Free product tools

AI readiness assessment Find the workflow worth automating first.

Score an AI workflow against task volume, data readiness, measurable quality, failure impact, human oversight, and operational ownership.

Clean build · Fast delivery · Scalable foundation · Less burn

Built for

Product and operations teams evaluating an AI feature or internal automation idea.

Intended outcome

A readiness verdict, risk posture, production-gap checklist, and a concrete first experiment matched to the selected workflow.

What matters

Ship the useful part. Kill the rest.

01

Score six readiness dimensions

02

Match guardrails to failure impact

03

Generate a measurable first experiment

Interactive planner · runs in your browser

AI feasibility inputs

Prove the workflow before buying the hype.

AI readiness verdict

Promising. Close the gaps.

75/ 100

Recommended first experiment

Create 30 answerable and 20 unanswerable questions, then measure grounded answers and correct refusals.

Evidence missing

data signal

Pilot safely

risk posture

What to fix before production

Collect representative, permissioned examples before choosing a model or vendor.
Define a scoring rubric, unacceptable failures, and a non-AI baseline.
Name a human owner and design escalation, correction, and fallback paths.
Make uncertainty visible and let users correct the system without friction.
Model latency, per-task cost, peak load, and rate limits against real usage.

A high score means the workflow is ready for a controlled evaluation—not that AI quality, safety, or return on investment is guaranteed.

No account, upload, or database. Your selections stay in this browser session.

What you get

Working outputs. No strategy confetti.

Readiness score and verdict
Evidence and operating gaps
Risk-specific guardrails
Recommended pilot design
How it works
01

Choose one bounded AI workflow

02

Rate volume, data, risk, and oversight

03

Check evaluation maturity

04

Run the smallest measurable experiment

Straight answers

What you should know before spending money.

Do we need a large dataset?

Not always, but you do need representative examples to evaluate quality. The necessary volume depends on task variability and risk.

What does AI-ready data mean?

Accessible, permissioned, relevant, reasonably current data with enough structure or context to support the selected workflow.

What is the best first AI use case?

A repeated, bounded workflow where imperfect automation still saves time, quality can be measured, and uncertain cases can be reviewed.

Enough research

The next useful artifact is working software.

Bring the workflow, idea, or delivery mess. We’ll cut it to the leanest credible build and tell you which partnership model fits.