Choose what to get done
Recommend a skim, penetration, or neutral launch-pricing strategy
I get a defensible launch-pricing call (skim, penetration, or neutral) that follows from an explicit read of the decision factors, so the strategy is not a guess.
Revenue Zone Matrix prospect classification grid
I get a structured grid that plots each of my prospects on demand (awareness/interest/demand) and trust (know/like/trust) and derives which of the three zones they sit in, with no prospect left unplaced.
SaaS magic number
Your SaaS magic number (annualized new revenue over prior-quarter sales-and-marketing spend) with an efficiency band telling you whether to scale acquisition.
Score a content piece on the STEPPS virality framework
The buyer gets a complete STEPPS scorecard: all six factors (social currency, triggers, emotion, public, practical value, stories) rated as whole numbers on the agreed scale, each with a written rationale that clears a minimum word count, plus a total_score that equals the sum of the six scores.
Score a set of OKRs (0.0-1.0) and run the six classic-trap litmus checks
You get your OKR set scored on the 0.0-1.0 scale (each objective = the average of its key-result completion rates), each objective color-banded on the Google red/yellow/green scale, and a structural pass/fail against the six classic OKR-writing traps.
Score a term sheet's economic terms clause-by-clause
A structured JSON scorecard that classifies every required economic clause of the term sheet onto a three-tier favorability scale and computes a weighted founder-friendliness index.
Score and rank candidate pricing metrics on the six-criteria rubric
I get a filled scorecard where every candidate metric is rated 1-5 on all six criteria with a weighted total and a clear rank order, so I can pick the key pricing variable defensibly.
Score and rank causes by importance, tractability, neglectedness
A function that takes a list of candidate causes each rated on importance (scale/severity), tractability (feasibility of progress), and neglectedness (how underfunded), and returns each cause's composite priority score plus a stable descending rank.
Score Problem Candidates on the Ikigai Four-Lens Fit
A founder receives a structured four-lens score for each candidate problem and a shortlist of those that clear every lens, so they pick a problem to focus on rather than a buzzword.
Screen Startup Against Incubator/Accelerator Eligibility Criteria
A founder receives a structured eligibility screen showing, criterion by criterion, whether the startup meets a given incubator or accelerator program's stated rules and whether it clears the overall must-have gate.
SETDA AI Outcome Discovery Register
A product team running AI discovery gets a structured register of candidate AI outcomes, each correctly categorized (revenue / cost / risk) and mapped to a Sense/Explain/Think/Decide/Act method, with needs and requirements broken out per the SETDA framework.
Shared value opportunity register
A validated shared value opportunity register where every opportunity has both a business benefit and a societal benefit, is mapped to a pathway, spans at least 2 distinct pathways, and has a feasibility tier.