Optimization
Not every task needs the most powerful — or most expensive — model. We analyze your actual AI usage and match each workflow to the right model, cutting costs without cutting capability.
As AI usage scales, costs compound fast. Organizations running all workloads through frontier models often spend 5–10x more than necessary — because no one has analyzed which tasks actually need that capability and which ones work just as well with a faster, cheaper alternative.
Model selection, prompt optimization, caching strategies, and batching patterns can dramatically reduce your AI spend without any degradation in output quality. We audit your current usage, identify waste, and implement a tiered model strategy that balances performance and cost across every workflow.
What's Included
Full analysis of your current AI API usage — model selection, token consumption, latency profiles, and cost per workflow. We find where you're overspending.
Tiered model architecture matching task complexity to model capability — routing simple classification to fast small models, reserving frontier models for complex reasoning.
Systematic prompt engineering to reduce token usage while maintaining or improving output quality — often yielding 20–40% cost reduction on its own.
Semantic caching for repeated queries and intelligent batching for high-volume workloads — eliminating redundant API calls without touching your agent logic.
Evaluate whether fine-tuning a smaller model on your specific domain data can match frontier model performance at a fraction of the inference cost.
Dashboards tracking AI spend by workflow, model, and team — with alerts when usage patterns drift and optimization opportunities emerge.
Our Approach
Instrument your AI calls to capture model, tokens, latency, and cost per workflow. Establish your real baseline before making any changes.
Identify workflows where a cheaper model achieves equivalent output quality. Score each task on complexity, accuracy requirements, and volume.
Implement model routing, prompt improvements, and caching — measuring impact at each step to confirm savings without quality degradation.
Ongoing cost and quality monitoring as your usage evolves — with quarterly optimization reviews as new models and pricing emerge.
Let's Talk
Share your current AI usage patterns and we'll estimate your optimization potential before any engagement begins.