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
Accuracy & Quality
- βResponse accuracy across test prompt datasets
- βFactual correctness benchmarking
- βOutput quality scoring (BLEU, ROUGE, custom)
- βConsistency across repeated prompts
Prompt Injection
- βDirect prompt injection attacks
- βIndirect injection via user-provided content
- βJailbreak attempt detection
- βSystem prompt leakage testing
Hallucination Detection
- βFactual claim verification
- βCitation and source fabrication
- βConfident incorrect answers
- βKnowledge boundary violations
Bias & Safety
- βDemographic and cultural bias detection
- βHarmful content generation testing
- βRefusal consistency for unsafe prompts
- βHuman oversight validation
Performance & Cost
- βResponse latency benchmarking
- βToken consumption analysis
- βCost per query optimisation
- βStreaming response validation
Integration Testing
- βRAG pipeline accuracy testing
- βVector search relevance validation
- βTool call and function calling accuracy
- βMulti-turn conversation coherence
How We Test This Platform
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.
How we run QA for teams building AI products: AI-Powered QA.
Technology Stack for This Platform
Common Bugs We Catch on AI Models & LLMs
Common Questions
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 QA Strategy CallWe 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.
