YAR Ventures · IC Prep
Pitch session prep.
Please select "Don't track" or write "N/A" If you don't track a metric.
01
Identity & contact
5 fields
Startup name
*
Founder name
*
Founder email
*
Founder phone / WhatsApp
*
Cofounder name
(if applicable)
Cofounder email
(if applicable)
02
Company profile & fundraising
6 fields
Website
*
Sector
*
Select sector
AI Infrastructure
Developer Tools
Enterprise Software
Fintech
Healthcare
Cybersecurity
Climate / Energy
Industrial / Manufacturing
Education
Consumer
Marketplace / Commerce
Biotech / Life Sciences
Other
Business model
*
Select business model
AI SaaS
AI Infrastructure
Vertical AI
Agentic AI
Foundation Model
AI Marketplace
AI Services (Hybrid)
Data & Intelligence
AI Hardware / Edge
Open Source + Commercial
Stage
*
Select stage
Pre-seed
Seed
Series A
Series B
Series C+
Bootstrapped
Other
Raised so far (USD)
*
Target raise (USD)
*
Valuation / SAFE cap
*
03
Pitch logistics
2 fields
Pitch session date
*
Select your confirmed session date
Friday, May 15, 2026 · 12:00 PM PST
Friday, June 19, 2026 · 12:00 PM PST
Friday, July 17, 2026 · 12:00 PM PST
Friday, August 21, 2026 · 12:00 PM PST
Friday, September 18, 2026 · 12:00 PM PST
Friday, October 16, 2026 · 12:00 PM PST
Friday, November 20, 2026 · 12:00 PM PST
Friday, December 18, 2026 · 12:00 PM PST
Pick the session Rosa confirmed you for
Pitch deck link
*
Share the exact deck version you want investors to review. Make sure link access is enabled.
04
Standard financials
8 fields
Updated ARR (USD)
*
Current annualized revenue
Updated customer count
*
Paying customers only — not LOIs
Gross margin %
*
Status
Measured
Estimated
Don't track
Not applicable
After COGS — include hosting + LLM costs
ACV (USD)
*
Status
Measured
Estimated
Don't track
Not applicable
Average annual contract value
Burn rate (USD/month)
*
Runway (months)
*
CAC (USD)
*
Status
Measured
Estimated
Don't track
Not applicable
Customer acquisition cost
LTV (USD)
*
Status
Measured
Estimated
Don't track
Not applicable
Customer lifetime value
05
AI cost structure
3 fields
Compute / LLM spend as % of revenue
*
Trajectory matters more than the absolute number. Use "N/A" if you weren't operating 6 months ago.
Inference cost per customer per month (USD)
*
Status
Measured
Estimated
Don't track
Not applicable
LLM API + compute cost to serve one customer for one month
Model dependency fallback plan
*
Select
Model-agnostic — could switch in 2–4 weeks
Moderate dependency — 3–6 months to migrate
Heavy dependency — would be very disruptive
Fully proprietary — no third-party dependency
If your primary LLM doubled in price, what happens?
06
AI defensibility
2 fields
Performance vs. baseline LLM
*
Select
Formal eval suite — we outperform baseline by measured X%
Qualitative lift — better but not formally benchmarked
Comparable to baseline — we win on UX/integration
Not applicable — we train our own models
How does your system perform vs. just calling GPT-4 / Claude directly?
Data flywheel — new labeled data per customer per month
*
Select type
Compounding — measurable model improvement
Static dataset — not actively growing
No proprietary data accumulation
Not applicable
A static dataset is replicable. A compounding flywheel is the moat.
Clear form
Submit IC prep
