AI & LLM cost optimization
Know what your AI costs to get the job done.
The bill shows how much you spent. It may not show which workflows were worth it. We trace AI usage to completed tasks, find avoidable context and retries, and test changes against an agreed quality benchmark. The goal is a more efficient AI system, not a cheaper answer that creates extra work for your team.
Discuss this workflowWhat this could look like
Illustrative scenario, not a client case study
A reporting agent repeatedly sends a large document set and retries when a tool fails. We measure the cost per accepted report, retrieve only relevant evidence, set retry and turn limits, and compare candidate models. Changes ship only after the team's quality tests and failure checks pass.
What you receive
- Usage breakdown and cost per successful business task
- Prioritized recommendations with implementation effort and tradeoffs
- Benchmark results for selected optimizations
- Budget alerts, execution limits and an operating review plan
How we measure whether it works
Compare cost per accepted output, quality, latency and total review time. Separate provider charges from integration, hosting and maintenance costs. Record a baseline before claiming savings.
What to know before you start
No percentage saving is guaranteed. Provider features and pricing change, and a smaller model can cost more overall if it creates rework. Caching and batching only apply where the provider and workflow support them.
Common questions
How can we reduce LLM token costs?
Start with attribution by workflow. Test smaller relevant context, better retrieval, supported caching, model routing, shorter outputs, batching and bounded agent loops. Keep the changes that meet your quality and reliability thresholds.
Is a cost audit worth it for a small AI bill?
Often not as a standalone engagement. If the likely recoverable waste is less than the cost of the audit and ongoing maintenance, simple budget limits and a lighter review may be the better choice.
Bring us one process worth improving.
Describe the work, the systems it touches and the result you want. We can explore fit before proposing a defined scope and fee. Do not include confidential records.
We agree on scope, acceptance tests and commercial terms before work begins. Implementation, third-party usage and ongoing support are itemized.