Superb AI
Automation-first training-data platform: Curate, Label, and Model modules for computer vision.
About
Superb AI is a training-data platform for computer vision founded in 2018 by Hyun Kim and fellow ML practitioners, headquartered in Seoul with an office in San Mateo, California. Its suite has three modules: Curate for dataset curation and edge-case discovery, Label for in-platform auto-labeling with models trained per use case, and Model for building and deploying. The company has raised about $11.3M and sells through AWS Marketplace, including a vision foundation model called ZERO. The pitch is data-centric: better-curated datasets over more raw labels, with the company claiming customers reach training-ready data faster. Those efficiency numbers come from the vendor's own experiments, such as an MS COCO curation study, not independent benchmarks.
What it can do
- Curate module for dataset curation and mislabel discovery
- In-platform auto-labeling models trained per use case
- Model module for building and deploying via API
- Managed annotation workforce option
- AWS Marketplace availability
- ZERO vision foundation model
Setup
Sign up on superb-ai.com or subscribe via AWS Marketplace; request a demo for enterprise.
Prerequisites
A Superb AI account. Enterprise plans are custom-quoted.
Use cases
Works with
Trust score — 6/10
Scored 6/10 (verified tier, metrics-v1). Evidence: founded 2018; ~$11.3M raised; AWS Marketplace listings; covered in independent comparisons. Strengths: coherent Curate/Label/Model suite, data-centric positioning, real funding. Docked for: (1) efficiency claims rest on vendor-run experiments; (2) closed commercial platform; (3) modest public customer evidence. License: proprietary commercial.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 7.0/10
- Maintenance · recency, releases, issue load
- 8.0/10
- Openness · license + docs
- 3.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · Founded 2018 by Hyun Kim and co-founders; HQ Seoul with San Mateo office; ~$11.3M raised (Craft).
- · Curate/Label/Model modules; ZERO vision foundation model; AWS Marketplace listings.
- · MS COCO curation efficiency figures are vendor-run experiments, flagged provider-claimed.
- · Manual review: independent comparisons corroborate product scope; public customer evidence modest.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.