Hey Product Hunt, I’m Alisson, founder of Ailin¹.

What if the next breakthrough in AI isn’t another model competing to be the best, but different intelligences working better together?

That is the idea behind Ailin¹ Collective Intelligence, which we’re launching today.

We believe the race to build stronger individual models is only part of the story. The other part is what becomes possible when their different strengths, perspectives, and capabilities stop working in isolation.

Ailin¹ makes that collaboration executable. It analyzes a task, selects suitable models, and determines how they should work together. Depending on the strategy, models reason independently, challenge assumptions, contribute specialized knowledge, critique one another’s outputs, and synthesize a response. Where correctness can be checked objectively, verification helps determine which answer to accept.

The difference isn’t simply which model answers. It’s how the answer is reached.

Behind the system is a continuously updated catalog of more than 100k+ models and 32 registered coordination strategies. A request activates only the eligible models its strategy needs, never the entire catalog. The goal is not to use the most models. It is to make their differences useful.

And knowing how to collaborate also means knowing when not to. When one model is the better fit, Ailin can use one. When independent perspectives and cross-checking justify the additional work, it can assemble a collective. You can set a spending limit and inspect which models participated, which strategy ran, and what the execution cost.

We’ve also put the thesis to the test. In our published experiment, a verifier-backed collective achieved 97% accuracy: 37 correct executions out of 38 across 10 machine-checkable tasks, compared with 68–82% for the four frontier models in the original comparison. This is a small, specific result, not a claim that collectives win everywhere. Our results also show where individual models remain the better choice.

That distinction matters to us. We’re not building a system that uses more AI for its own sake. We’re building toward better outcomes through the right form of intelligence for the problem.

The coordination engine is open source, and the benchmark scripts, data, and limitations are public. For developers, the collective is accessible through an OpenAI-compatible API, so exploring this approach does not require rebuilding an application from scratch.

My conviction is simple: a breakthrough from any model builder should be an opportunity to strengthen the collective, not a reason to start over.

Today, we’re inviting you to explore the project, request early access, challenge our assumptions, and help shape what comes next.

The first frontier was making individual models more capable. We believe the next is making intelligence collective.

What task would you bring to put Ailin¹ Collective Intelligence to the test?