Laurence

Quantitative research for autonomous e-commerce growth

3.2Score#332 of 999 agents#8 of 22 in Performance marketer

Why it ranks #332

Laurence, an AI performance marketer from YC W26, ranks #332 of 999 AI agents and #8 of 22 in Performance marketer, with a score of 3.2 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 4 × 30% 1.2
Scale 1 × 30% 0.3
Momentum 3 × 25% 0.8
Autonomy 6 × 15% 0.9
= 3.2

Proof
Do customers use it? 30% of the score
4
Claims a customer result and work done at scale.
- +2
  A customer result
  “Laurence advertises like modern hedge funds trade markets, driving a 15-40% improvement in net profit for Amazon brands within a month of onboarding.”
  YC page
- +2
  Work done at scale
  “Our quant system is automating $10 million in live ad spend, increasing advertising profits by 15-20% while growing total revenue.”
  Launch YC post · 5 Feb 2026
Not found: revenue or growth (+4), named customers (+3), a customer count (+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
3
Shows hiring and a new batch.
- +2
  Hiring: 1 open roleDeployment StrategistYC page
- +1
  New: YC Winter 2026
Not found: launch YC post in the last 6 months (+3), 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: 92% likely
- Finishes whole tasks: 45% likely
- Calls itself an agent: 13% likely
Level 2: Takes actions in other systems; a person approves key steps.

About Laurence

Advertising today is like trading in the 1980s pits: archaic, manual, and iterative guesswork. Humans update bids and keywords with noisy, delayed data using simple if-else rules that both break at scale and ignore stochasticity.

Laurence advertises like modern hedge funds trade markets, driving a 15-40% improvement in net profit for Amazon brands within a month of onboarding.