Build any multi-step AI pipeline, set each step's reliability, and see the end-to-end success rate compound in real time. Works for RAG pipelines, agentic workflows, classification chains, or any sequential AI system.
Needsyour pipeline steps and a reliability estimate for each — cost per call is optional
Givesend-to-end success rate, cost per successful task, and your weakest step
If every step in a 5-step pipeline runs at 90% reliability, end-to-end task completion is only 0.90⁵ ≈ 59%. Each step you add multiplies the failure risk.
Load a template or build your own pipeline below. Name each step, set its reliability %, and optionally set an estimated cost per call (in $) to see total cost per task.
#Step nameReliability %Cost / call $
End-to-end success rate
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Pipeline metrics
Cumulative success rate — step by step
Your pipeline as a diagram
Drawn from the steps above. Color shows how much success survives to that point — teal at 85% or more, amber from 65%, red below 65%, the same bands as the verdict. The outlined box is the least reliable step: fix it first. Copy it as Mermaid to drop into a PRD, GitHub, or Notion — they render it as a diagram — or download an image for slides.
Your move
Use this before architecture decisions, not after. If the end-to-end rate drops below your shipping threshold (see the Go/No-Go Rubric), the answer is usually fewer steps or human checkpoints between steps — not higher individual-step accuracy. A 4-step pipeline at 88% per step outperforms a 7-step pipeline at 95% per step. Every step you add is a tax on the whole system.
Step reliability is the probability that this step produces a usable output given a good input from the prior step. Independence assumption: this calculator treats steps as independent — in practice, correlated failures (e.g. a bad retrieval affecting every downstream step) can make real end-to-end rates even lower. Cost per task includes all call costs on successful and failed paths — failures still consume tokens. See ROI Calculator for full unit economics.