Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2022 · https://www.daft.ai/
Diligence memoA one-page analyst read on Eventual — recommendation, valuation, rhythm, risks.→Eventual: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Eventual is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2022. 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”
Building the AI data engine for any modality and scale
Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product. Eventual was founded in 2022 to solve this. Our mission is to make querying any kind of data, images, video, audio, text, as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t. Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and CloudKitchens. We’ve assembled a world-class team from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, Tesla, and more, quadrupling our size within a year. With backing from Y Combinator, Caffeinated Capital, Array.vc, and top angels from the co-founders of Databricks and Perplexity, we’re looking to double the team now. Join us—Eventual is just getting started. Please note we are looking for someone who is willing and able to come into our San Francisco office in the Mission district 4 days / week.
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.
Eventual 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 |
|---|---|---|---|---|---|
| 14.ai | AI / ML | — | — | — | same sector |
| 21st | AI / ML | — | — | — | same sector |
| Absurd | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Aether | AI / ML | — | — | — | same sector |
| AfterQuery | AI / ML | — | — | — | same sector |
| AgentMail | 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 | 80% |
| Sieve Video datasets for frontier AI | Robotics | — | — | 78% |
| Playment Mechanical Turk for enterprises. | Space / Aerospace | — | — | 77% |
| Nowadays AI-native platform for corporate meetings and events | AI / ML | — | — | 77% |
| Halluminate Data and RL environments to automate knowledge work | AI / ML | — | — | 77% |
| Roboflow, Inc. 🖼️ Give your software the sense of sight. | AI / ML | Series B | $293.2M | 77% |
| Thesis Autonomous AI research | AI / ML | — | — | 77% |
| Abundant Agent simulation and RL for researchers | AI / ML | — | — | 76% |
See where Eventual sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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