Estimate the text-token allowance for one AI meeting assistant workflow and scale it to a billing period. It accounts for spoken-word density, transcript conversion, prompt or meeting context, and downstream output such as summaries and action items. Use the result for context-window checks, quota planning, and cost modeling.
Tokens per task = prompt tokens + (audio minutes × spoken words per minute × tokens per word) + (transcript tokens × output allowance multiplier). Period budget = tokens per task × tasks.
The main result is a planning estimate for one meeting assistant workload or its available capacity. It is only as accurate as the pricing, model-speed, token-density, and workload assumptions entered. Use measured production values whenever possible and run high- and low-case scenarios before committing capacity or a customer price.
This planning estimate does not guarantee vendor billing, model performance, transcription accuracy, translation quality, or service-level objectives.
A 45-minute meeting at 145 words/minute and 1.33 tokens/word produces about 8,678 transcript tokens. With 1,800 context tokens and a 1.2× output allowance, the task budget is about 20,891 tokens.
Use representative production values or a weighted average from recent tasks. Peak values are better when testing capacity risk.
Not automatically. Increase workload, overhead, tokens, or utilization headroom to model retries and operational variance.
Yes. Enter each option’s measured speed, infrastructure assumptions, and labor or usage prices as separate scenarios.
Headroom helps absorb variable media length, traffic bursts, startup time, retries, and queueing without immediately missing targets.
Update it after pricing, models, hardware, workflow stages, average task size, or concurrency patterns change.
| Variable | Meaning |
|---|---|
| Workload | Media length, source volume, or concurrent demand entered above. |
| Efficiency | Measured model rate, token density, batching gain, or utilization assumption. |
| Overhead | Workflow work outside the primary model calculation. |
| Result | Estimated requirement or capacity before unmodeled operational variance. |