Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://www.runcanary.ai
Diligence memoA one-page analyst read on Canary — recommendation, valuation, rhythm, risks.→Canary: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Canary 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 first AI QA engineer that understands your code
AI coding tools have made developers 100x faster but customer-facing incidents are up 43% YoY, QA hasn't kept up. Canary is the first AI QA engineer that understands your codebase. Instead of relying on flaky DOM scraping or screenshot analysis, Canary reads your source code directly to understand developer intent and catch broken user flows before they hit production. Engineering teams go from weeks of manual testing to 90%+ coverage in days. Built by a team that built coding agents at Windsurf, Cognition and Google.
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.
Canary 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 |
|---|---|---|---|---|
| Spur Spur is your AI QA Engineer. Test your websites with natural language. | AI / ML | — | — | 79% |
| Squire.ai Never code alone. | AI / ML | — | — | 77% |
| Waydev AI Track AI-generated code from commit to production | AI / ML | — | — | 77% |
| Outship Hire amazing engineers by seeing how they use AI | AI / ML | — | — | 76% |
| Decipher AI QA agents that write tests 10x faster with zero maintenance | AI / ML | — | — | 76% |
| Lark The E2E testing layer for AI-driven development | AI / ML | — | — | 76% |
| Prism Deploy AI Agents with a Single API Call | AI / ML | — | — | 76% |
| VortexifyAI Build AI applications and automations for supply chain operations | AI / ML | — | — | 75% |
See where Canary sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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