Recommendation Methodology

A transparent scoring model for practical AI stack decisions. No vendor pay-to-play.

Base Tool Score

Trust

Company maturity, reliability, support, and risk profile.

9.1

ROI

Time saved or revenue created within a realistic first month.

8.7

Demo

How quickly the tool reaches an obvious aha moment.

8.2

Retention

Whether teams still use it 90 days after adoption.

8.9

What We Refuse

No pay-to-play rankings.

No scoring weighted by affiliate payout.

No hype-cycle tools without evidence of retention.

No duplicate tools doing the same job in one stack.

Quiz Match Signals

Applied after base score

Signal	Weight	What it means
Role fit	+25	Built for what the user actually does.
Goal fit	+25	Directly supports the chosen business outcome.
Pain fit	+12	Solves the stated friction point.
Budget fit	+10 / -30	Keeps recommendations inside the stated spend range.
Skill fit	+6 / -15	Avoids tools that are too complex for the user's comfort level.

Step 1

Score the catalog

Each tool receives base trust, ROI, demo, and retention scores.

Step 2

Apply quiz context

Role, goal, pain, budget, skill, and team size reshape the ranking.

Step 3

Pick workflow coverage

The engine selects top tools across different categories.

Step 4

Return rollout order

Results include budget, keep/drop guidance, and a 30/60/90 plan.