FlyCodeSummer 2022Active☆Watch⊞Compare

FlyCode is one of 1063 Fintech companies tracked from Boston, MA, USA, on record since 2022. By capital raised it ranks mid-pack (ahead of 66% 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”

Recover failed subscription payments to increase retention and LTV

FlyCode helps subscription merchants maximize revenue. We reduce involuntary churn by managing payment failures intelligently. We’re Jake, Etai, and Tzachi and we’re building FlyCode. TL;DR: If you sell SaaS or B2C subscriptions, we’re here to help you recover lost revenue due to failed payments and involuntary churn. ‘Involuntary’ or ‘Passive’ churn is a hidden Revenue Killer 💳 for Subscription Businesses ❌ THE PROBLEM: Recovering Failed Payments is Complex Involuntary churn refers to the loss of subscribers due to payment failures, not due to them actively canceling. While it is both possible and a worthwhile strategy to attempt to win back a customer during the cancellation process — involuntary churn is not intentional and in most cases, your customers aren’t even aware that their payment failed. 💰 Bottom line? Effective payment optimization does reduce involuntary churn and will increase ARR by as much as 5-10% FLYCODE SOLUTION 💡 FlyCode leverages ML and AI to provide Payment Optimization and smart payment retries to maximize subscription revenue and reduce churn. We apply a unique model tailored to each Merchant and their customers to ensure optimal results. ⚡️Automatically recover failed subscription payments and reduce passive churn ⚡️Use ML & AI based payment optimization to find the best time to charge your customers ⚡️ Improve customer experience by coordinating payment retries with customer emails/SMS 🛠️ We support OOTB + Custom Integrations for Merchants and White-Label for Platforms

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 1063 companies in Fintech. 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 66% of sector peers (real $). Modeled value above 66% 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.

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

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