AI Test & Evaluation
Why It Matters
Most AI systems are evaluated in controlled environments with clean data and predictable inputs. In the field — on edge devices, in contested networks, across languages — they encounter conditions their developers never anticipated. Models degrade. Outputs drift. Vulnerabilities surface
Without independent T&E, organizations deploy risk instead of capability. The cost of an untested model isn't a bad benchmark score — it's a missed fault on a power grid, a security breach in a deployed system, or a failure that erodes trust in AI at the moment it matters most.
What We Evaluate
We stress-test AI systems end to end — from model performance to security to fairness to deployment readiness. Each evaluation is tailored to the specific system, mission, and operating environment, and conducted under the real-world conditions the model will actually face.
Security & Adversarial Robustness
Red teaming, prompt injection testing, adversarial input generation, and model extraction resistance. We probe AI systems the way real adversaries would — finding the vulnerabilities before they do, and documenting exactly what it takes to exploit or break the model under hostile conditions.
Performance & Accuracy
Baseline and edge-case evaluation across inference speed, accuracy, latency, and throughput — tested on the target hardware and in the conditions where the model will actually operate. We measure what matters in deployment, not what looks good on a leaderboard.
Bias, Fairness & Safety
This isn’t just a business—it’s a reflection of what we believe in. We’re here to create work that matters, led by a shared commitment to quality and care.
Edge Readiness
Can the model actually run where it needs to — on-device, offline, within strict power and compute constraints? We validate deployment readiness for resource-constrained and disconnected environments, testing against the real hardware, memory limits, and power envelopes of the target platform
Edge readiness isn't just about whether a model fits on a device. It's about whether it performs reliably once it's there — under noisy inputs, degraded conditions, and sustained operation without cloud fallback. We test for all of it before the model leaves the lab
We also evaluate how models behave at the boundaries — when battery is low, when temperatures spike, when connectivity drops mid-inference, when inputs fall outside the training distribution. These are the conditions that define edge deployment, and they're exactly where untested models fail
