Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2026 · https://beesafe.ai/
Diligence memoA one-page analyst read on BeeSafe AI — recommendation, valuation, rhythm, risks.→BeeSafe AI: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
BeeSafe AI 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”
Frontier AI Defenses for Social Engineering Attacks
BeeSafe AI is building the frontier model layer for social engineering defense. Cyberattacks have moved from code to conversation: attackers now use AI to carry out long-term, trust-based manipulation across SMS, voice, email, and social platforms, often inside encrypted channels where defenders cannot see, detect, or stop them. BeeSafe changes that by deploying undercover AI agents that engage attackers directly, expose hidden infrastructure, and convert live adversarial conversations into actionable intelligence. Each engagement adds to BeeSafe's proprietary dataset of real attacker behavior and trains foundational models that continually learn about counter-persuasion, deception, and manipulation in the wild. BeeSafe is starting in financial crime, where banks already value our data against Authorized Push Payment (APP) fraud and money laundering, but the opportunity is far broader. The same data and models that uncover mule networks today can protect consumers, secure enterprises, and strengthen governments against the next generation of AI-powered social engineering attacks tomorrow.
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.
BeeSafe 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.
| 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 |
|---|---|---|---|---|
| Socratix AI AI coworkers for fraud and risk teams. | AI / ML | — | — | 75% |
| Ballerine Agentic Intelligence Layer for Merchant Risk Decisions | Insurance | — | — | 74% |
| GhostEye AI-Powered Social Engineering Defense | Cybersecurity | — | — | 74% |
| MindFort Autonomous Security Agents | AI / ML | — | — | 74% |
| Senso Control what AI says about you | AI / ML | — | — | 73% |
| Nessie A shared context layer for you, your team, and your agents. | AI / ML | — | — | 73% |
| BitPatrol AI-powered code security | Cybersecurity | — | — | 73% |
| BentoLabs AI Monitoring and learning layer for long-running agents | AI / ML | — | — | 73% |
See where BeeSafe 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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