The problem
AI agents misread enterprise data: no idea which numbers are trusted, who owns them, or what is allowed
What they do
Context layer that tells enterprise AI agents what company data means and who may use it
Traction Fortune 500 customers incl. AlphaSense and Zayo; Gartner Cool Vendor 2026; revenue undisclosed
Hiring trend
- Open roles
- 2
- Headcount
- ~30-34
Headcount +55% YoY (~34, LinkedIn); plans to double by end of 2026
Funding
- Last round
- $23M
- Stage
- Series A
- Date
- Sep 2026
Led by N47
- Hiring+0.52 open roles on the live job board (1–2 roles = +0.5)
- Funding recency+1.5Series A · $23M, Sep 2026 (1 month ago): 6 months or less = +1.5
- Traction+0.75Some growth: Fortune 500 customers incl. AlphaSense and Zayo; Gartner Cool Vendor 2026; revenue undisclosed
- Red flags0None found in the last 24 months
- Total2.750.5 + 1.5 + 0.75 − 0 = 2.75 → Steady (1.5–3.49)
Distance (Unproven) is a separate read on how long the lead can last. It doesn't change the score.
Distance
Unproven · edge: learns from use
Context graph compounds per customer, but only a handful of named accounts against Atlan, Collibra and Alation.
Sources: SiliconANGLE: Euno $23M Series A (Sep 2026) ↗ · N47: Why we invested in Euno ↗
Open ops roles
No ops roles open right now
Last checked Oct 14, 2026