End CloseWinter 2026Active☆Watch⊞Compare

End Close is one of 1063 Fintech companies tracked, on record since 2026. 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”

AI powered reconciliation for high-volume payments companies

End Close builds automatic reconciliation for payments companies like fintechs, marketplaces, banks and payroll companies. Reconciliation helps companies track every dollar and resolve every exception, ensuring they have no money loss, can report audit ready financials, and stay compliant. Sean (CEO) spent the last 6 years building the reconciliation org at Modern Treasury (S18), processing a trillion dollars in payments across 40 banks with 99.995% automation. Fast growing companies like Modern Treasury are forced to build a solution in-house or buy a legacy solution. End Close provides a modern reconciliation system that works with a company's existing stack and is managed by AI agents. Exception handling is where 99% of the manual work happens today, payment ops teams spend hours gathering context across various data sources to investigate each ticket. End Close provides an AI ops agent that connects to a company's data warehouse, invoicing platform and payment processors to triage and resolve issues without human intervention. Legacy providers only provide reconciliation for month end close applications. End Close continuously reconciles with developer friendly APIs, allowing teams to integrate real time payments data within their products to create product experiences for customers like faster balance top-ups, instant trading etc. New payment rails and agent-to-agent payments are increasing online transaction volumes, making reconciliation a growing challenge for companies. David (CTO and 2nd time YC founder) most recently built these kinds of high-scale ingestion pipelines when starting and leading error tracking at PostHog (W20), the fastest growing product in the company's history.

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.

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

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