Companies · Insurance
San Francisco · CA, USA · Insurance · founded 2025 · https://www.leapingai.com
Diligence memoA one-page analyst read on Leaping AI — recommendation, valuation, rhythm, risks.→Leaping AI: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Leaping AI is one of 146 Insurance companies tracked from San Francisco, CA, USA, on record since 2025. By capital raised it ranks mid-pack (ahead of 68% 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 voice and texting agents
Leaping AI is the best platform to deploy voice and texting AI agents for customer service, lead qualification and appointment scheduling. We target mid-market and Enterprise companies and industries are: home remodeling, roofing, pay per call, travel, retail, health insurance, Medicare, health and real estate. We automated contact centers employing >100 human agents and now handle their incoming customer support and appointment scheduling calls, end-to-end. All while maintaining 90% customer satisfaction score. Pain points that we solve: missed calls, insufficient speed to lead, not enough call center resources, call center inefficiencies, agents not sticking to the script, etc. At its core, we have a multi-channel outreach campaign feature, whereby our customers can call and text potential leads over an arbitrary time period. If end customers call or text back, the Leaping voice or text AI agent can autonomously schedule appointments and resolve service requests. Some of our differentiating features: most-human like voice in the market, CRM integrations in place (Salesforce, Hubspot, MarketSharp, AccuLynx, JobNimbus, LeadPerfection, Google, etc.) and white-glove onboarding (in-person for larger accounts). Our AI agents can hold 100+ conversations at once, speak multiple languages and be equipped with company specific FAQ. Lastly, we have our own number health monitoring and DNC software.
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 146 companies in Insurance. 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 68% of sector peers (real $). Modeled value above 68% 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.
Leaping 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 |
|---|---|---|---|---|---|
| Acadia Fire Insurance Company | Insurance | — | — | — | same sector |
| Acolite | Insurance | — | — | — | same sector |
| Adaptional | Insurance | — | — | — | same sector |
| Aegis | Insurance | — | — | — | same sector |
| AI Insurance | Insurance | — | — | — | same sector |
| Air Centurion Insurance Services, Inc. | Insurance | Pre-Seed | $600K | $13.9M | same sector |
| Albiware Inc. | Insurance | — | — | — | same sector |
| Altrina | Insurance | — | — | — | 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 |
|---|---|---|---|---|
| Retell AI Supercharge Your Contact Center Operations with AI Phone Agents | AI / ML | — | — | 85% |
| Boom AI AI agents that recover lost revenue through real conversations. | AI / ML | — | — | 83% |
| Hamming AI Complete QA platform for voice agents | AI / ML | — | — | 82% |
| Marr Labs AI-voice agents that are indistinguishable from humans. | AI / ML | — | — | 80% |
| Command AI AI-powered user assistance platform | AI / ML | — | — | 80% |
| Simple AI AI voice that sells | AI / ML | — | — | 80% |
| Uplift AI Foundational Voice Models for regional languages | AI / ML | — | — | 80% |
| Bland AI The enterprise platform for AI phone calls | AI / ML | — | — | 79% |
See where Leaping 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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