
Tenyks
Helps AI developers build reliable vision systems.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor investor investor | €0.0 | round |
investor investor | €0.0 | round | |
investor | €0.0 | round | |
investor | €0.0 | round | |
* | $3.4m | Seed | |
Total Funding | 000k |
USD | 2022 | 2023 |
---|---|---|
Revenues | 0000 | 0000 |
% growth | - | 100 % |
EBITDA | 0000 | 0000 |
Profit | 0000 | 0000 |
EV | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x |
R&D budget | 0000 | 0000 |
Source: Dealroom estimates
Related Content
Tenyks.ai is a startup that operates in the field of Machine Learning Operations (MLOps), specifically catering to computer vision teams. The company's primary offering is a platform that accelerates the process of getting production-ready models by eight times. This is achieved through a variety of features such as data balancing, multi-modal search, and model comparison, among others.
Tenyks.ai's platform allows clients to host data privately in their enterprise cloud storage, enabling them to load their datasets in minutes. It also provides tools to detect, visualize, and correct data failures, which in turn helps in training better models. The platform also allows users to compare their model's performance and ship industry-models quickly.
The company's business model is likely based on a subscription or usage-based pricing model, where clients pay for the services they use. This model allows Tenyks.ai to generate revenue while providing value to its clients by automating laborious processes and improving the quality of their models.
Tenyks.ai operates in the rapidly growing MLOps market, serving clients who need to develop and deploy machine learning models, particularly in the field of computer vision. This includes industries such as industrial safety and surveillance, where computer vision models can be used for tasks like object detection and recognition.
In summary, Tenyks.ai is a promising startup in the MLOps space, providing a platform that accelerates the development and deployment of computer vision models, while also improving their quality and performance.
Keywords: MLOps, Computer Vision, Model Development, Data Balancing, Multi-modal Search, Model Comparison, Cloud Storage, Data Visualization, Industrial Safety, Surveillance.