Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://www.perfectly.so/
Diligence memoA one-page analyst read on Perfectly — recommendation, valuation, rhythm, risks.→Perfectly: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Perfectly is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2026. 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 Recruiting OS
Perfectly is the first AI-native Recruiting OS. Since starting YC, we’re already helping top startups like Giga, Corgi, LlamaIndex, Porter, and dozens more hire faster. We’re seeing: -> 4× faster hiring -> 10× candidate volume -> 2× higher interview pass rates Behind the scenes, it’s even more impressive: -> 20x higher recruiting efficiency: from 20 hours to 1 hour -> 250% higher candidate interview retention We automate the entire recruiting function: sourcing, outreach, screening, and qualification. Interview ready candidates just show up in Slack. We recently launched our recruiting agent Paul that provides each candidate white glove treatment so they're closed even before your offer call Hiring should work like a recommendation system. Learn more now at perfectly.so
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.
Perfectly 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 |
|---|---|---|---|---|
| Outrove Ultra-realistic AI Recruiter | AI / ML | — | — | 81% |
| Candidately The digital storefront for staffing and recruiting teams. | AI / ML | — | — | 80% |
| Standout Agentic hiring marketplace | AI / ML | — | — | 80% |
| Weekday AI recruiter that runs outbound sourcing campaigns to find top talent | AI / ML | — | — | 79% |
| Alex Your AI recruiter for interviewing and identifying the best talent | Hospitality / Travel | — | — | 79% |
| Spott AI-native ATS/CRM for recruiting firms | AI / ML | — | — | 77% |
| Hirebolt AI engineering capacity. Made simple. ⚡️ | AI / ML | — | — | 77% |
| OpenProse An open-source operating system for reliable long-running agents | AI / ML | — | — | 77% |
See where Perfectly sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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