Rasyn

Making chemicals 100,000x faster

2.8Score#413 of 999 agents#6 of 16 in Scientist

Why it ranks #413

Rasyn, an AI scientist from YC S26, ranks #413 of 999 AI agents and #6 of 16 in Scientist, with a score of 2.8 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 2 × 30% 0.6
Momentum 4 × 25% 1.0
Autonomy 8 × 15% 1.2
= 2.8

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
2
3 people.
- +2
  3 people on its YC page3 or 4 people
Not found: funding round in its news (+3)
Momentum
Is it shipping and growing now? 25% of the score
4
Shows a recent launch and a new batch.
- +3
  Launched in the last 6 monthsRasyn: One place to run any Chemistry taskLaunch YC post · 3 Aug 2026
- +1
  New: YC Summer 2026
Not found: 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
8
Autonomous worker: completes whole tasks; people handle exceptions.
- +8
  Level 3 of 4: Autonomous workerCompletes whole tasks; people handle exceptions.
What the model read
- Acts in other systems: 65% likely
- Finishes whole tasks: 61% likely
- Calls itself an agent: 7% likely
Level 3: Completes whole tasks; people handle exceptions.

About Rasyn

We builds foundational AI models for chemistry. Almost nothing you buy is a single chemical. Products like datacenter coolant, GPU thermal paste, paint, and glue are formulations, which are mixtures of around ten ingredients each, and demand for them has grown quickly because of AI and EVs.

Designing a new formulation is essentially a search problem, because there are more possible mixtures than anyone could ever test, and none of them can be predicted in advance. As a result, bringing a single formulation to market today takes three to five years of trial and error in a lab.

Our models predict how a mixture will behave far faster than the standard methods used today, which lets us design new formulations in weeks rather than years, at a fraction of the cost. To prove this, we used them to invent three chemicals that never existed before, including a PFAS-free coolant for datacenters, and we have since synthesized and patented all three.

We are already working with some of the largest manufacturers to design formulations for them, and we are building our own lab to generate the data that will train our next models.