The AI Cost & Performance Audit is KitelyTech's fixed-scope audit of the AI applications and integrations an organization has built or commissioned: systems making model calls, where cost and performance live in the architecture, not the license invoice. It identifies exactly what data reaches the model on each call, what each call costs, and where latency or model selection is misaligned with need.
A full inventory of what reaches the model on each call: prompt content, retrieved context, system instructions
A per-query and monthly cost breakdown by model, integration, and workflow, built from token-level analysis
Identification of waste patterns: oversized context, redundant retrieval, an unnecessarily large model for the task
A written optimization recommendation
One findings presentation
Implementation of the optimization recommendations: scoped separately
Ongoing cost monitoring after the initial audit
License and seat reporting for chat products: the platforms' own admin dashboards already provide this, and this audit is not a repackaging of them
Formal load or stress testing
Security or governance assessment of the system itself: see AI Security Audit
Timeline: 1–2 weeks, confirmed in scoping.
Price: fixed for the defined scope, set during scoping.
The pattern here is rarely dramatic; it's compounding. A retrieval step that pulls in more context than the question needs, a model tier chosen for the hardest query type and applied to every query regardless of complexity, a workflow nobody's re-priced since it was first built. None of it looks like a problem in isolation. Multiplied across every call in production, it becomes a cost structure nobody actually chose.
No. The dashboard shows what you spent. This audit shows why: the retrieval step pulling more context than a question needs, the premium model answering routine queries, the workflow nobody has re-priced since launch. Those costs live in your architecture, and no vendor dashboard can see them.
No, and we won't sell it as if it did. Seat and license reporting is already in the platforms' own admin consoles. This audit is for AI applications and integrations you've built or commissioned, where the costs come from how the system is designed.
A full inventory of what reaches the model on each call, a cost breakdown by model, integration, and workflow, the specific waste patterns identified, and a written optimization recommendation. One findings presentation closes it out.
That's scoped separately once you've seen the findings. Keeping the audit and the fix separate keeps the recommendation honest; we're not auditing our way into work you don't need.
