AI / ML Startup Pitch
Model advantages, data moats, and responsible AI
How to pitch an AI company in 2026 — proving your model advantage isn't just GPT with a wrapper, demonstrating data moats, showing responsible AI practices, and navigating the 'what if OpenAI does this?' question.
Cover Slide
Company, tagline, key differentiator.
💡 What to Include
- Don't lead with 'AI-powered' — lead with the outcome
- Mention if you have proprietary models or data
⚠️ Common Mistakes
- 'AI-powered [X]' tagline
- Leading with the technology instead of the value
📝 Example
MedScan — Radiology results in 30 seconds, not 30 days. Proprietary model trained on 2M clinical scans | Series A, $10M
The Problem
Show the problem AI solves — focus on the human impact.
💡 What to Include
- Start with the real-world consequence, not the technical gap
- Quantify time/cost/error rates of current approach
- Show why traditional software can't solve this
- Make it tangible with a specific scenario
⚠️ Common Mistakes
- Problem is 'AI doesn't exist for X' (circular)
- No quantification of current pain
- Too technical for non-expert investors
📝 Example
Radiologists review 20,000+ images per year. Average time per study: 15 minutes. Error rate: 3-5% (meaning 600-1,000 misdiagnoses per radiologist per year). Wait time for patients: 5-14 days for results. 1 in 3 radiologists reports burnout. The US faces a shortage of 5,000 radiologists by 2028.
The Solution & Product
Show what the AI does — in plain language first.
💡 What to Include
- Explain it to a smart non-technical person first
- Show the workflow: before AI vs. with AI
- Include actual product screenshots
- Highlight the human-in-the-loop aspect (AI assists, doesn't replace)
⚠️ Common Mistakes
- Claiming AI 'replaces' experts
- No product screenshots
- Only describing the model, not the product
📝 Example
MedScan analyzes radiology images in 30 seconds and highlights potential findings for the radiologist. Before: Radiologist manually reviews every image (15 min/study) With MedScan: AI pre-screens and prioritizes urgent cases, highlights anomalies, radiologist confirms in 2 minutes Result: 7x faster turnaround, 92% reduction in missed findings.
Why Your AI is Different
Address the 'What if OpenAI/Google does this?' question.
💡 What to Include
- Show your unique data advantage
- Explain why general models fail for your use case
- Demonstrate domain-specific performance vs. general AI
- Show your proprietary training data pipeline
⚠️ Common Mistakes
- No comparison vs. foundation models
- Claiming your AI is 'better' without benchmarks
- No data moat explanation
📝 Example
Why GPT-4 / general vision models fail here: • Medical imaging requires sub-millimeter precision (general models miss 40% of findings) • HIPAA compliance means data can't go to third-party APIs • Domain-specific: our model trained on 2M labeled clinical scans (vs. web-scraped images) MedScan accuracy: 96.3% sensitivity vs. 57% for GPT-4V on the same dataset. Our data moat: Exclusive partnerships with 12 hospital systems for labeled training data.
Market Size
Size the market for the AI application, not 'AI' generally.
💡 What to Include
- Size the application market, not the AI market
- Show current spend on the problem you're solving
- Include regulatory tailwinds if applicable
⚠️ Common Mistakes
- 'The AI market is $500B'
- Not scoping to your specific application
- Mixing AI infrastructure and application TAMs
📝 Example
Clinical decision support market: $4.2B (2025) → $12B by 2030 Radiology AI specifically: $1.8B, growing 35% CAGR US healthcare system wastes $200B/year on diagnostic inefficiencies MedScan targets: 6,000 radiology practices × $48K avg. annual contract = $288M near-term SAM
Business Model
Show sustainable AI economics.
