Yoneda Labs

Foundation Model for Chemical Manufacturing

1.8Score#768 of 999 agents#13 of 16 in Scientist

Why it ranks #768

Yoneda Labs, an AI scientist from YC W24, ranks #768 of 999 AI agents and #13 of 16 in Scientist, with a score of 1.8 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 3 × 30% 0.9
Momentum 0 × 25% 0.0
Autonomy 6 × 15% 0.9
= 1.8

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
3
3 people, raised $4.0M.
- +2
  3 people on its YC page3 or 4 people
- +1
  Raised $4.0MYoneda Labs raises $4M to build the 'OpenAI for chemistry' | VentureBeatSource · 26 Apr 2024
Momentum
Is it shipping and growing now? 25% of the score
0
Nothing new in the last 6 months.
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), a batch in the last year (it is YC Winter 2024) (+1)
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: 11% likely
- Finishes whole tasks: 9% likely
- Calls itself an agent: 2% likely
- Level 0 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 Yoneda Labs

Yoneda Labs provides software to help chemists optimise reactions. When chemists make a drug or a material, we help them figure out the best reaction parameters such as temperature, concentration and catalyst.

When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process.

Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation.

Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab.

Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.