Stop overpaying to run your agents. Kalibr routes every request to lower-cost model and tool paths without degrading performance.

Measures dollars per correct outcome on LLMs. Contamination-resistant: benchmark questions are generated fresh at runtime.

Caveman prompting, measured. A two-channel evaluation protocol scoring what input and output compression actually cost LLMs in dollars, accuracy, and surface-text fidelity across seven models and five benchmarks.

Tamper-evident, stranger-verifiable receipts for LLM cost-savings — anyone can recompute your caching/routing savings math offline, no trust in your dashboard required. Pure stdlib, zero-dependency.

AI Infrastructure Portfolio: agent orchestration, model routing, inference cost analysis — working code, real measurements.

Cost-aware LLM gateway: semantic cache + difficulty-based model routing that cuts spend (~66%) and latency vs always using the frontier model, measured against a baseline and gated in CI. Fully offline.

Inference cost allocation for autonomous AI agent collaborations — Shapley-fair splitting, congestion pricing, token metering. Part of the Agent Trust Stack.

Inference cost allocation for autonomous AI agent collaborations — Shapley-fair splitting, congestion pricing, token metering. Part of the Agent Trust Stack.