We built an AI directory, but decided not to sell rankings

When we started building AIFinanceTools, we looked at dozens of AI directories.

Most had the same problems:

• Every tool looked "Top Rated."

• Sponsored listings were hard to distinguish from editorial recommendations.

• It was difficult to understand which tools were actually worth trying.

As founders ourselves, we wanted something we'd actually trust.

So instead of building the biggest AI directory, we focused on building one that answers a simple question:

"If I work in finance, which AI tool should I actually use?"

That led us to make a few decisions that weren't necessarily the best for short-term revenue:

• No sponsored rankings.

• Every tool is reviewed using the same methodology.

• We score products based on practical criteria such as features, usability, pricing, integrations, and overall value.

• We keep comparisons transparent so users can understand why one tool scores higher than another.

Building the database has been much more time-consuming than we expected.

The hard part isn't collecting AI tools—it's reviewing them consistently.

Every week, dozens of new finance AI products launch, and many disappear just a few months later. Keeping information accurate has become a bigger challenge than writing code.

One unexpected benefit is that we've learned a lot about the AI finance landscape.

A few trends we've noticed:

• AI bookkeeping and accounting tools are improving rapidly.

• Workflow automation is becoming more valuable than standalone AI chatbots.

• Many startups solve the same problem with different positioning, making objective comparisons increasingly important.

We're still early, but we're curious:

If you were building an AI directory today, would you prioritize having the largest database, or the most trustworthy reviews?

I'd love to hear how other founders think about this trade-off.