A deep dive into your AI system design — model selection, data pipelines, serving infrastructure, and integration patterns. We identify bottlenecks, single points of failure, and over-engineering.
Rigorous evaluation of your models against the metrics that matter for your business — not just accuracy, but latency, cost per prediction, fairness, and reliability.
A line-by-line breakdown of where your AI spend is going — compute, storage, API calls, data labeling — with concrete recommendations to reduce costs without sacrificing performance.
Identify security vulnerabilities, compliance gaps, and operational risks in your AI systems — before they become incidents.
Quantified assessment of accumulated technical debt in your AI systems — with a prioritized payoff plan based on risk and impact.
Load testing and capacity planning to determine whether your AI infrastructure can handle 10x your current traffic — and what breaks first.
Assess your dependency on specific cloud providers, model vendors, or tooling — and develop a mitigation strategy.
Evaluate the quality, completeness, and freshness of your training and inference data — the foundation that makes or breaks model performance.
Assess your team's AI/ML maturity, development processes, and operational practices — with recommendations for improvement.
A prioritized, phased plan to address findings — quick wins first, then structural improvements. Actionable, not aspirational.