Eos AI

Autonomous OS for healthcare

2.1Score#681 of 999 agents#20 of 29 in Medical biller and coder

Why it ranks #681

Eos AI, an AI medical biller and coder from YC W26, ranks #681 of 999 AI agents and #20 of 29 in Medical biller and coder, with a score of 2.1 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 2 × 30% 0.6
Scale 1 × 30% 0.3
Momentum 1 × 25% 0.3
Autonomy 6 × 15% 0.9
= 2.1

Proof
Do customers use it? 30% of the score
2
Claims a customer result.
- +2
  A customer result
  “In our early clinics, we are seeing \~3x productivity improvements in admin workflows and 37% revenue recovery by identifying early intervention points and automating operational workflows.”
  Launch YC post · 5 Mar 2026
Not found: revenue or growth (+4), named customers (+3), 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
1
Shows a new batch.
- +1
  New: YC Winter 2026
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)
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: 72% likely
- Finishes whole tasks: 22% likely
- Calls itself an agent: 4% likely
Level 2: Takes actions in other systems; a person approves key steps.

About Eos AI

Eos helps clinics turn their data into actionable insights to identify more eligible patients and early care interventions.

We harmonize historical data across all systems to create standardized patient data distributions, acting like a translation layer between different applications. Then we build a centralized index over it: a compressed representation that lets petabytes of raw data stay where it is while allowing us to search and reason across it as one system.

Instead of treating the visit or document as an isolated instance, we can look at it as a continuous story. We can predict how a case will resolve, trigger the required approvals and actions, and do so while reducing staff workload and recovering hospital revenue.