We've recently integrated the YRIKKA APEX API into our model validation pipeline, and the experience has been nothing short of transformative. As a team deeply invested in building robust, production-grade computer vision models, ensuring they operate reliably across diverse, real-world scenarios is critical. APEX made that process faster, smarter, and more rigorous.
The standout feature is its natural language-based scenario generation. Instead of painstakingly curating datasets or trying to simulate edge cases manually, we simply describe the kind of context we want to test (e.g., "pedestrians in fog at night near construction sites")—and APEX delivers a curated suite of synthetic tests targeting exactly that. This approach helped us uncover blind spots that traditional benchmarks completely missed.
The API workflow is clean and intuitive. Uploading models, triggering evaluations, and monitoring jobs is seamless. The integration with pre-signed URLs and model packaging ensures data privacy, and the asynchronous job system makes it easy to fit into CI/CD pipelines.
Where APEX truly shines is in its metrics and reporting. It doesn’t just test accuracy—it reveals how your model performs under context-specific stress, which is invaluable for both debugging and compliance. We now use its output to inform not only technical decisions but also to communicate model robustness to stakeholders.
Overall, YRIKKA APEX API is a must-have tool for any team serious about the real-world readiness of their AI models. It’s like having an expert red team working around the clock to challenge and improve your systems.
Hey Product Hunt 👋
I'm one of the makers of APEX API — a tool we built after realizing how difficult it is to evaluate visual AI models in the messy, unpredictable environments they’re meant to operate in.
🎯 What it does:
APEX is an API that helps you automatically red team your object detection models. You define the operational context (e.g. lighting conditions, object angles, occlusions), and we generate data and simulate those conditions to probe for weaknesses and failure modes — all via API.
🧠 Why we built it:
Visual AI is being deployed in high-stakes scenarios, but most testing still happens in overly controlled, idealized environments. We wanted a better way to uncover how models behave in context, before they fail in the wild.
💡 Who it’s for:
We’ve seen early interest from teams working across a variety of use cases, such as:
Defense: Evaluating aerial object detection systems for spotting vehicles in mountainous terrain — across varying times of day, weather conditions, and camera angles.
Autonomous systems: Stress-testing warehouse robot vision with low-light environments, cluttered shelves, shiny surfaces, and moving obstacles.
AgTech: Testing fruit detection and ripeness classification under inconsistent lighting, natural occlusions (branches, leaves), and different growth stages.
If you’ve ever asked “but will this model actually work in the real world?” — APEX is for you.
🔓 Today we’re opening API access for object detection models.
No waitlist. Just sign up, integrate, and start testing.
→ https://github.com/YRIKKA/apex-quickstart
We’re excited to hear what you think! Feedback, questions, use cases — drop them below 👇
Checkout this tutorial for an AgTech use case here: https://github.com/YRIKKA/apex-quickstart/blob/main/notebooks/agtech_example.ipynb
With the abundance of both supply and demand in off the shelf vision models, it's crucial for the users to stress test such models and to build confidence before deployment in their target applications. Thanks to the YRIKKA team for filling this void and looking forward to some hands on experience with the API!
Congrats on the launch! Context-aware testing is such a critical piece in building reliable AI systems, glad to see you making it accessible via API. This is going to be a game changer for many teams.
That’s amazing, congrats on the launch 🚀 So excited to start testing the API!
@ali_afshar3 Thank you! Please reach out to help@yrikka.com if you have any questions.