AI Operations

AI systems behave differently from traditional software. They can degrade gradually as the world changes around them. They can fail in ways that are not immediately obvious. They need continuous tuning to maintain and improve performance.

Without proper operations, AI investments decay. Models drift. Performance drops. Users lose trust. The value you expected never materialises or disappears over time.

Our AIOps service encompasses several connected activities.

Monitoring tracks how your AI is performing in real time. We measure response quality, accuracy, latency, and user satisfaction. Dashboards show current status; alerts notify us when metrics fall outside acceptable ranges.

Incident response handles problems quickly. When something goes wrong, we diagnose the cause, implement fixes, and communicate with stakeholders. Defined escalation procedures ensure serious issues get appropriate attention.

Performance analysis examines trends and identifies opportunities. Regular reviews look at how AI is being used, where it succeeds, and where it struggles. This insight drives improvement priorities.

Continuous optimisation makes AI better over time. We refine models, improve conversation flows, tune parameters, and add capabilities based on what we learn from real usage.

Change management handles updates carefully. When AI systems need modification, we test changes thoroughly before deployment and monitor closely afterward.

Effective monitoring requires the right metrics. For conversational AI, we typically track:

Resolution rate. How often AI handles enquiries without human involvement.

Answer quality. How often responses are correct and helpful.

User satisfaction. How users rate their experience.

Fallback rate. How often AI cannot understand or respond.

Response time. How quickly AI responds to user input.

We establish baselines during deployment and set thresholds that trigger alerts when performance degrades.

Improvement follows a structured cycle.

Measure what is happening. Collect data on real interactions and outcomes.

Analyse patterns. Identify common failure points, frequent queries the AI handles poorly, and opportunities for improvement.

Prioritise changes. Focus on improvements that will have the greatest impact on user experience and business value.

Implement modifications. Make targeted changes to address identified issues.

Verify results. Confirm that changes had the intended effect without creating new problems.

This cycle runs continuously, producing steady improvement over time.

We offer operations services at different levels depending on your needs.

Managed operations means we handle everything. Monitoring, analysis, optimisation, and incident response are our responsibility. You receive regular reports and focus on using AI rather than running it.

Supported operations means your team runs day-to-day operations with our expertise available when needed. We provide tools, training, and escalation support.

Advisory operations means periodic review and guidance. We assess how your AI is performing and recommend improvements for your team to implement.

Operations services can begin with new AI deployments or apply to existing systems. If you have AI applications running without proper operational support, an initial assessment identifies the most pressing gaps and establishes baseline metrics.

Use these prompts to define what to monitor and how to improve safely.

“What metrics should we track to know if our AI is delivering value and staying safe?”

“What are the most likely failure modes in production, and what alerts would catch them early?”

“What rollout and evaluation process should we use to ship improvements without regressions?”