The problem with video isn't making it. It's finding anything inside it. For a sports league like Maple Leaf Sports & Entertainment, a 16-hour search for a specific highlight across an archive used to be a full workday. Now, according to a case study, it's nine minutes [SVG Play, 2026]. That 99% reduction in search time is a direct translation of compute into saved human hours, and it’s the wedge for TwelveLabs, a San Francisco and Seoul-based startup that has raised roughly $167 million to build video-native foundation models [ngram.com, 2026].
Their bet is simple: the world is drowning in video, and text-based search, relying on transcripts or manual tags, is inadequate for finding visual concepts, actions, or objects. TwelveLabs is selling the ability to ask a video library, in plain language, for the first touchdown, the moment a person in a red shirt entered a room, or every scene with a specific machine sound [TwelveLabs].
A wedge of video-native understanding
TwelveLabs approaches video as its own modality. The company’s technology is built around two core proprietary models. Marengo handles multimodal video embedding and semantic search, mapping natural language queries to what’s happening on screen. Pegasus is for video-language generation and understanding, powering tasks like summarization, chaptering, and highlight generation [TwelveLabs].
This allows the platform to answer queries that would baffle a transcript search. For enterprise customers sitting on petabytes of untapped video, from media archives and manufacturing floor footage to security camera feeds, this capability turns a cost center into a searchable asset.
The investor consortium betting on video intelligence
TwelveLabs has assembled a capital table that reads like a who’s who of strategic AI and data infrastructure players. The $50 million Series A in April 2024 was led by New Enterprise Associates (NEA) and included NVentures, Nvidia’s venture arm [Tracxn]. A $100 million Series B followed, led by NAVER Ventures [Ventureburn, 2026]. The list of other investors includes Databricks Ventures, Snowflake Ventures, SK Telecom, HubSpot Ventures, In-Q-Tel, and Amazon [Crunchbase].
This is a coalition of cloud data platforms, chipmakers, and industry-specific giants placing bets that video intelligence will be a core workload on their stacks. The post-Series A valuation was estimated between $200 million and $300 million [Mapping Studio, 2026].
Traction beyond the tech demo
The path to market is through developers and large enterprises. TwelveLabs offers a self-serve Video Intelligence API platform, and its models are also available through cloud ecosystems like Amazon Bedrock [AWS]. Target industries are sprawling, from media and sports to manufacturing, security, and education [TwelveLabs].
The MLSE case study is the clearest public proof point of traction. While specific revenue figures aren’t public, the strategic investor mix and the scale of funding suggest TwelveLabs is moving beyond early adopters and into scaled enterprise deals.
Where the competitive pressure builds
The race to understand and generate video is one of the most crowded in AI. TwelveLabs must navigate a landscape that includes generative video giants like Runway and Pika, tech platform expansion from cloud providers, and open-source models that could commoditize video understanding.
TwelveLabs’ answer to this is its video-native foundation and its focus on the enterprise search-and-analysis workflow. The strategic investments from Databricks and Snowflake are particularly telling; they are bets that TwelveLabs will become the preferred video intelligence layer for companies that already store their data on those platforms.
The next twelve months
The key milestones to watch will be less about model benchmarks and more about commercial footprint. The company needs to convert its impressive investor roster into a roster of flagship, seven-figure enterprise customers across multiple verticals. Another signal will be deeper, product-level integrations with its strategic investors.
Financially, with a fresh $100 million Series B, the focus will be on scaling the go-to-market team and proving that the cost of querying their API is justified by the operational savings for customers. If the MLSE story can be replicated across dozens of similar organizations, the unit economics will speak for themselves.