Risely AISummer 2025Active☆Watch⊞Compare

Risely AI 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”

AI agents that automate administrative work across college campuses.

Risely’s AI agents will form the new operating system for universities, improving retention, staff productivity, and efficiency. Our platform integrates directly into existing university systems (SIS, LMS, CRM), learns each institution’s unique workflows, and deploys specialized AI agents. This dramatically reduces the $735 billion universities spend annually on administrative labor, expands staff capacity without increasing headcount, and improves the student experience. This hasn't been done before because university workflows require nuanced, autonomous judgment, exactly what agentic AI was designed to solve. Our team uniquely combines deep expertise in higher education, enterprise-scale AI, and regulated enterprise sales. Sadia previously built Salesforce’s Education Cloud from scratch, creating the core Student Information System widely used across higher education. She deeply understands university operations and maintains trusted relationships with hundreds of higher-ed CIOs and Provosts. Danial was one of DoorDash’s earliest engineers, where he architected and scaled complex systems from zero to billions of transactions, including their search, recommendation, and Voice AI platforms. He knows how to build robust AI solutions that reliably perform at enterprise scale. Shahryar was a GM at Airwallex, where he built the entire Americas business from 0 to 1 and drove 11x revenue growth. He’s also a master closer, single-handedly securing over $17M in enterprise deals at NerdWallet. He’s a relentless startup executor with deep expertise launching and scaling go-to-market machines in highly regulated industries. We launched six weeks ago and are already at $8k MRR, growing 55% week-over-week -- one of the fastest ramps ever in higher ed. Universities urgently need solutions that expand labor capacity and efficiency, especially as they face unprecedented budget pressure and staffing shortages. With unmatched buyer access, deep technical expertise, and rapid early traction, we are perfectly positioned to own this enormous market.

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.

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

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