FlootSummer 2025Active☆Watch⊞Compare

Floot 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 easiest way for non-coders to build apps with AI.

Floot lets non-coders & entrepreneurs build real web apps that actually work. We’re taking a new approach by building everything from the ground up for AI. ✨ Try building an app at floot.com Existing AI app builders fall short of enabling non-coders to build real, serious apps. They often: - Break down when building anything more than a prototype or toy app - Have non-coders stuck with errors they don’t understand - Require non-coders to handle multiple services designed for developers (e.g. for backend, database, hosting, etc) The fundamental problem: these AI app builders just cobble together existing developer tools and slap AI on top, but these tools were designed for humans and not for AI. The result? Non-coders get excited, start building, hit a wall, and then abandon their projects. Floot is purpose-built for AI. Our secret sauce: 1) Instead of reusing a coding framework designed for humans, we created a specialized framework that dictates how AI should write code and how the code runs on our infrastructure. This allows us to enforce best practices, detect errors earlier, and give the best feedback to the AI. As a result, Floot can write better code and fix issues more effectively. 2) Floot has the entire tech stack built-in (backend, database, hosting, etc) so building and managing an app feels seamless, especially for non-coders. And because we control every layer of the stack, we can optimize each layer for AI-first coding. Because of this, non-coders can get more done with less time and prompting. They can finally build serious apps that work, instead of getting stuck in frustrating error loops and then abandoning their projects. We also built a highly scalable hosting system directly on AWS so Floot apps can handle real traffic and business demands. This means non-coders can build, deploy, and scale their apps on a single platform seamlessly. Plus, Floot is really fun to use! Try building an app at floot.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.

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.

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

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