CharacterQuiltSpring 2026Active☆Watch⊞Compare

CharacterQuilt is one of 214 Sports & Fitness companies tracked from San Francisco, CA, USA, on record since 2026. By capital raised it ranks in the long tail (ahead of 14% of sector peers), and in the long tail 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”

Computer use agents to deploy enterprise marketing campaigns

CharacterQuilt builds computer use agents to deploy enterprise marketing campaigns. Marketers today don’t actually do much marketing at all. Today, over 80% of time spent on launching a marketing campaign is actually ops work: tweaking automation flows in Pardot, configuring audiences in LinkedIn, or wiring HTML emails in Marketo. What’s crazy about enterprise marketing is companies pay agencies millions of dollars just to deploy campaigns in their systems — and they still have to wait weeks for each one. CharacterQuilt is AI infrastructure for marketing teams: a brain that learns your brand, and agents that operate your existing tools. Marketing campaigns that used to take 3 agencies, 10 tools, and 6 weeks are now being built, designed, and deployed from CQ in an hour. Customers submit a brief like they would send to a human teammate or marketing agency, and they get back fully designed and deployed campaigns. Teams like 1upHealth, ABC Fitness, and Emplifi use us to: 1-) Build segments/audiences faster 2-) Create on-brand creative (AI is terrible at staying on-brand) 3-) Deploy campaigns automatically into HubSpot, WordPress, LinkedIn etc. We replicate your brand guidelines, deeply understand past campaigns, and work inside your systems. If you run demand gen or lifecycle at a B2B/B2C company and want to ship campaigns faster, send us a brief and we’ll build it in 15 minutes.

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 214 companies in Sports & Fitness. 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 14% of sector peers (real $). Modeled value above 14% 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.

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.

CharacterQuilt 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.

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.

See where CharacterQuilt sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.

If you work at CharacterQuilt, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.