Datafold, Inc.Summer 2020Active☆Watch⊞Compare

Analyst read est. — computed against this company's niche peers in the same era. Inputs shown; not a quoted figure.

No named principal in this company's public records yet — see all operators below.

Explore how these operators interlock with other companies in the operator network.

Datafold, Inc. is one of 2067 AI / ML companies tracked from New York, NY, USA; Remote, on record since 2020. By capital raised it ranks among the largest (ahead of 93% of sector peers), and in the upper tier 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”

Automating manual and repetitive data engineering tasks with AI

Datafold automates manual work in data engineering. We leverage agentic AI to automate both day-to-day tasks, such as testing and code reviews, and massive one-off projects, such as data platform code migrations. Companies from Perplexity to Disney use Datafold to unlock more value from their data by freeing up their data teams from manual work, accelerating developer velocity, and ensuring data quality.

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.

Round size and date are reported; the stage label is inferred from round size (latest is Series B — a $15M–$40M round). Valuation is modeled from stage benchmarks scaled by the ai/ml sector profile. Directional, not a quoted figure.

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 93% of sector peers (real $). Modeled value above 80% of peers (estimate).

A tighter cohort than the sector chart above — only companies at the same stage (Series B) that last raised in the same ~3-year era, ranked by modeled value est. (not money raised). This is the exact set the analyst read compares against, so a like-for-like cohort is what makes "over/under-valued" meaningful. This company ranks #22 of 22.

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.

Step-up, pace versus the sector's normal cadence, and revenue/exemption moves — read straight from the reported round facts reported.

1. Series B$21.1M2021-10-25
   • First recorded raise — entered as Series B.
   • 35 investors on this round.
2. Series B$4.0M2025-05-13
   • Raised again after 43 mo — slower than the ~11 mo sector norm.
   • Round 5.3× smaller than the prior — bridge or down round.
   • 6 investors on this round.

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.

Officers, directors and promoters named in this company's public recordsreported — the people who run it (not its investors). "Also runs" counts come from name-matching across all records, so verify before acting.

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

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