ResearchAudio evidence lab

Eight instruments for the decision behind the demo.

Audit the claim, budget tokens and voice latency, calculate real operating cost, inspect the production loop, stress-test the business case, then configure the coding agent.

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Instrument 01 / claim

AI launch evidence scorecard

Seven checks expose the gap between a launch claim and decision-ready proof.

Score a launch → Instrument 02 / economics

Cost per successful task

Calculate the retry-adjusted cost after model calls and human review.

Calculate real cost → Instrument 03 / reliability

Agent loop diagnostic

Find missing termination, validation, recovery, and escalation guardrails.

Inspect a loop → Instrument 04 / business case

AI agent ROI stress test

Price failed automations, human review, recurring cost, and implementation payback.

Stress-test the ROI → Instrument 05 / token budget

LLM API cost calculator

Estimate monthly spend from traffic, input and output tokens, current prices, prompt caching, retries, and attached tools.

Build a token budget → Instrument 06 / cache economics

Prompt caching cost calculator

Model cache reads, cache writes, monthly savings, and the hit rate required to break even.

Find the break-even rate → Instrument 07 / developer workflow

Codex CLI config.toml generator

Generate project document fallbacks and the instruction byte limit without displacing AGENTS.md.

Build the config → Instrument 08 / conversational latency

Voice AI latency calculator

Budget endpointing, transcription, parallel fast and slow model branches, TTS, and playout.

Map the silence →

Lab protocol

Start with the decision.
A metric matters only if it changes what you do.

Name the hidden denominator.
Retries and failures belong in the result.

Leave with a next test.
Every score should create an inspection, not a vibe.