Services · Our Expertise
Independently validate what an AI vendor claims
A datasheet says the model is accurate, robust to noise, fast and frugal with power. Every one of those numbers was measured by the company that needs it to be true. We test the claim instead: a written protocol agreed before measurement, data the vendor has never seen, instrumentation we control in our San Antonio lab, and a report that includes the conditions under which the claim fails.
We do this for buyers who need evidence before they commit, and for builders who need someone else's name on a result before a program office or an investor will accept it. Validation is technical evidence, not a certification: we say so plainly rather than implying a stamp nobody issued.
Evidence somebody else produced
The fastest way past "prove it" is a reproducible result from a lab with no equity in the vendor, no resale margin and no fee that depends on the answer. The price is the same whether the product passes or fails, and failures stay in the report.
What you get
Claim decomposition and a frozen protocol
Each marketing claim becomes a testable statement with its conditions written out: which noise, over what range, measured by which metric, with what pass threshold. The protocol, data and sample sizes are agreed and frozen before anything is measured.
Noise robustness and distribution shift
Accuracy is measured as a curve, not a point: performance under controlled, sensor-realistic perturbation across a stated range, on held-out data, so the buyer sees where the model degrades and where it falls over.
Latency, throughput and power at the wall
p50, p95 and p99 latency under realistic concurrency, and sustained watts under load in the enclosure the device will actually live in. Edge and neuromorphic hardware is where we spend most of our lab time.
Adversarial and security testing
Red-team work against the model and the system around it: prompt injection, tool misuse, data extraction and agent permission boundaries for software; out-of-spec inputs, thermal stress and fault injection for hardware.
A reproducibility package and a plain report
Scripts, seeds, dataset manifests and hardware configuration so another engineer can rerun the test, plus a report written for the technical buyer and the contracting officer who will read it later.
Questions about independent ai validation
How do I independently validate a vendor's noise-robustness claim?
Pin the claim down first: which noise types, over what range, measured by which metric. Then test on held-out data the vendor has never seen, sweep the noise level in controlled steps, report accuracy as a degradation curve with confidence intervals, and repeat across runs. The test plan and pass criteria should be written and agreed before any measurement, and the work should be done by someone with no stake in the result.
Is a validation report the same as a certification?
No. A certification is a status granted by the body that owns a published scheme. A validation report is independent technical evidence of what was tested, under what conditions, and what happened. It is often what a certification body, a program office or an investor's diligence team asks to see.
Can testing run on-premises or air-gapped?
Yes. Measurement can run in our lab, on your premises, air-gapped, or in your own cloud. Nothing has to leave your perimeter for us to measure it.
Do you validate products you helped build?
Not as a third party. If we engineered it, we are the first party, and we will say so in writing.
How long does an engagement take?
A focused validation of one or two specific claims is usually measured in weeks. A full assessment across performance, robustness, power, security and governance alignment takes longer, and the schedule is driven mostly by test design and access to representative data.
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- Digital Twins
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See also
Field notes
AI and data intelligence,
from San Antonio.
Monthly notes on applied AI, synthetic and real-world data, and neuromorphic and physical AI, written for the people actually deploying them. No pitches.
Delivered by an SDVOSB in San Antonio · NVIDIA DLI & IBM SkillsBuild partner