Eventual
7 known investors
Eventual develops data infrastructure for teams building physical AI, particularly focused on handling video and sensor data at scale. Their product MultiBase enables semantic indexing and querying of fleet perception data to optimize model training and GPU efficiency.
Also known as Daft · Eventual Computing · MultiBase
Founders & leadership
Investors · 7
Also in the syndicate · 3
Funding
SEC filings, press & company announcementsSource: company announcements and press reports — follow each round's link for the claim.
Company profile
researched Aug 2026Eventual builds data infrastructure for AI workloads involving images, video, audio, and other multimodal data. The company argues that existing data platforms, such as Databricks and Snowflake, were designed for spreadsheet-like tabular analytics rather than the petabyte-scale multimodal data used to train modern AI systems, which forces teams to build brittle custom infrastructure. Its stated mission is to make querying any modality of data as intuitive as working with tables while scaling to production workloads.
The company's core technology is Daft, an open-source (Apache-2.0) distributed query engine and multimodal data engine that coordinates with external APIs, manages GPU clusters, and handles failure modes that traditional engines do not address. On top of Daft, Eventual offers MultiBase, a product aimed at teams working with fleet-scale visual robotics data. MultiBase indexes sensor and camera data, supports natural-language search for rare or long-tail scenarios (for example, "left gripper dropped a deformable object in a bin" or "hard braking with phantom objects"), returns matching clips with their spans and match rationale, curates datasets for dataloading into training and evaluation, and monitors live deployments to surface failures as alerts.
Eventual's marketing materials target three industry segments: mobility (self-driving), industrial, and manipulation robotics. The company states that 99% of robot data is low value and that physical AI therefore requires intelligent data mining and curation of long-tail scenarios.
Founding story
Eventual was founded in 2022 by Sammy Sidhu and Jay Chia, who met while working together at Lyft Level 5, where they observed AI engineers repeatedly slowed down by data infrastructure hurdles. Sidhu, the CEO, has a background in high-performance computing and deep learning, with prior work in high-frequency trading, medical AI research at Berkeley, and self-driving at DeepScale (acquired by Tesla) and Lyft Level 5 (acquired by Toyota); he holds a degree in Electrical Engineering and Computer Science from UC Berkeley. Chia graduated from Cornell University, where he researched deep learning and computational biology, and previously built ML data platforms at Freenome and Lyft Level 5; he is originally from Singapore, where he served as a tank commander and later led recruiting at a startup.
Business model
Eventual maintains Daft, an Apache-2.0 licensed open-source multimodal data engine, and sells MultiBase, a commercial product built on top of Daft that indexes, searches, and curates fleet-scale visual and sensor data for teams building autonomy and physical AI systems. Sources do not state pricing or contract structure.
Sources do not disclose pricing or revenue figures. Daft is offered as free open-source software under Apache-2.0, while MultiBase is identified as the company's commercial product.
Traction
Daft is reported to process petabytes of multimodal data daily at companies including Amazon, CloudKitchens, Essential AI, and Together AI, with Mobileye also cited as a user. The company reports quadrupling its team within a year and lists a team size of 18, with open engineering roles in San Francisco. Total disclosed funding is $30M as of June 2025. The website features an unattributed customer quote credited to a head of AI at an agricultural robotics company.
Latest developments
In June 2025 Eventual announced a $20M Series A led by Felicis, with participation from M12 Ventures and Citi, bringing total disclosed funding to $30M, to be used for expanding operations and development. TechCrunch published a profile in June 2025 describing how a data processing problem at Lyft became the basis for Eventual. The company's website now foregrounds MultiBase for visual understanding in autonomy, spanning mobility, industrial, and manipulation use cases.
▸Full profile — market position, technology, go-to-market, geography, history, risks & controversies
Market position
Eventual competes with general-purpose data platforms and query engines, explicitly naming Databricks, Snowflake, and Spark as tools built for tabular analytics rather than multimodal AI data. Its open-source engine Daft is described as running in production at hyperscale companies and AI labs, with named users including Amazon, Mobileye, Together AI, CloudKitchens, and Essential AI. Its Y Combinator profile lists it in the Computer Vision category.
