AI Services

AI Model Testing

Rigorous Validation for Probabilistic Systems

Overview

AI systems are probabilistic, not deterministic. Standard QA misses model failures. We provide specialized frameworks to validate accuracy, bias, robustness, and latency — and we set up the monitoring that catches drift after launch.

Key Features

  • Model accuracy and bias testing
  • Adversarial attack simulation
  • Automated regression testing
  • Performance and latency benchmarking
  • Edge case discovery
  • Data quality validation
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6 core capabilities included

Frequently asked questions

We build evaluation sets that stratify across demographics, inputs, and edge contexts, then measure fairness metrics (disparate impact, equalized odds). We document where the model still has gaps so you can decide acceptable risk.

Standard QA tests deterministic boolean outcomes. AI testing requires statistical validation across distributions — accuracy bands, confidence calibration, drift detection. We use both, in parallel.

Yes. We wire up dashboards for accuracy, latency, hallucination rate, and cost, plus alerts when any of them slip. You see the model's health like you see any other production service.

Most focused engagements ship in 4–10 weeks. Smaller sprints (audits, integrations, prototypes) run 1–2 weeks. We give you a milestone-based timeline before kickoff so you can plan around it.

Sprints start from US$3,500, fixed-scope MVPs from US$12,000, and embedded engineering from US$8,000 per senior engineer per month. Full breakdown on our How We Work page.

You do, from day one. All deliverables and IP transfer to you on each milestone, documented in the engagement agreement. There is no lock-in and no ongoing license fees on the code we write.

We handle change in writing. Small adjustments are absorbed; larger changes get a quick change order with revised scope and price so there are no surprise invoices.

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AI Model Testing?

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