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Top 21 Data Labeling Software Companies Under $1M Revenue (October 2026)

As of October 2026, Latka tracks 21 data labeling software companies under $1M in annual revenue. They have combined revenues of $10.4M and employ 121 people. They have raised $1.7M.

Every company below sells data labeling software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.

What Data Labeling Software Companies do

Data labeling software is designed to facilitate the process of annotating data, which is crucial for the development of machine learning and artificial intelligence models. Users of this software can label various data types, including images, audio, and text, providing the necessary annotations that allow algorithms to recognize patterns and make predictions. The software streamlines workflows, enabling large datasets to be processed efficiently and ensuring data quality through collaborative tools and automated features. Typical use cases for data labeling software include applications in computer vision for object detection, natural language processing for text classification, and audio analysis for speech recognition. With common features like user-friendly interfaces, quality control mechanisms, and integration capabilities with machine learning frameworks, this software empowers data scientists, AI developers, and researchers to prepare their data sets comprehensively. The primary buyers often include tech companies, research institutions, and enterprises looking to enhance their AI solutions and analytics capabilities.

Companies
21
Revenue
$10.4M
Funding
$1.7M
Employees
121

Filters

Sorting: Highest -> Lowest

Filters

Top Data Labeling Software Companies Under $1M Revenue

Showing 10 of 21 companies ranked by annual revenue.

1Redouble AI logo
Redouble AI

San Francisco, California, United States

Scale human-in-the-loop for AI workflows in regulated industries

Revenue
$900K
Year founded
2024
Team size
6
2AI Aware logo
AI Aware

Newcastle, United Kingdom

Current systems of AI -content detection using machine learning have proven to be inaccurate (around 90% success at spotting AI -generated content and up to 5-10...

Revenue
$880K
Year founded
2023
Funding
$519.6K
Team size
8
3Pointly logo
Pointly

Berlin, Berlin, Germany

Pointly is a cloud-based and user-friendly 3D labeling platform for all types of point clouds and enables fast training data generation for AI Use Cases. Combined with Pointly Services, where we offer tailormade 3D point cloud solutions (e.g. automated feature extraction or CAD data generation), we can support you with your projects from proof of concept to fully scalable solutions. Visit Pointly at www.pointly.ai and try it out for free!

Revenue
$770K
Year founded
2021
Team size
7
4TaQadam | We Make Visual Data AI-Ready logo
TaQadam | We Make Visual Data AI-Ready

New York, New York, United States

TaQadam transforms imagery collected by satellites and drones into valuable insights with humans-in-the loop, computer vision, annotation, and mapping technologies. We build solutions for businesses and sustainable development initiatives. With our mobile app and collaborative team model, we deliver a high quality annotation at scale.

Revenue
$770K
Year founded
2017
Team size
7
5RedBrick AI logo
RedBrick AI

Claymont, Delaware, United States

Rapid medical data annotation

Revenue
$699.2K
Year founded
2021
Team size
19
6Mundo AI logo
Mundo AI

San Francisco, California, United States

High Quality Multilingual Training Data for AI Models

Revenue
$660K
Year founded
2024
Team size
6
7BOUNTE logo
BOUNTE

Lake Forest, Illinois, United States

BOUNTE™ is a cloud-based technology that automates lost and found operations and minimizes staff contact with lost property. Our smartphone app uses AI image recognition to record and log items. Each item is stored in a barcoded BOUNTE bag and sealed for hygiene and security, and a return shipping wizard handles labeling. All contribute to a greatly enhanced customer experience. We take the pain and frustration out of lost and found.

Revenue
$660K
Year founded
2018
Team size
6
8RecTag logo
RecTag

Berlin, Germany

RecTag is a podcast discovery platform that discovers new and trending podcasts episodes and personalizes podcast experience for free. The platform trains a speech-to-text algorithm through podcasting (crowdsourced supervised learning) and aims to provide a high-performance transcription SaaS at scale. The AI enables content-based discovery beyondsimple chart lists.

Revenue
$644.2K
Year founded
2019
Team size
3
9Caplena logo
Caplena

Zurich, Switzerland

Developer of a text data analysis platform designed to provide deep learning for natural language processing and image recognition. The company's platform annotates machine learning training data, categorizes short texts, evaluates employee and customer feedback, surveys codes as well as efficiently codes open-ended questions, enabling market research professionals to reduce the effort of analyzing open-ended questions, save costs as well as improve data handling, data consistency and data versioning.

Revenue
$570.6K
Year founded
2017
Team size
22
10NaturalText logo
NaturalText

Ellicott City, Maryland, United States

How NaturalText A.I. works NaturalText A.I. uses novel artificial intelligence technology to uncover hidden relationships in text data. ​ The software uses various state-of-the-art methods to understand context, analyze patterns, and reveal insights—all in a human-readable way. Enhance enterprise search By comparing the content and context of search results, NaturalText generates human-readable popularity scores to rank results in a meaningful and understandable way. Never train learn-to-rank systems, extracting features, and binary models again! Discover similar documents Do you struggle to classify huge amounts of documents, emails, and other data? Does your current classification software make grouping decisions that are not always intuitive? NaturalText A.I. makes it easy to understand the logic behind grouping documents and other data—and you can even explore labeling for documents without any pre-training! Reveal insights hidden in your data Finding everything hidden in your text data is a difficult—if not impossible—task. With traditional search, you can only locate information related to a document. NaturalText A.I., on the other hand, uncovers new information within millions of documents, including scientific papers and patents. Use NaturalText A.I. to reveal insights in your data you are currently missing.

Revenue
$440K
Year founded
2015
Team size
4

Frequently asked questions about Data Labeling Software Companies Under $1M Revenue

How many data labeling software companies under $1M in annual revenue are there?

Latka tracks 21 data labeling software companies under $1M in annual revenue. Together they generate $10.4M in annual revenue and employ 121 people.

Which data labeling software company under $1M in annual revenue is the largest?

Redouble AI is the largest, with $900K in annual revenue, founded in 2024.

How much revenue does a typical data labeling software company under $1M in annual revenue make?

The average data labeling software company in this list makes $497.4K a year, across 21 companies with reported revenue.

Who are the leading Data Labeling software vendors under $1M in annual revenue?

Ranked by annual revenue, the leaders are Redouble AI, AI Aware, Pointly, TaQadam | We Make Visual Data AI-Ready and RedBrick AI.

How much funding have data labeling software companies under $1M in annual revenue raised?

The 21 data labeling software companies under $1M in annual revenue tracked here have raised $1.7M in disclosed funding between them.

Related Artificial Intelligence Software categories

Inclusion Criteria

- Must provide tools for labeling diverse data types including images, text, and audio. - Should support both manual labeling and automated annotation processes. - Must include collaboration features for teams to work on data labeling tasks. - Must ensure quality control mechanisms to verify the accuracy of labeled data. - Not just a data management tool; must also provide data annotation capabilities.