Companies · AI / ML
San Francisco · CA, USA; Remote · AI / ML · founded 2025 · https://www.legalos.ai
Diligence memoA one-page analyst read on LegalOS — recommendation, valuation, rhythm, risks.→LegalOS: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
LegalOS is one of 2067 AI / ML companies tracked from San Francisco, CA, USA; Remote, 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-Native Immigration Law Firm
LegalOS is an AI-native immigration law firm that combines cutting-edge technology with 40+ years of legal expertise. We’ve studied 12,000 successful petitions to design our process - which allows us to deliver top-quality visa applications in as quickly as 48 hours. Our specialized AI agents draft petition narratives, compile evidence, and anticipate USCIS objections - work that traditionally takes attorneys weeks, if not months. Every case is reviewed and signed by licensed immigration attorneys with 40+ years of experience, so companies get both speed and reliability. We support O-1, H-1B, L-1A, L-1B, TN, EB-1A, EB-1C, and EB-2 NIW petitions. Our unique approach works. We’ve filed dozens of visa applications and have a 100% approval rate so far. The U.S. processes 1M+ work visas annually—a market where incumbents still operate on fax machines, email, and 1990s case management software. We're building the modern immigration law firm.
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.
| Stage | Amount · real | Announced | Post-money · est | Value · est | Conf. |
|---|---|---|---|---|---|
| No rounds recorded. | |||||
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.
LegalOS 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.
| Company | Sector | Stage | Raised · real | Value · est | Why similar |
|---|---|---|---|---|---|
| Accord | AI / ML | — | — | — | same sector |
| Acely | AI / ML | — | — | — | same sector |
| Aedilic | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Affogato AI | AI / ML | — | — | — | same sector |
| Aftercare | AI / ML | — | — | — | same sector |
| Agentic Labs | AI / ML | — | — | — | same sector |
| Ai Aiba | AI / ML | — | — | — | same sector |
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.
| Company | Sector | Stage | Value · est | Match |
|---|---|---|---|---|
| Vector Legal A premier AI-native law firm & legal operating system for Startups. | AI / ML | — | — | 79% |
| Parley Automating flat-fee legal work, starting with work visas + green cards | Legaltech | — | — | 76% |
| Artos Authoring documents for life sciences in minutes, not months. | AI / ML | — | — | 76% |
| Lexi AI Associates for Corporate Law | Legaltech | — | — | 76% |
| Solve Intelligence AI patent drafting, prosecution, litigation and more for legal teams. | AI / ML | — | — | 75% |
| General Legal Elite AI law firm for high growth companies | AI / ML | — | — | 75% |
| Juro System Inc Intelligent contracting is here. Juro empowers your team to agree and manage contracts end-to-end with flexible AI automation that lives where you live. | Business Services | Growth/Late | $574.5M | 75% |
| ROSS Intelligence ROSS is an AI-powered legal research platform that can read and… | Legaltech | Pre-Seed | $3.1M | 75% |
See where LegalOS sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
If you work at LegalOS, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.
Is this your company? Update your profile or add contact details — and choose exactly who can reach you. Reviewed before anything is published.