The general structure
Every Bayesian evaluation has the same structure: a falsifiable hypothesis, a set of predicates that evaluate evidence bearing on that hypothesis, and weights that reflect the relative importance of each predicate. This structure is not specific to startups. It applies to any decision where evidence can be gathered and evaluated.
The built-in domains evaluate startup readiness. Custom domains can evaluate anything. The method is the same — the hypothesis and predicates change to match the evaluation context.
Use cases beyond startups
Hiring decisions. Root hypothesis: IS(candidate, effective_in_role). Predicates: Has the candidate demonstrated the specific skill the role requires in a previous position? Is there a reference who can speak to the candidate's performance in a comparable context? Has the candidate named a specific failure and described what they learned from it? These predicates are specific and falsifiable. The evaluation produces a score, not a gut feeling.
Product launch readiness. Root hypothesis: IS(product, ready_to_ship). Predicates: Has the product been tested by users who were not involved in building it? Are all known P0 bugs resolved? Is there a rollback plan if the launch degrades key metrics? Does the support team know what the product does and how to handle common issues? Each predicate has observable evidence — either it is done or it is not.
Investment thesis. Root hypothesis: IS(investment, thesis_validated). Predicates: Is the market size estimate supported by primary research rather than top-down TAM math? Has the team demonstrated they can recruit talent against larger competitors? Is there evidence the unit economics improve at scale, not just in the model? The evaluation turns a narrative pitch into a structured evidence map.
Academic research quality. Root hypothesis: IS(paper, publication_ready). Predicates: Is the methodology clearly described and independently reproducible? Are the statistical claims supported by the reported effect sizes and sample sizes? Has the related work section addressed the most recent relevant literature? Each predicate is specific enough to fail.
How to design a custom domain
The fastest path is DNA extraction: paste the document you want to evaluate, and Bayescore generates a root hypothesis and predicates from it. Review and adjust the predicates before saving. The key question for each predicate: can I imagine a document that fails this? If not, it is too vague.
For domains you will use repeatedly — a hiring rubric, a launch checklist — invest more time in predicate design. Run a few evaluations, observe which predicates are consistently passing or consistently failing, and refine the ones that are not producing information. A predicate that always passes is not contributing to the evaluation.
The weights matter most when predicates have genuinely different leverage. For a hiring evaluation, demonstrated skill in the specific role is worth more than cultural fit indicators — not because culture does not matter, but because demonstrated skill is harder to train and faster to verify. Assign weights that reflect what you have actually learned about what predicts success in this specific context.
The common thread
Every domain that works well in Bayescore has the same property: the predicates are specific enough to fail. A hiring rubric where every candidate passes every predicate is not a rubric — it is a formality. A launch checklist where every item is always checked is not providing safety — it is providing comfort.
The value of structured evaluation is not the score. It is the discipline of defining, in advance, what evidence would change your conclusion. That definition forces precision. And precision produces information that informal evaluation consistently misses.
Drop any structured document
Paste a document and Bayescore extracts its evaluation DNA — root hypothesis, predicates, and weights — then scores it against its own standard.
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