BetterQA Software Testing Company
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AI Systems Inspection

Map the unknown
in your AI systems

Validate machine learning models, LLMs, and AI-powered features. We test for accuracy, bias, hallucinations, and the edge cases that fail silently.

Start Assessment View approach
◈ Input
⬡ Model
⚠ Bias Check
◉ Accuracy
▣ Output
95%
Accuracy Target
1000+
Test Cases
24/7
Drift Monitoring
15+
Years QA Expertise
Testing Domains

Four vectors of AI inspection

Comprehensive validation across accuracy, fairness, robustness, and output quality for modern AI systems.

01

Model Validation

Accuracy Testing

Verify predictions against ground truth across diverse datasets. Measure precision, recall, F1, and custom metrics aligned with your business outcomes.

95% Target
1000+ Test cases
02

Bias Detection

Fairness Testing

Identify and measure bias across demographic groups. Test for demographic parity, equalized odds, and disparate impact using diverse test populations.

8+ Fairness metrics
Multi Demographics
03

LLM Testing

Response Quality

Test for hallucinations, coherence, and factual accuracy. Use adversarial prompts to identify failure modes in language model outputs.

GPT Claude, Llama
Adv Prompting
04

Edge Cases

Robustness

Adversarial inputs, boundary conditions, and unexpected data patterns. Ensure your model handles the real world, not just clean training data.

Boundary Conditions
Adversarial Inputs
Methodology

Systematic inspection process

A repeatable framework for validating AI systems from scope definition through production monitoring.

01

Scope

Define inputs, outputs, and success criteria for the AI system

02

Dataset

Create representative test data including edge cases and adversarial examples

03

Execute

Run systematic tests and measure against established baselines

04

Analyze

Identify failure patterns, root causes, and areas for improvement

05

Monitor

Continuous drift detection and performance tracking in production

AI systems fail silently

Unlike traditional software with clear error messages, AI failures manifest as degraded predictions, biased outcomes, and hallucinated content. Without systematic testing, these failures reach production and erode user trust.

  • Model drift causes accuracy to degrade over weeks without alerts
  • Bias issues surface only after regulatory scrutiny or public backlash
  • LLM hallucinations pass internal review but mislead end users
  • Edge cases in production data were never in training data
Questions

Common inquiries

What AI systems can you test?
ML models (classification, regression, recommendation), LLMs (GPT, Claude, Llama), computer vision systems, NLP pipelines, and AI-powered features embedded in applications.
How do you test for bias?
We use fairness metrics like demographic parity and equalized odds across protected groups. We test with diverse datasets and measure disparate impact across demographic segments.
What about LLM hallucinations?
We test factual accuracy against verified sources, check for internal consistency in responses, and use adversarial prompts to identify hallucination triggers and patterns.
Do you support production monitoring?
Yes. We set up drift detection, performance monitoring, and alerting for production AI systems. Catch model degradation before it affects end users.
Ready for inspection

Validate your AI systems

Get a comprehensive assessment of your AI quality and reliability before production deployment.

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Last updated: March 5, 2026 Originally published: June 16, 2025