Floatingpoint
Floatingpoint builds high-fidelity visual environments, evaluations, and supervised datasets to advance artificial visual intelligence. The company offers public and private benchmarks for frontier vision capabilities, plus custom data pipelines co-designed with ML teams through a structured alignment, tooling, golden set, and scale process. Every data point is backed by in-house training and thorough QA.
- Artificial Intelligence
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Visual intelligence lags behind textual reasoning, which has advanced rapidly in domains like software engineering, law, and mathematics. The bottleneck is a shallow data layer trying to feed a deep and wide modality, preventing frontier AI systems from achieving comparable breakthroughs in vision-based tasks.
Solution
Floatingpoint builds high-fidelity visual environments, evaluations, and supervised datasets to push the frontier of artificial visual intelligence. The company provides public and private benchmarks for measuring frontier vision capabilities, alongside custom data pipelines co-designed with ML teams to unlock targeted vision features. Their process follows a structured methodology: alignment on schema and annotation guides, custom QA and ML-assist tooling, a small golden dataset for validation, then scale. Every data point is backed by in-house training and thorough QA, ensuring fresh, diverse data with meaningful learning signal.
Target Audience
Primary customers are machine learning teams building frontier vision models who need high-quality supervised data and rigorous evaluation benchmarks to unlock targeted vision capabilities.
Features
- Public and private evaluation benchmarks for measuring frontier vision capabilities
- Custom supervised dataset pipelines co-designed with ML teams through a four-step alignment, tooling, golden set, and scale process
- In-house training and thorough QA backing every data point
- Custom annotation tooling and ML-assist systems tailored to each project
- Schema, distribution, and annotation guide co-design with clients, including edge-case surfacing
- Document AI expertise stemming from the team's work on Chunkr, including contributions to RealDocBench