Intro.
#Criterion 1 — Consistency
Does submitting the same business plan twice produce the same result? By default, AI models can generate a different response every time. A good AI review service either reliably promises the same result for the same input, or transparently discloses how much variance to expect.
- How to check — submit the same business plan 2-3 times and compare the results
- Warning sign — a score difference of ±5 points or more suggests weak consistency
- Acceptable range — varies by model and industry, but smaller is always better
TIP
Consistency works the same way as trusting a teacher who gives the same student a different grade every time — you can't. Objectivity is the core promise of an AI review, and there's no objectivity without consistency.
02
#Criterion 2 — Reasoning
Does it explain why you got 85, not just that you got 85? A good AI review pairs the score with the specific part of the business plan the reasoning was drawn from. A score with no reasoning behind it can't be a starting point for trust.
| Good result format | Bad result format |
|---|
| Problem recognition: 22/30 — '12 prospective-customer interviews are laid out in a table, satisfying the quantitative bar, but the why-now case is weak' | Problem recognition: 22/30 |
| Market opportunity: 18/25 — 'TAM is specified, but the SOM calculation is vague — needs more evidence for the 24-month acquisition target' | Market opportunity: 18/25 (average) |
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#Criterion 3 — Transparency
Is it public what criteria the evaluation uses, and what data it references? If the evaluation criteria themselves are undisclosed, users have no way to verify what the result actually means.
- Published evaluation criteria — the weighting of each P·S·S·T category, and how it varies by industry
- Reference data sources — what industry data, track records, and benchmarks are used
- Disclosed reviewer composition — clear on how many reviewers cover which areas
- Disclosed result variance — consistency metrics and the expected margin of error
주의
If all you get is marketing copy claiming 'AI did this,' with no disclosure of the evaluation criteria, data, or review structure, you have no way to verify what the result means. That's a sign of a trust deficit.
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#Criterion 4 — Dispute Path
If you believe the AI result misread the context of your business, can you raise an objection or request a re-review? Without a dispute path, the AI's output becomes an unappealable verdict, and the possibility of learning from it disappears too.
- A clearly stated dispute channel — email, a forum, or a contact form
- Whether a re-review is possible — re-evaluation after submitting additional materials
- Whether it learns — does user feedback actually improve future reviews
- A refund policy — for cases where the result is clearly inappropriate
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#Summary Table of All Four Criteria
| Criterion | Question to ask | User action |
|---|
| Consistency | Does the same input give the same result? | Submit 2-3 times and compare |
| Reasoning | Is the score's reasoning provided alongside it? | Check for citations on the results page |
| Transparency | Are the evaluation criteria, data, and structure disclosed? | Check the service page and terms of use |
| Dispute path | Is there a process to contest the result? | Check the FAQ and contact channels |
Summary.
#What OpenSeed Promises Its Users
- Consistency — an operating principle aimed at the same business plan producing the same result
- Reasoning — 15 reviewer personas provide category scores together with their reasoning
- Transparency — a published directory of the 15 reviewer personas, with P·S·S·T evaluation criteria disclosed
- Dispute path — an active channel for feedback and re-review requests
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