VCs read decks with AI.
Make sure yours reads well.
Before a VC opens your deck, they paste it into their LLM and ask whether you're worth a meeting. If the AI can't answer "what do you do, why now, why you," the meeting was a pass before it was scheduled. deckllama runs the same workflow on your deck and shows you exactly what the AI took away.
Upload a PDF, get a score and the three biggest fixes. About two minutes.
Send us your PDF
Drag-drop or browse. 50 pages, 20 MB max. Stored encrypted, deleted on request.
The same 8 questions
We ask the questions every VC's analyst pastes into their AI: what do they do, what's the moat, who are the founders, what's the traction. Then we grade how clearly your deck answered them.
Score, fixes, red flags
A 0–100 score, a letter grade, the three things to fix first, and a tally of risks the AI flagged. About two minutes from upload to verdict.
Here's what you get back.
A score, a verdict, the three biggest fixes, and the red flags a partner will notice, laid out the way you'd want to read before walking into the meeting.
Value prop landed cleanly. Traction is where points were lost.
An LLM reading your deck cold answers some questions cleanly but asks follow-ups on others. The lowest-scoring sections below are where a partner is likeliest to walk in with the wrong mental model.
Add time-bounded growth metrics. "1,000 users" without a window is unreadable to an LLM as a traction signal. Show MoM/QoQ, cohort retention, or ARR with explicit periods.
Revenue projection assumes 40% free-to-paid conversion. Industry benchmark for similar B2B SaaS is 2–5%. The 8× delta is the first thing a partner will ask about.