Familiar

Multimodal Translation

2.8Score#403 of 999 agents#14 of 25 in Video and audio producer

Why it ranks #403

Familiar, an AI video and audio producer from YC S26, ranks #403 of 999 AI agents and #14 of 25 in Video and audio producer, with a score of 2.8 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 3 × 30% 0.9
Momentum 4 × 25% 1.0
Autonomy 6 × 15% 0.9
= 2.8

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
3
8 people.
- +3
  8 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
4
Shows a recent launch and a new batch.
- +3
  Launched in the last 6 monthsFamiliarLaunch YC post · 14 Aug 2026
- +1
  New: YC Summer 2026
Not found: 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: 9% likely
- Finishes whole tasks: 44% likely
- Calls itself an agent: 2% 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 Familiar

Familiar is the Neolab for world translation. Dubbing into 30 languages. Now with Familiar, the same actor's performance is preserved instead of a mismatched voice over a mismatched face. One model does the voice, the lips, and the scene together, with identity permanence and scene & world understanding. Built by the researchers who pioneered joint audio-visual human models.