SimVC is a fundraising rehearsal tool for founders. Upload a pitch deck, run it through 150–300 simulated investors matched to your stage and sector, and get ranked pass reasons, diligence questions, slide-level feedback, and honest limits.
A startup from Vienna, Austria that is founded by Patrick Jaritz.
This page is designed to help you find out whether SimVC is good and if it is the right choice for you.
Investors rarely tell founders the real reason they passed. The email says “not a fit for our thesis.” The useful information — missing proof, unclear wedge, weak traction story, category confusion, round-shape mismatch — stays hidden.
SimVC gives founders a rehearsal room before the real room. Upload a pitch deck and SimVC runs a multi-round cohort simulation across 150–500 distinct investor entities: cold read, round 1 and round 2, with rounds that conditionally skip and say so instead of faking a neat three-act story. The cohort is composed of psychology-grounded investor archetypes, not named real firms or celebrity personas, and stage is load-bearing: angel, pre-seed and seed rooms are judged against different bars.
The core deliverable is the output triad founders can act on: ranked pass reasons, a diligence dossier, and an interactive partner-meeting drill. The system can also use sourced-or-null benchmarks, including company and grant-context data, and it has a grant-fit companion for deterministic eligibility checks plus reviewer-panel evaluation against published program criteria.
It is deliberately bounded. SimVC does not predict fundraising success, does not emit a funding or award probability, and does not pretend to reproduce a specific real investor. Guardrails scrub banned entities from founder-visible strings, contract checks watch for genericness or invalid output, and adaptive delivery/repair paths are designed to survive LLM failure modes instead of silently degrading.
The free /deck-check flow gives a structural read before signup. Deeper reports are designed for founders who have an actual raise, grant application or investor conversation coming up and want structured reasoning before spending a scarce meeting.
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Cohort Analysis
Multi-round cohort simulation engine — not one LLM opinion, but 150–500 distinct investor entities run through a real funnel (cold read → round 1 → round 2), with rounds that conditionally skip and say so, rather than faking a three-round arc.
Archetypes
Psychology-grounded archetypes, not celebrity personas — the cohort is composed of distinct investor decision styles, deliberately scrubbed of any named real firm or person.
Evidence-Based
Evidence-grounded, sourced-or-null benchmarks — real benchmark data (incl. EDGAR sync), with a hard honesty rule: a figure is sourced or it's null, never fabricated-as-real.
SimVC is for founders preparing for consequential investor conversations or grant applications, especially angel, pre-seed, seed and early-stage SaaS founders with a pitch deck and a limited number of real shots. The best-fit user is not looking for prettier slides; they already have a story and want to know what evidence, framing, or diligence questions could break it in the room.
SimVC is not a single LLM opinion or a generic deck score. It runs a founder’s pitch deck through 150–500 psychology-grounded investor entities in a multi-round, stage-aware funnel: cold read, follow-up rounds, and conditional skips that say so instead of faking a neat arc. The output is structured investor reasoning — ranked pass reasons, a diligence dossier, partner-meeting drills, sourced-or-null benchmarks, and hard guardrails that refuse funding probabilities or named-investor impersonation.
Choose SimVC if you want fundraising rehearsal, not presentation polish or fake certainty. Many alternatives help design slides, summarize a deck, or produce a score. SimVC is built to answer a sharper question: “What would different investor psychologies object to before I spend a real meeting?” It is stage-aware, uses psychology-grounded archetypes instead of celebrity personas, refuses fabricated benchmarks, and turns the result into actions: pass reasons to fix, diligence questions to prepare, and drills for the partner-meeting conversation.
SimVC came from a simple fundraising frustration: investor passes are polite, but rarely useful. “Not a fit for our thesis” might be professional, but it does not tell a founder whether the real problem was market size, traction evidence, round shape, category framing, missing benchmarks, or one unanswered diligence question. SimVC was built to move that learning earlier. Instead of waiting for real investors to reveal the weak spots after a scarce meeting is spent, founders can rehearse against simulated investor decision styles, see the likely objections, and revise while there is still time.
SimVC is built as a web SaaS with a Flask/Python backend and a Next.js frontend. The simulation layer uses multiple LLM model families routed by role: stronger synthesis models for hard reasoning and workhorse models for simulation volume. It also uses deck parsing/classification, metered run infrastructure, Stripe payments, consent-gated analytics, a token-accurate spend ledger, GDPR deletion flows, and sourced-or-null benchmark data including real funding round context where relevant.
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