The problem
AI agents never learn from user corrections, so they repeat the same mistakes
What they do
Continual-learning platform that retrains AI products on their real usage
Traction Early customers Clay, Harvey, Decagon, Mercor, Rogo; revenue not disclosed
Hiring trend
- Open roles
- 6
- Ops roles
- 1
- Headcount
- ~15
12 staff (Aug 2026) to ~17 (LinkedIn); founded May 2026
Hiring in
- San Francisco
Funding
- Last round
- $40M
- Stage
- Series A
- Date
- Aug 2026
Led by Sequoia Capital
- Hiring+16 open roles on the live job board (3–9 roles = +1)
- Funding recency+1.5Series A · $40M, Aug 2026 (2 months ago): 6 months or less = +1.5
- Traction+0.75Some growth: Early customers Clay, Harvey, Decagon, Mercor, Rogo; revenue not disclosed
- Red flags0None found in the last 24 months
- Total3.251 + 1.5 + 0.75 − 0 = 3.25 → 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
Months old with marquee AI customers; no revenue shown yet.
Open ops roles · 1
Last checked Oct 25, 2026