pap!Summer 2024Active☆Watch⊞Compare

pap! is one of 1063 Fintech companies tracked from Berkeley, CA, USA, on record since 2024. By capital raised it ranks mid-pack (ahead of 66% 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”

Agents that earn consumers dollars when they shop, travel, and live.

Consumers miss out on $70B+ every year because they're busy and unaware, and that money is sitting in their email inbox. So, we automated reading email receipts and claiming the money they're entitled to, offering a service to which you can sign up once, do (literally) nothing, and earn $. Where is this money coming from? - Retailers: When the price of clothes or furniture you've already bought drops, you're entitled to a refund for the difference - Airlines: When a flight you've booked gets delayed or canceled, you're entitled to a cash compensation. - Enterprises: When a class-action lawsuit is filed against a company you engage with, you're entitled to money. - And many more, that even the government holds some of your unclaimed funds. Today, the dream of one service that automatically claims the money you deserve when you shop, fly,... and live is (finally) possible. We started with automating refunds in the shopping industry and have secured $8,000 in refunds to our users, growing 233% MoM. With AI, we've been able to repurpose that same automation to tackle refunds in the flight industry and the class-action lawsuit landscape. Why us? (1) We're obsessed with reclaiming what's rightfully yours: Kamal and Anthony grew up in Lebanon, where people's money was unfairly taken away. (2) Weijie was the lead infra engineer for Wish's recommendation system, the feature that fueled their IPO. Our combined experience includes Apple, Addepar, Bolt, Wish, and StackAdapt. And we're all working towards one goal: move those billions of dollars where they belong.

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 1063 companies in Fintech. 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 66% of sector peers (real $). Modeled value above 66% 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.

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

If you work at pap!, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.