How we recommend
No algorithm magic. No vendor pay-to-play. Here's exactly how we decide which tools show up in your stack.
Every tool is scored on four things
Before a tool ever shows up in a recommendation, we hand-score it on four dimensions from 0–10. These scores are deliberately subjective, because the internet already has plenty of algorithmic review aggregators and they're mostly noise.
Is the company credible? Funded, staffed, not shutting down next quarter? Do they do right by their users when things break?
Does this tool actually save real time or make real money for people in the target role, within 30 days of adopting it?
Is the 'aha' moment obvious inside 5 minutes? Tools that take an hour to demo well have a higher drop-off rate.
Do people still use this 90 days in, or does it end up in the subscription graveyard? We care about keep-rate, not install-rate.
How we pick your stack from those scores
When you take the quiz, we score every tool in our database against your specific situation, layering five signals on top of the base scores:
- Role fit (+25). Is this tool built for what you actually do?
- Goal fit (+25). Does it directly help you achieve the outcome you picked?
- Pain fit (+12). Does it target the specific friction you said was biggest?
- Budget fit (+10 / −30). Hard filter. If it's out of your stated budget, we drop it.
- Skill fit. A beginner doesn't get handed an enterprise automation platform on day one.
Then we pick the top-scoring tool per category (never two tools that do the same thing), and return the 4–6 tools that together form a complete workflow for your goal.
What we refuse to do
- Pay-to-play. We don't accept payment to add, promote, or rank a tool. Ever.
- Weight recommendations by affiliate payout. Some tools have 30% recurring commissions; some have zero. The payout never shows up in the scoring formula.
- Recommend tools we wouldn't use ourselves. Plenty of tools have a big affiliate program and a bad product. We leave those out.
- Promote hype-cycle tools. If something just launched and hasn't proven retention, it doesn't get a high retention score yet. We'd rather be a few months late on a great tool than early on a burning one.
How we keep the database fresh
The AI tools market moves. We update the catalog regularly based on:
- New tools we've actually tested or seen working well in customer workflows
- Price changes, product pivots, or retention drops that move an existing tool's scores
- User feedback — if a recommended tool doesn't stick, we want to know
- Tools being sunset, acquired, or changing ownership in ways that affect reliability
Why you should trust this
You shouldn't. Not yet. Trust us as far as the first recommendation you try. If the stack we suggest makes sense and the first tool delivers value in your first week, keep going. If not, our recommendations aren't for you, and that's fair. We'd rather you bounce than pay for something that doesn't stick.