Secure AI Cost Leak Audit
Get Implementation-Ready Fixes to Cut AI Costs in 7 Days. For CTOs Who Need Code-Level Precision.
Peter C. Bennett
AWS/LangChain/OpenAI production systems
The Problem
Token costs dropped while AI bills exploded. Your leadership demands code-level answers. Internal teams delivered minimal P&L impact after months of work.
Monitoring tools show what is expensive but can't explain why at line-number precision or how to fix it without breaking production.
Your team ships features, not cost forensics. Most consultants offer surface recommendations when you need exact file paths, refactor instructions, and ROI per fix.
The Solution
The 6-Step LLM Code Audit. I review your LLM integration code to find cost leaks in prompt architecture, context assembly, error handling, model routing, caching implementation, and agentic workflow patterns.
You get exact line numbers with why it leaks and how to fix it. Implementation-ready refactors with engineering effort estimates.
7 days, $7,500 fixed vs $30K-$100K for traditional technical due diligence.
How It Works
Day 0: 30-min call on AI spend, tech stack, deployment. You grant read-only GitHub access or encrypted export.
Days 1-2 (Validation): I clone code to secure AWS environment (MFA-protected). Analyze: prompt construction, context assembly, error handling, model selection, caching, agentic workflows. Audit for multi-agent token waste, semantic caching gaps, missing circuit breakers, context chunking issues, model routing inefficiencies. Initial findings with line numbers delivered.
Decision Point: Credible issues? Proceed to full audit. Nothing found? Stop, pay nothing.
Days 3-7: Systematic review of all LLM call sites. Refactor instructions, savings estimates, effort hours documented.
Day 7: Report delivered, 60-min walkthrough. Code deleted from AWS, cryptographic proof provided.
What You'll Receive
Executive Summary — Leadership presentation with risk matrix, quantified savings, Week 1 priorities.
Code-Level Findings — File paths, line numbers, refactor instructions. Ship first fix same day.
30-Day Roadmap — Sprint-ready tasks prioritized by ROI with hour estimates.
Model Routing Strategy — Decision matrix showing cost impact per task type.
Caching Patterns — Provider-specific implementation (OpenAI, Anthropic, Azure, Google).
Agentic Optimization — Multi-call consolidation with exact reduction points.
No-Risk Start
48-hour validation: I analyze your code and identify specific issues. Real findings with line numbers, or we stop and you pay nothing.
If issues are credible, you decide whether to proceed with full 7-day audit. Pay 25% at start, 75% at delivery.
Fast movers book, validate, and fix in under 2 weeks.
Next Step
Email peter@petercbennett.com to discuss your AI spend. I'll respond within 1 hour.