HumanSignal

HumanSignal

Data labeling platform with human supervision

Overview

HumanSignal provides a data labeling platform that helps improve AI models by combining automation with human supervision. It offers Label Studio as a subscription-based tool with features like custom UI building, templates, team permissions, and workflow options, supporting multiple data types. Clients subscribe, label data using templates and configurable workflows, and use human review where needed, with options for integrations and advanced support. The goal is to help organizations produce high-quality labeled data at scale, tailored to their needs through a flexible platform and professional services.

About HumanSignal

Simplify's Rating
Why HumanSignal is rated
C+
Rated C on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & Analytics

Consulting

Enterprise Software

AI & Machine Learning

Company Size

51-200

Company Stage

Series A

Total Funding

$25.2M

Headquarters

San Francisco, California

Founded

2018

Simplify Jobs

Simplify's Take

What believers are saying

  • Label Studio used by 350,000 researchers and tens of thousands of organizations over four years.
  • Enterprise features provide role-based access, audit logs, and cloud or self-hosted deployment.
  • LLM-powered pre-labeling, bulk annotations, and synthetic data generation accelerate workflows.

What critics are saying

  • Scale AI forks open-source Label Studio, undercutting Enterprise subscriptions within 6-12 months.
  • Encord poaches computer vision clients with 40% faster workflows in 12-18 months.
  • Labelbox diverts 25% of cloud-native revenue via Vertex AI integrations in 6-12 months.

What makes HumanSignal unique

  • Label Studio Enterprise enables direct PDF OCR correction with labels and coordinates.
  • Prompts interface builds and tests LLM prompts for automated labeling with quality metrics.
  • Adala framework deploys autonomous agents for labeling without human intervention.

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Funding

Total Funding

$25.1M

Above

Industry Average

Funded Over

2 Rounds

Series A funding typically happens when a startup has a product and some customers, and now needs funding to scale. This money is usually used to grow the team, expand marketing, and improve the product. Venture capital firms are frequently the main investors here.
Series A Funding Comparison
Above Average

Industry standards

$15M
$8.2M
Discord
$15M
Canva
$25M
HumanSignal
$30M
Kalshi

Benefits

Remote Work Options

Performance Bonus

Growth & Insights and Company News

Headcount

6 month growth

-3%

1 year growth

-8%

2 year growth

-10%
TechCrunch
Dec 15th, 2022
Heartex’S Label Studio Makes Labeling Audio For Ml Easier

Heartex, the well-funded machine learning data labeling startup, is launching a major update to its platform today that will open up new use cases for its tools by making annotating audio files in the commercial and open source versions of its Label Studio a lot easier for its users. These updates include a new user interface, optimized for working with audio files, the ability to annotate longer files (up to two hours in length) and millisecond controls in the built-in annotation tool. There is also now a new rendering engine for displaying waveforms. In a previous update, Heartex also added to its tool features like frame-by-frame video object tracking and an overhauled annotation UI.The company says about 150,000 users are currently relying on Label Studio, with more than 95 million annotations created so far.Image Credits: Heartex“When we were originally thinking about building the data labeling solution, we did a lot of data scientist interviews,” Heartex co-founder and CEO Michael Malyuk told me. “And what we figured out is that many of them say they don’t have enough flexibility with existing tools. There is a tool for images, but you can only put a single image on the screen

TechCrunch
May 18th, 2022
Heartex raises $25M for its AI-focused, open source data labeling platform

Heartex, a data labeling firm, has raised $25 million in a venture funding round, bringing its total raised to $30 million.

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