Companies · AI / ML
New York City · NY, USA · AI / ML · founded 2023 · https://www.deasylabs.com/
Diligence memoA one-page analyst read on Deasy Labs — recommendation, valuation, rhythm, risks.→Deasy Labs: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Deasy Labs is one of 2067 AI / ML companies tracked from New York City, NY, USA, on record since 2023. By capital raised it ranks mid-pack (ahead of 62% 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”
Metadata for GenAI workflows
Deasy Labs was acquired by Collibra in July 2025 (global leader in enterprise data governance). Deasy Labs provides metadata orchestration for AI workflows. Deasie's platform provides the best way for AI teams to create and embed high-quality, customized metadata into their AI workflows (e.g., RAG, Agentic frameworks). Our three founders (from Amazon, McKinsey/QuantumBlack & MIT) previously built an ML data governance tool from 0 to 1 within McKinsey, which we deployed with 11 Fortune 500 companies. We saw in early 2023 the ability to create high-quality metadata (without reliance on domain experts) would be a key factor in achieving the accuracy & speed in GenAI applications required for production. Our investors include General Catalyst, Y Combinator, RTP Global and world experts in enterprise data. Website: https://deasylabs.com
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 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 62% of sector peers (real $). Modeled value above 62% 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.
Deasy Labs 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 |
|---|---|---|---|---|---|
| Accord | AI / ML | — | — | — | same sector |
| Acely | AI / ML | — | — | — | same sector |
| Aedilic | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Affogato AI | AI / ML | — | — | — | same sector |
| Aftercare | AI / ML | — | — | — | same sector |
| Agentic Labs | AI / ML | — | — | — | same sector |
| Ai Aiba | AI / ML | — | — | — | 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 |
|---|---|---|---|---|
| Databricks, Inc. Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform. | AI / ML | Growth/Late | $41.5B | 76% |
| Chamber The AIOps Agent for ML Teams | AI / ML | — | — | 75% |
| Altrina The SOP Automation Platform | Insurance | — | — | 75% |
| Jarmin AI Employees for human-level roles | AI / ML | — | — | 74% |
| Dataland AI agents for customer support. | AI / ML | — | — | 74% |
| Humwork Human experts as API for AI Agents | AI / ML | — | — | 74% |
| RefineTrain AI AI agents rewriting & optimising your internal documentation for LLMs | AI / ML | — | — | 74% |
| Workload.ai, Inc. | Computers | Pre-Seed | $10.6M | 74% |
See where Deasy Labs sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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