Prompt Efficiency Audit

Prove prompt savings before you change production traffic.

Bring a representative set of long prompts and the downstream behavior that matters. Receive a savings distribution, risky-removal review, paired evaluation, and guarded rollout recommendation.

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✓50–100 representative prompts

✓No production change required

✓Savings and latency distribution

✓Risk map and rollout gate

Inputs

Define the real workload.

The audit begins with prompts and success criteria that resemble the production distribution.

Representative prompt set and model configuration
Critical information and structure definitions
Current input cost, latency, and cache assumptions

Evidence

Compare original and compressed paths.

We separate economic savings from information preservation, grounding, and task performance.

Token and estimated net-savings distribution
Before/after removals and protected spans
Paired downstream evaluation on the same configuration

Decision

Leave with rollout conditions.

The output is a bounded decision, not a generic recommendation to compress everything.

Protection and workload profile
Failure categories and fallback recommendation
Proceed, narrow, or stop gate for production testing

Founding-customer program

A two-week audit, followed by an optional guarded rollout.

The initial program is founder-assisted and intended for production AI teams with long-context workloads, visible input cost, an engineering owner, and a willingness to share quantified results as a named or anonymized case study.

Find out whether compression is worthwhile for your prompts.

A useful audit can conclude that a workload should be compressed, narrowed to specific paths, or left unchanged.

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