Cajal

Scaling formal verification to accelerate scientific discovery

1.5Score#882 of 999 agents#4 of 4 in Code reviewer

Why it ranks #882

Cajal, an AI code reviewer from YC W26, ranks #882 of 999 AI agents and #4 of 4 in Code reviewer, with a score of 1.5 of 10. Data as of 3 Oct 2026.

The score adds four parts, each 0 to 10, by weight. Every point comes from public evidence. How scoring works ·

Proof 0 × 30% 0.0
Scale 1 × 30% 0.3
Momentum 1 × 25% 0.3
Autonomy 6 × 15% 0.9
= 1.5

Proof
Do customers use it? 30% of the score
0
No public claims of customers or revenue yet.
Not found: revenue or growth (+4), named customers (+3), a customer result (+2), a customer count (+2), work done at scale (+2)
Scale
Is there a real company behind it? 30% of the score
1
2 people.
- +1
  2 people on its YC page2 people
Not found: funding round in its news (+3)
Momentum
Is it shipping and growing now? 25% of the score
1
Shows a new batch.
- +1
  New: YC Winter 2026
Not found: launch YC post in the last 6 months (+3), open roles on YC (+2), new traction claim in the last 6 months (+2), funding news in the last year (+2)
Autonomy
How much of the job does it do on its own? 15% of the score
6
Supervised agent: takes actions in other systems; a person approves key steps.
- +6
  Level 2 of 4: Supervised agentTakes actions in other systems; a person approves key steps.
What the model read
- Acts in other systems: 31% likely
- Finishes whole tasks: 29% likely
- Calls itself an agent: 84% likely
- Level 1 from the model's reading of its pages; the evidence review found, with quotes, that its AI acts in the work, so the level is 2
Level 2: Takes actions in other systems; a person approves key steps.

About Cajal

Cajal (YC W26) is massively scaling formal verification to accelerate scientific discovery. We deploy superhuman AI mathematicians to high-impact applied domains, starting with quantum computing and finance.

We do this with Lean - a framework that allows us to formally verify any mathematical statement, grounding AI in truth and validating the tools discovered by our systems.