AI & Automation
AI in production
needs engineering care.
AI systems degrade in ways that traditional software does not — model drift, distribution shift, prompt injection, cost escalation, and silent accuracy decline. GreaterHeight's production assurance practice gives AI systems the same reliability engineering attention we give to the rest of a technology stack.
Services
How we keep your AI systems reliable
LLM Quality Monitoring
Continuous evaluation of LLM response quality using automated scorers, human-labelled reference sets, and statistical baselines — alerting on quality degradation before users notice.
Model Drift Detection
Monitoring for distribution shift, embedding drift, and accuracy decline in ML models — with automated retraining triggers and model versioning to roll back when needed.
AI Safety Testing
Red-teaming, adversarial prompt testing, jailbreak resistance evaluation, and content safety audits — particularly important for customer-facing AI systems in regulated industries.
Cost Optimisation
Token usage analysis, prompt compression, model tier selection, and caching strategy — reducing LLM inference costs while maintaining output quality.
Evaluation Pipeline Design
Automated evaluation suites with regression tests, golden set management, and continuous benchmarking — so every model update is measured against the same quality bar.
AI Incident Response
Structured incident management for AI-specific failures — hallucination spikes, cost runaway, safety violations — with root cause analysis and remediation playbooks.
Is your AI system being monitored as rigorously as the rest of your stack?
Talk to us about production assurance for your AI systems.