AI Quality Assurance
Ensure your AI systems deliver consistent, reliable outputs. Build evaluation frameworks that catch problems before users do.
Traditional software testing does not translate directly to AI systems. AI outputs are probabilistic, context-dependent, and can degrade silently over time. A model that performed well at launch may produce subtly worse results months later as the world changes around it. Without structured quality assurance, you only find out when users complain — or worse, when they quietly stop trusting the system. Our AI quality assurance service builds comprehensive evaluation frameworks for your AI systems. We design test suites that cover accuracy, consistency, edge cases, failure modes, and regression detection. We establish baseline metrics and set up continuous monitoring so you know immediately when quality drops. For LLM-based systems, we build evaluation pipelines that assess factual accuracy, hallucination rates, instruction following, tone consistency, and format compliance. We create golden datasets of expected outputs, build automated evaluation harnesses, and design human evaluation protocols for cases where automated metrics are insufficient. The result is confidence in your AI outputs and early warning when something starts to go wrong.
Use Cases
What this looks like in practice
LLM Output Evaluation
Build evaluation frameworks for large language model outputs — measuring accuracy, hallucination rates, instruction following, consistency, and tone.
Regression Testing
Establish test suites that detect quality regressions when models are updated, prompts change, or data shifts. Catch degradation before users do.
Golden Dataset Creation
Create curated datasets of expected inputs and outputs for systematic evaluation. Cover normal cases, edge cases, and known failure modes.
Continuous Monitoring Setup
Implement real-time quality monitoring with automated alerts when AI outputs deviate from established baselines or quality thresholds.
Human Evaluation Protocols
Design structured human evaluation processes for cases where automated metrics are insufficient — including inter-rater reliability and calibration.
Pre-Deployment Testing
Comprehensive testing of AI systems before launch — covering functional requirements, performance under load, failure gracefully, and user acceptance.
Technology
Tools we work with
How It Works
Our approach
Requirements & Metrics
Define what good looks like — the quality dimensions, thresholds, and metrics that matter for your use case
Test Suite Design
Build comprehensive test suites covering accuracy, edge cases, failure modes, and regression scenarios
Evaluation Pipeline Build
Implement automated evaluation harnesses with both model-based and rule-based assessment
Baseline & Benchmarking
Establish quality baselines, set alert thresholds, and benchmark against alternatives
Monitoring & Handover
Deploy continuous monitoring, train your team on the evaluation framework, and hand over documentation
Starting from
£12K
Timeline
2-4 weeks
Ready to get started?
Book a free strategy call and we'll assess whether this service is the right fit for your business.