Contextual Testing, Red Teaming, and Fine Tuning for Visual AI

Visual AI models don’t fail on benchmarks — they fail in context. Our platform uses contextual synthetic data to catch and address those failures so you can deploy models with confidence.

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Why context matters

Benchmarks measure averages. Failures depend on context.

Most failures don’t occur in the average case — they happen at the edges: extreme weather, poor lighting, cluttered scenes, or unusual angle. Our platform recreates those conditions on demand so you can spot failure contexts quickly and ship fixes — fast.

Select a context to see how detection confidence changes:

Red pickup truck parked on a dirt path surrounded by tall green trees and forest vegetation.

The model easily detects the truck in clear conditions.

Red pickup truck driving on a dirt road in a dense forest of tall pine trees.

Shifting the camera angle drops confidence.

Red truck driving on a forest dirt road during rainfall.

Rain does not significantly impact detection in this case.

A white pickup truck partially covered in snow parked on a snow-covered forest path surrounded by snow-laden trees.

Model struggles in detecting white trucks in snow.

Red pickup truck driving on a foggy forest dirt road surrounded by tall trees and branches.

In heavy fog, the model misses the truck.

BENEFITS

Reduce Data Collection and Labeling Costs

Collecting and labeling EO/IR data for each sensor and environment is slow and expensive.

Yrikka generates fully annotated EO/IR image pairs tuned to your actual sensors and operating conditions, without new flights, drives, or spinning up a labeling team.
You describe the task and context once, and the Platform delivers large labeled datasets for the conditions you care about, freeing budget and time to focus on model development instead of data wrangling.

Make Models Hold Up in Real-World Conditions

Standard benchmarks miss the real-world conditions that break deployed models—glare, fog, partial occlusion, crowded scenes, unusual viewpoints, sensor noise.
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With Yrikka, you design and run targeted EO/IR scenario suites that mirror deployment conditions. The Platform evaluates your existing models against these scenarios and returns a ranked list of concrete failure patterns your team can fix, instead of a single performance metric that hides risk.

Ship New Vision Capabilities Faster

Every new deployment environment or sensor mode usually requires rebuilding data and tests from scratch.

With Yrikka, you reuse the same pipeline: describe the new task, plug in the new sensor samples, and generate adapted and labeled EO/IR data plus focused tests for that context. Product leads get a predictable path from “we need this capability” to “we have a model that passes these scenarios,” instead of another multi-month bespoke effort.

OUR STORY

Born out of necessity, YRIKKA emerged as a bold response to the urgent need for repeatable and trusted processes that enable the alignment of AI with human values.

Drawing on groundbreaking experiences—from advancing healthcare AI innovations at the Mayo Clinic to spearheading space ML explorations at NASA—our co-founders transformed their expertise into a mission to redefine how AI is built and deployed in high-stakes environments.

Today, YRIKKA stands at the nexus of human-aligned AI and AI red teaming, ensuring that every AI solution not only drives progress but also upholds the core values that make our world worth advancing.