sieve

AI + human review to solve data cleaning - accessible via API or Excel

1.2Score#926 of 999 agents#7 of 7 in Data engineer

Why it ranks #926

sieve, an AI data engineer from YC Sp25, ranks #926 of 999 AI agents and #7 of 7 in Data engineer, with a score of 1.2 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 0 × 30% 0.0
Scale 1 × 30% 0.3
Momentum 0 × 25% 0.0
Autonomy 6 × 15% 0.9
= 1.2

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
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 Spring 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: 24% likely
- Finishes whole tasks: 22% likely
- Calls itself an agent: 95% 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 sieve

sieve solves data cleaning for hedge funds and investment firms by letting them get clean data in four lines of code. Currently, their data pipelines have conditions that raise for human review, which literally send an email to engineers with data that needs to be reviewed. We provide an API that integrates directly into their existing pipeline - instead of raising for human review, they can send all the same information to our API and get clean, high-quality data back.

By using our AI agents built specifically for financial data collection, along with expert-in-the-loop review, we provide our clients with clean, validated data at a scale and level of quality that wasn't achievable before.