GDPR, etc.)
  • Data flywheel dynamics
3

Market Positioning

We map your position in the AI value chain and benchmark against relevant transactions and public comparables.

  • Horizontal vs. vertical AI positioning
  • Recent AI transaction analysis
  • Public company benchmarking
  • Market timing considerations
4

Revenue Quality

AI revenue models vary widely. We assess sustainability, scalability, and margin trajectory.

  • Recurring vs. services revenue mix
  • Customer concentration risk
  • Usage-based pricing dynamics
  • Gross margin trajectory
Use Cases

When you need an AI-focused valuation

Series A-C Fundraising

Valuation memos that speak the language of AI-focused investors

Acqui-hire Transactions

Team and IP valuation for talent-driven acquisitions

409A for AI Startups

Defensible strike prices that account for intangible value

Strategic M&A

Full enterprise valuation for acquisition or sale

IP Licensing Deals

Fair market value for model licensing and partnerships

Spin-off Transactions

Carve-out valuations for AI divisions

Building something intelligent?

Let's discuss how to value your AI company in a way that captures what makes it defensible and valuable.