AI Models & LLMs Testing Services
Traditional QA tools were not built for AI. You cannot write a simple assertion for "the model answered correctly". because correctness depends on context, user intent, and probabilistic outputs. Testing AI models requires a completely different discipline.
- 01Hallucination detection & accuracy
- 02Prompt injection vulnerability
- 03Response consistency testing
- 04Bias and safety evaluations
- 05Latency and cost benchmarking
AI models are powerful. but untested AI is a liability
Our AI Model & LLM Testing service validates your AI-powered features for accuracy, safety, consistency, and security. We test for hallucinations, prompt injection vulnerabilities, output bias, and edge cases that only emerge at scale.
Whether you are building a customer support chatbot, a code assistant, a document summariser, or a complex multi-step AI workflow. we make sure it behaves reliably, safely, and within your defined boundaries before it reaches your users.
What We Test on AI Models & LLMs
A comprehensive breakdown of every testing area we cover for this platform.
Accuracy & Quality
- ✓Response accuracy across test prompt datasets
- ✓Factual correctness benchmarking
- ✓Output quality scoring (BLEU, ROUGE, custom)
- ✓Consistency across repeated prompts
Prompt Injection
- ✓Direct prompt injection attacks
- ✓Indirect injection via user-provided content
- ✓Jailbreak attempt detection
- ✓System prompt leakage testing
Hallucination Detection
- ✓Factual claim verification
- ✓Citation and source fabrication
- ✓Confident incorrect answers
- ✓Knowledge boundary violations
Bias & Safety
- ✓Demographic and cultural bias detection
- ✓Harmful content generation testing
- ✓Refusal consistency for unsafe prompts
- ✓Regulatory compliance (EU AI Act)
Performance & Cost
- ✓Response latency benchmarking
- ✓Token consumption analysis
- ✓Cost per query optimisation
- ✓Streaming response validation
Integration Testing
- ✓RAG pipeline accuracy testing
- ✓Vector search relevance validation
- ✓Tool call and function calling accuracy
- ✓Multi-turn conversation coherence
How We Test This Platform
A structured process with clear deliverables at every stage.
Define Test Objectives
We work with you to define what "correct" behaviour looks like. accuracy thresholds, forbidden outputs, required refusals, and performance benchmarks.
Build Prompt Test Dataset
We build a comprehensive dataset of test prompts covering normal use, edge cases, adversarial inputs, and boundary conditions specific to your use case.
Automated Evaluation
We run automated evaluation using LLM-as-judge patterns, embedding similarity, and custom scoring rubrics to evaluate outputs at scale.
Red Team Testing
Our experts manually probe for prompt injection, jailbreaks, and safety failures. the adversarial testing that automated tools cannot replicate.
Bias & Fairness Analysis
We analyse output distributions across demographic groups and prompt variations to identify systematic biases or inconsistencies.
Report & Recommendations
Detailed report covering accuracy scores, failure modes, security vulnerabilities, and specific recommendations for prompt engineering and guardrails.
Technology Stack for This Platform
We are tool-agnostic. we always select the best technology for your specific needs.
Real Bug Examples We Catch on AI Models & LLMs
Real issues we find regularly. bugs that cost businesses money or reputation.
Common Questions
Everything you need to know about how we test this platform.
Have a specific question?
We're happy to discuss your platform, tech stack, and testing needs in a free 30-min discovery call. no commitment required.
Book a Free Call →We test all major LLMs including GPT-4, Claude, Gemini, Llama, Mistral, and any custom fine-tuned models. We are model-agnostic and adapt our evaluation framework to each model.
Related Platforms
Other platforms we test that are commonly used alongside this one.
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