Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://pavoot.com
Diligence memoA one-page analyst read on Pavoot — recommendation, valuation, rhythm, risks.→Pavoot: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Pavoot is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2026. By capital raised it ranks mid-pack (ahead of 62% of sector peers), and mid-pack by modeled valuation est..
Ranking is computed against this company's own sector cohort — reported capital is fact; valuation tiers are modeled.
AI analyst read est. — model-extracted from this company's public description, not a verified fact. 30%
operates a technology-led product inferred from public copy
Grounded in: “You are a venture analyst”
AI Event Manager for in-person events
Pavoot is an AI event manager for companies that run in-person customer events. More and more companies are investing in dinners, summits, and community events because relationships drive revenue. But today, running events means sourcing attendees, sending invitations, hosting the event, and then manually digging through notes, emails, and spreadsheets afterward to figure out who attended, what was discussed, who is a strong lead, and who should be followed up with. Most teams either send generic "thanks for coming" emails or never properly capture the value created during the event. Pavoot replaces this fragmented workflow with a single AI system that keeps every attendee, interaction, conversation, and follow-up connected in one place, making relationship-driven growth scalable. Gohar and Ana met while studying Computer Science at ETH Zurich. Gohar is a six-time Swiss chess champion and was the youngest member of Switzerland’s national chess team at age 13. Ana won more than 20 medals in scientific Olympiads, ranked 1st in CS at Latin America’s most competitive university, and built internal AI systems used by hundreds of data scientists at some of the largest banks in Latin America before moving to ETH Zurich for AI research. === Book a demo: pavoot.com
As reported in public records reported — not modeled.
Solid bars are reported offering amounts reported; hatched bars are the modeled post-money valuation est. — both on one shared scale so you can read raise-vs-worth at each round directly. Use the toggles to overlay data labels and the niche-peer / market average value lines.
No round amounts on record to chart.
No staged rounds to sequence.
Round size and date are reported; the stage label is inferred from round size. Valuation is modeled from stage benchmarks. Directional, not a quoted figure.
Not enough modeled valuation points to chart a trajectory.
Benchmarked against 2067 companies in AI / ML. Each bar is a median (the middle company, not an average — outliers don't skew it). Two yardsticks: real money raised (reported on Form D) and modeled value (our estimate est.). These are whole-sector medians across all stages, except the per-stage row.
Raised more than 62% of sector peers (real $). Modeled value above 62% of peers (estimate).
Stage is inferred from round size est., not reported on the filing — a round's dollar size maps to a bucket: Pre-Seed <$1.0M · Seed $1.0M–$4.0M · Series A $4.0M–$15M · Series B $15M–$40M · Series C $40M–$100M · Series D+ $100M–$400M · Growth/Late >$400M.
| Stage | Amount · real | Announced | Post-money · est | Value · est | Conf. |
|---|---|---|---|---|---|
| No rounds recorded. | |||||
Predictive signals are modeled est. from this company's own cadence and step-up, plus sector benchmarks — directional, not advice. Peer set and a CSV export live in your analyst workspace.
Pavoot is an official record sourced from the U.S. Securities and Exchange Commission (SEC). U.S. data is aggregated from SEC Form D filings.
Nearest neighbours across the whole database — matched on sector, stage and capital scale, and on shared operators (officers or directors named at both companies in public filings). A discovery shortlist, not a valuation cohort — verify before acting, the same way modeled figures are directional.
| Company | Sector | Stage | Raised · real | Value · est | Why similar |
|---|---|---|---|---|---|
| Accord | AI / ML | — | — | — | same sector |
| Acely | AI / ML | — | — | — | same sector |
| Aedilic | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Affogato AI | AI / ML | — | — | — | same sector |
| Aftercare | AI / ML | — | — | — | same sector |
| Agentic Labs | AI / ML | — | — | — | same sector |
| Ai Aiba | AI / ML | — | — | — | same sector |
Matched by meaning, not labels — a local language model reads each company's name, sector and description and ranks the closest in that learned space. This catches look-alikes that cross sector boundaries; the structured list above explains its matches, this one trusts the text. Directional, like every modeled signal here.
| Company | Sector | Stage | Value · est | Match |
|---|---|---|---|---|
| Conveo Confident decisions in days with AI-led interviews. | AI / ML | — | — | 74% |
| Venu AI Meet qualified leads in person by automating conference production | AI / ML | — | — | 74% |
| Popl Your Complete In-Person GTM Platform | AI / ML | — | — | 73% |
| Surface Labs Building the AI-powered marketing ops platform that turns website… | AI / ML | — | — | 73% |
| Artificial Societies We build networks of AI personas that simulate stakeholder opinions | AI / ML | — | — | 72% |
| Argovox Voice AI agents for patient billing and collections | Healthtech | — | — | 72% |
| Leaping AI AI voice and texting agents | Insurance | — | — | 72% |
| Pavo AI Inc. | AI / ML | Seed | $11.8M | 72% |
See where Pavoot sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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