Expected Parrot

Simulate your customers with AI agents

2.7Score#434 of 999 agents#5 of 11 in Market researcher

Why it ranks #434

Expected Parrot, an AI market researcher from YC F25, ranks #434 of 999 AI agents and #5 of 11 in Market researcher, with a score of 2.7 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 3 × 30% 0.9
Scale 3 × 30% 0.9
Momentum 0 × 25% 0.0
Autonomy 6 × 15% 0.9
= 2.7

Proof
Do customers use it? 30% of the score
3
Claims named customers.
- +3
  Named customers
  “We’re working with tech companies like Upwork, a global consulting firm, and users in banking, entertainment, consumer products, government statistical agencies, and non-profits.”
  Launch YC post · 4 Nov 2025
Not found: revenue or growth (+4), 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
5 people.
- +3
  5 people on its YC page5 to 9 people
Not found: funding round in its news (+3)
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 Fall 2025) (+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: 12% likely
- Finishes whole tasks: 12% likely
- Calls itself an agent: 99% 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 Expected Parrot

Expected Parrot helps companies simulate their customers with AI agents to explore pricing, product, marketing, communications and other scenarios at scale. Our open-source library and no-code web app let you design custom agent personas and conduct interviews and surveys with them using LLMs of your choice to generate results and reports. Everything is cached and reproducible, and you can send the same surveys to human respondents to validate results in the same interface.