StartupStuttgart, GermanyFounded 2021
Develops the Cortex vision-language-action models for warehouse and industrial pick-and-place robots.
Sereact is a Stuttgart-based robotics software company founded in 2021 by Ralf Gulde and Marc Tuscher. It develops Cortex, a vision-language-action model trained on data from more than 100 industrial deployments, and Cortex 2.0, which adds a world model that evaluates candidate trajectories before acting. In April 2026 it raised a $110M Series B led by Headline to scale Cortex 2 and open a U.S. office.
Sources: massrobotics.org (26 Apr 2026) · tech.eu (29 Sep 2025) · arxiv.org (22 Apr 2026)
Private company · $110M disclosed in USD across 1 of 2 listed rounds
| Date | Round | Amount | Lead | Source |
|---|---|---|---|---|
| 20 Jan 2025 | Funding round (stage not stated in source) | EUR 25M | n/a | deutsche-startups.de, 20 Jan 2025 |
| Apr 2026 | Series B | $110M | Headline | massrobotics.org, 26 Apr 2026 |
#76 of 104 model companies · score 10.3 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 0 of 2 listed models have open weights.
The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating). |
| Open weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero. |
| Released models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion. |
Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.
Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.
Closed weightsWorld model
Hierarchical system that adds a world model to a VLA: it generates candidate future trajectories in visual latent space, scores them, and executes the highest-scoring one.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsVLA
Vision-language-action model trained on data from over 100 industrial deployments and described as hardware-agnostic across arms, mobile manipulators and humanoids.
No benchmark results on a shared public benchmark have been published for this model.