💡 What to Include
- Show inference costs and how they decrease over time
- SaaS pricing tied to value delivered, not API calls
- Demonstrate gross margin (GPU costs are real)
- Show model improvement flywheel
⚠️ Common Mistakes
- Not showing inference costs
- Pricing per API call (unpredictable for customers)
- Gross margin below 60% without a plan to improve
📝 Example
Pricing: $4,000/month per radiology site (unlimited scans) Inference cost: $0.03/scan (optimized for edge deployment) Gross margin: 78% (and improving as model efficiency increases) Value to customer: $4K/month saves $15K/month in radiologist time → 3.75x ROI Flywheel: More scans → Better model → Higher accuracy → More customers → More scans
Traction
Prove the AI works in the real world, not just benchmarks.
💡 What to Include
- Show clinical/production deployments, not just pilot data
- Revenue traction matters more than model accuracy
- Show customer retention and expansion
- Include regulatory approvals if applicable
⚠️ Common Mistakes
- Only benchmark accuracy, no real-world deployment
- Pilot customers who don't pay
- No regulatory status information
📝 Example
12 hospital systems deployed (from 2 last year) ARR: $1.8M (growing 20% MoM) 3.2M scans analyzed in production $0 customer churn (100% renewal rate) FDA 510(k) cleared for chest X-ray analysis Customer quote: 'MedScan reduced our average reporting time from 8 days to same-day.'
Responsible AI & Trust
Show you take AI safety and ethics seriously.
💡 What to Include
- Explain your approach to bias testing
- Show explainability features
- Address data privacy and security
- Include your AI governance framework
⚠️ Common Mistakes
- Ignoring bias/fairness
- No explainability story
- Handwaving on data privacy
📝 Example
Bias testing: Model validated across 14 demographic groups, <2% variance in accuracy Explainability: Every AI finding includes a visual heatmap showing what the model detected Privacy: On-premise deployment option, HIPAA compliant, SOC 2 Type II Governance: AI Ethics Board including 2 external clinicians and 1 patient advocate
Competition
Show your technical and commercial edge.
💡 What to Include
- Compare on accuracy, speed, deployment model, and data advantage
- Address both AI startups and incumbent software vendors
- Show switching costs once deployed
⚠️ Common Mistakes
- Ignoring big tech AI efforts
- Not comparing accuracy head-to-head
- Claiming no competition in AI
📝 Example
Aidoc: Strong in ER triage, limited to acute findings, cloud-only Viz.ai: Stroke detection only, narrow use case Epic/Cerner: Adding basic AI but 5 years behind on accuracy MedScan: Only solution with on-premise + cloud, covering 12 radiology subspecialties, 96%+ accuracy across all.
Team
AI + domain expertise is non-negotiable.
💡 What to Include
- Show ML publication track record
- Domain expertise (clinicians, industry veterans)
- Show the team can ship product, not just research
- Include data partnerships and advisory board
⚠️ Common Mistakes
- All ML, no domain expertise
- All domain, no ML expertise
- Research team that can't ship product
📝 Example
CEO — Radiologist + ML researcher, 12 publications in medical AI CTO — Ex-Google Brain, built production ML systems at scale VP Clinical — Former CMO of radiology practice (200 radiologists) 8 ML engineers (avg. 6 years experience), 3 clinical advisors 27 peer-reviewed publications from the team
The Ask
Tie funding to model improvement and market expansion.
💡 What to Include
- Budget for GPU/compute costs explicitly
- Show the data acquisition plan
- Map to regulatory milestones
- Show the path from narrow to broader AI
⚠️ Common Mistakes
- Not budgeting for compute
- No regulatory milestone plan
- Not showing expansion beyond initial use case
📝 Example
Raising $10M Series A • R&D / Compute (40%): Expand model to 8 new subspecialties • Sales & Marketing (30%): Hire enterprise sales team, 50 hospitals target • Regulatory (15%): FDA clearances for 4 new modalities • Operations (15%): SOC 2, security infrastructure Milestones: $8M ARR, 50 hospitals, 5 FDA clearances, 10M scans analyzed
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