Eventual positions Daft as natively handling the messiness and diversity of multimodal data through declarative queries at petabyte scale, in contrast with legacy tabular engines such as Spark and Snowflake, which it characterizes as built for spreadsheet-like analytics. The company also frames its value as removing the need for AI developers to become distributed systems experts, turning months of infrastructure work into days of application development. For MultiBase specifically, it cites an indexed "understanding layer" spanning both live feeds and archives, with per-match explanations, and a reported 6.4x training speedup benchmark.
Technology
Daft is an open-source, Apache-2.0 licensed distributed query engine and multimodal data engine designed to process images, video, audio, and text at petabyte scale using declarative queries, coordinate with external APIs, manage GPU clusters, and tolerate failures that traditional engines do not handle. MultiBase is built on Daft and provides an indexing and understanding layer over archived and live camera and sensor feeds, supporting natural-language scenario search with match spans and explanations, dataset curation, dataloading into training, and live deployment monitoring. The website reports a benchmark of 6.4x faster training for V-JEPA on Ego4D using 8x RTX Pro 6000 GPUs, against a baseline of PyAV over object storage.
Go-to-market
Eventual distributes Daft as free open-source software that is already used in production at large technology companies and AI labs, and converts interest into commercial engagement through MultiBase, with website calls to action inviting prospects to have the product demonstrated on their own datasets. The company also appears at industry events such as The AI Conference startup showcase and hires actively for engineering roles in San Francisco.
AI and machine learning teams that process large volumes of images, video, audio, and sensor data, including foundation model labs, autonomous vehicle and robotics developers, and large technology companies. Named users of Daft include Amazon, Mobileye, Together AI, CloudKitchens, and Essential AI. MultiBase targets mobility/self-driving, industrial, and manipulation robotics teams.
Geography
Eventual is headquartered in San Francisco, with an office in the Mission district where staff are expected on site four days per week. Sources do not describe additional locations.
History
Eventual was founded in 2022 and participated in Y Combinator's Winter 2022 batch. Its founders, Sammy Sidhu and Jay Chia, met while working at Lyft Level 5. The company released Daft as an open-source distributed query engine and reports quadrupling headcount within a year, reaching a team size of 18 as listed on its Y Combinator profile. It raised a seed round led by Brittany Walker of CRV with support from Y Combinator, Essence VC, and Array Ventures, followed by a $20M Series A announced in June 2025 led by Felicis with M12 Ventures and Citi, bringing total funding to $30M. Its current positioning emphasizes MultiBase, a visual-understanding product for robotics and autonomy data.
Risks & controversies
No controversies, litigation, or negative events are described in the available sources. Note that multiple unrelated companies operate under the name Eventual, including an insurance operations software vendor at eventual.com and a climate-insurance fintech founded in 2023 by Dylan DiMarchi and Youssef Doss; these are distinct entities from eventual.ai.
Compiled by commissioned research from 8 cited public sources — announcements, filings, and press listed under research sources below.
Key figures
latest reportedCompany-reported or press-reported figures, each dated to when it was claimed — not independently audited.
Competitors · 3
by search overlap
Companies competing with Eventual for the same Google search keywords, organic and paid, via search-intersection analysis.
Timeline · 2
launches, deals, and filingsEventual, a San Francisco-based company building AI infrastructure for multimodal data, raised $20M in Series A funding led by Felicis with participation from M12 Ventures and Citi; funds intended to expand operations and development efforts.
$20M source ↗
Eventual offers MultiBase, built on its open-source Daft engine, which indexes sensor and camera data, enables search for rare long-tail scenarios, curates datasets for training and evaluation, and monitors live deployments.
Dated company events from announcements, filings, and press; legal rows summarize public dockets and regulator releases.
In the news
▸Research sources · 8
primary sources listed
- Eventualeventual.ai · web
8 public sources were cited for this profile; the first-party ones are listed here.
Frequently asked questions
- What does Eventual do?
- Eventual builds Daft, an open-source multimodal data engine, and MultiBase, a search and curation product for fleet-scale robotics video.
- Who founded Eventual?
- Eventual was founded by Dylan DiMarchi, Jay Chia.
- Who are Eventual's investors?
- Eventual's investors include CRV (Charles River Ventures), Felicis Ventures, AlleyCorp, Upfront Ventures.