Excellence LearningWinter 2025Active☆Watch⊞Compare

Excellence Learning is one of 262 Edtech companies tracked from Stanford, CA, USA, on record since 2025. By capital raised it ranks in the long tail (ahead of 21% of sector peers), and in the long tail 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 tutor starting with maths and science

Excellence an after-school maths and science program. We replace/supplement private tutoring and long-term study programs such as Kumon (over 30B TAM). US Parents are our primary customers. Our web and iPad software serves both ambitious and struggling students. As we believe each student is unique, we adapt our high quality courses and exercises to their knowledge and learning style in a very granular way. To facilitate learning, they get detailed text, voice and graphical explanations, can craft study plans, revise and memorize, and received human mentorship if needed. We believe LLMs have made edtech interesting, as technology now allows us to replicate a tutoring learning experience. In addition, we offer a program with daily courses and exercises, detailed feedback on their worksheets and more (super Kumon). Finally we sell the AI tutoring and exercise databases to schools in a B2B and B2B2C model and already work for the most prestigious institutions in France (Ecole polytechnique, college Stanislas...). We're a passionate team of ex-quants, mathematicians and physics champions devoted to improving science education.

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 262 companies in Edtech. 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 21% of sector peers (real $). Modeled value above 21% 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.

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

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