The AI Boom: Hype vs Durable Value

Over the last 24–36 months, AI has transitioned from experimentation to infrastructure. However, this acceleration has created two parallel realities:

1. AI-native, deeply engineered companies building real advantage

2. AI-layered startups adding superficial automation without defensibility

Traditional due diligence frameworks were designed for SaaS, marketplace, or hardware businesses. AI startups demand a more nuanced evaluation model. At Jaipur.VC, we believe AI diligence must go beyond revenue and traction — it must evaluate:

• Data advantage

• Model defensibility

• Compute economics

• Regulatory exposure

• Founder-level technical depth

• Ethical and governance risks

Why AI Due Diligence Must Evolve

In conventional startups, investors ask:

• Is there a problem?

• Is there traction?

• Is there a scalable GTM model?

In AI startups, additional questions become critical:

• Is AI the core engine or a cosmetic feature?

• Is there proprietary or hard-to-replicate data?

• How dependent is the startup on third-party foundation models?

• Are inference costs sustainable at scale?

• What happens if platform APIs change pricing or access?

• Is the founder technically capable of evolving the system as AI progresses?

AI startups can grow very fast — but they can also fail quickly if their technology or business model is weak. So, we’ve created a simple framework to help you evaluate AI startups and make better investment decisions.

AI-Focused Due Diligence Questionnaire

1. Problem, Pain & Market Reality (Investor Signal: Real Pain, Real Buyer)

Problem Definition

• What specific, high-frequency problem are you solving?

• Who feels the pain most, vs who pays for the solution?

• What happens if this problem remains unsolved in the next 3–5 years?

Problem Validation

• How did you discover the problem (first-principles, founder insight, customer pull)?

• Number of customer discovery conversations conducted

• Evidence of budget ownership by the target customer

• How is the problem currently solved (manual, legacy software, human labor)?

Market Readiness

• Is this problem AI-native or AI-augmented?

• Why is now the right time (data availability, regulation, compute, cost curves)?

• What technological or structural changes have enabled your solution now?

2. Solution & AI Architecture (Investor Signal: Defensibility + Scalability)

Product Overview

• Describe the solution and where AI is used (core engine vs feature).

• What decisions or outcomes does AI materially improve?

AI Stack & Design

• Model type: LLM / CV / NLP / Predictive / Hybrid

• Build vs Buy vs Fine-tune strategy

• Dependency on third-party models (OpenAI, Anthropic, Azure, AWS, open-source)

• Inference & training cost per customer/transaction

Data Advantage

• Source of data (owned, licensed, synthetic, public)

• Data moat: why competitors can’t replicate it easily

• Data freshness, quality, and bias mitigation approach

Performance & Validation

• Key accuracy/outcome metrics (vs human / baseline)

• Results from pilots, PoCs, or production deployments

• Model monitoring & retraining strategy

3. Customer & Buyer Economics (Investor Signal: Willingness to Pay)

Customer Segments

• ICP definition (size, industry, geography)

• Buyer vs user vs beneficiary clarity

• Enterprise vs SMB vs Govt vs Consumer

Buying Process

• Typical sales cycle length

• Decision makers and blockers

• Procurement/regulatory friction (especially in healthcare, BFSI, govtech)

Adoption & Stickiness

• Switching cost vs status quo

• AI explainability & trust barriers

• Customer onboarding and learning curve

4. Product Readiness & Go-to-Market (Investor Signal: Repeatable Growth)

Product Stage

• MVP / Beta / Live / Revenue-generating

• Deployment model (SaaS, API, On-prem, Hybrid)

Go-to-Market Strategy

• Founder-led vs sales-led vs partner-led

• Channel partnerships (SI, OEM, platforms)

• Customer acquisition cost (CAC) drivers

Retention & Expansion

• Usage-based stickiness

• Upsell / cross-sell levers

• Churn drivers and mitigation

5. Competitive Landscape & Defensibility (Investor Signal: Moat Over Time)

Competition

• Direct AI competitors

• Indirect (human labor, Excel, ERP modules, offshore services)

Differentiation

• Why you win vs incumbents and fast followers

• Speed of iteration advantage

• Network effects/ecosystem lock-in

Defensibility

• Data moat

• Workflow embedding

• Switching costs

• Regulatory or domain complexity moat

6. Business Model & Unit Economics (Investor Signal: Path to Profitability)

Revenue Model

• SaaS / Usage-based / Outcome-based / Licensing

• AI cost pass-through vs absorbed

Pricing

• Pricing logic vs customer ROI

• Sensitivity to AI compute cost volatility

• Will margins improve as you scale?

Unit Economics

• Gross margin today vs at scale

• Cost drivers: compute, data, people, sales

• Break-even assumptions

7. Technology, Security & Compliance (Investor Signal: Risk Management)

Tech Stack

• Cloud infrastructure dependency

• Vendor concentration risk

• System scalability & uptime

Security & Privacy

• Data security standards followed

• PII / PHI handling (HIPAA, DPDP, GDPR, ABDM, etc.)

• AI governance & audit readiness

Regulatory Exposure

• Sector-specific regulations

• AI compliance & explainability requirements

• Risk of regulatory disruption

8. Team & Execution Capability (Investor Signal: Founder-Market Fit)

Founding Team

• AI, domain, and execution balance

• Prior startup or scale experience

• Ability to hire top AI talent

Ownership & Commitment

• Founder equity & skin in the game

• Key-person risk

Adaptability

• Willingness to pivot based on market or regulation

• Speed of learning & iteration

9. Financials, MIS & Operations (Investor Signal: Control & Discipline)

Financial Readiness

• Audited or management accounts

• Burn rate & runway

• AI infrastructure cost forecasting

MIS

• Product analytics

• Sales pipeline visibility

• Model performance monitoring

10. Funding, Capital Efficiency & Use of Funds (Investor Signal: Capital Leverage)

Funding History

• Capital raised (equity, grants, debt)

• Non-dilutive capital leveraged

Use of Funds

• % allocation to product, AI, sales, hiring

• Clear milestones tied to capital

Capital Efficiency

• Revenue per employee

• Time to next inflection point

11. Impact, Ethics & Responsible AI (Especially for Health, Climate, GovTech)

• Intended social or systemic impact

• Responsible AI principles followed

• Bias, fairness, and explainability safeguards

• Long-term societal risk considerations

12. Exit Readiness (Optional, Later Stage)

• Natural acquirers (strategic buyers)

• Platform dependency risks

• IPO vs acquisition plausibility

At Jaipur.VC, we remain committed to evolving our frameworks alongside technological advancement — ensuring capital meets true capability. We strongly recommend that AI startup evaluations be conducted in collaboration with a qualified technical expert to provide deeper clarity on architecture, data strategy, model dependencies, and long-term defensibility.

For feedback, comments, or to engage with our framework, please write to venture@jaipur.vc