AirCapsFall 2025Active☆Watch⊞Compare

AirCaps is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2025. 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”

The AI copilot for in-person conversations.

AirCaps is bringing AI assistance to in-person conversations. Our AI-copilot provides live captions, translations, AI meeting notes and insights for in-person conversations in real-time. It's deployed as an app for lightweight AR glasses so you can see visual information overlaid onto your field of vision. You can think of it like a Zoom AI meeting assistant or Granola, but for in-person conversations, and with the ability to proactively help you in real-time, not just after the conversation. We’re building the capture and intelligence layer for the 216 billion daily conversations that happen face-to-face. We’ve already processed 16,500 hours of real-world conversations and counting. Today, AirCaps assists with 11% of our users’ in-person conversations. We did $93K in revenue in October and grew 6.5x from September while spending <$3K on marketing (and hit $70K in revenue in the first 2 weeks of November). Our power users average 6h+ daily usage and our day-30 retention is 91%. We previously went viral (75M+ views on TikTok, 150K+ followers) and have been featured by The New Yorker, WIRED, and Forbes. Madhav (CEO, Yale CS) has been building in audio and AR since age 13, starting with Google Glass apps and voice assistants for Raspberry Pis. He researched audio AI at the MIT Media Lab. Nirbhay (CTO, Cornell CS) built voice AI on smart glasses in high school and built conversational AI products as the first engineer at 2 YC startups.

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.

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.

AirCaps 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.

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.

See where AirCaps sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.

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