Latka logo

Top 25 Data Extraction Software SaaS Companies in May 2026

As of May 2026, there are 25 SaaS companies in Data Extraction Software. They have combined revenues of $474.6M and employ 3.1K people. They have raised $1.1B and serve 6.8K customers combined.

Data Extraction Software is designed to enable the automated extraction of structured and unstructured data from various sources, such as documents, websites, and databases. These tools are essential for businesses seeking to convert raw data into usable formats, facilitating better decision-making and operational efficiency. Common use cases include gathering insights from customer feedback, enabling competitive intelligence, and ensuring compliance with regulatory requirements. Typically, data extraction software includes features such as data parsing, data cleansing, and integration with other analytics tools. It automates tedious data entry tasks, reducing the potential for human error and significantly increasing the speed of data processing. The primary users of this software often include data analysts, business intelligence teams, finance professionals, and IT departments tasked with data management.

Companies
25
Revenue
$474.6M
Funding
$1.1B
Employees
3.1K

Filters

Sorting: Highest -> Lowest

Filters
Revenue
All<$1M$1M-$5M$5M-$10M$10M-$100M$100M+
Funding (Locked)Growth (Locked)Team-Size (Locked)

Top Data Extraction Software Companies

Showing 10 of 5 companies ranked by annual revenue.

1
Actian

Santa Clara, California, United States

For over 50 years, we’ve been helping organizations around the globe confidently transform their business by simplifying how people connect, manage, and analyze data. Organizations trust Actian data management and data intelligence solutions to streamline complex data environments and accelerate the delivery of AI-ready data. Actian transforms complex data landscapes into AI-ready assets with Actian Data Intelligence – the platform that enables Fortune 100 leaders to discover, understand, and trust their data across any environment.

Revenue
$56.5M
Customers
-
Year founded
1980
Funding
-
Team size
514
Growth
-
2
Bitam

Tampico, Mexico

Bitam is an information technology and services company. It offers a SaaS ETL engine and KPI Online platform for data analysis and performance measurement.

Revenue
$20M
Customers
-
Year founded
2000
Funding
-
Team size
122
Growth
-
3
Airbyte

San Francisco, California, United States

Open Data Movement Platform

Revenue
$20M
Customers
-
Year founded
2020
Funding
$181.2M
Team size
154
Growth
-
4
Crux Informatics

San Francisco, California, United States

Provider of a cloud-based informatics platform designed to help companies discover and make use of relevant valuable data from a multitude of sources. The company's platform offers data engineering managed service that delivers easy access to actionable data, tools for data exploration and evaluation provisioning system, enabling businesses to extract value from their structured and unstructured data quickly and efficiently.

Revenue
$19.3M
Customers
-
Year founded
2017
Funding
$115.7M
Team size
126
Growth
115.79%
5
Coalesce.io

San Francisco, California, United States

Coalesce is the only data transformation and governance platform designed for the AI era. Built on a metadata-driven framework, Coalesce gives data teams the speed to build and deploy transformations 10× faster—while enforcing the standards, structure, and governance needed to scale sustainably. With Coalesce Catalog, transformation and metadata management come together in a single solution, enabling discovery, trust, and collaboration across the business. Whether accelerating AI-assisted migrations from legacy tools or future-proofing enterprise data architectures, Coalesce provides the guardrails and efficiency to keep data teams AI-ready. To learn more, visit https://coalesce.io.

Revenue
$14.5M
Customers
-
Year founded
2020
Funding
-
Team size
132
Growth
-

Inclusion Criteria

- Must automate the extraction of data from multiple sources including documents and websites - Should provide tools for data cleansing and formatting to ensure usability - Must enable integration with other software tools for analytics and reporting - Should allow for varying levels of data structuring depending on user needs - Must support compliance and regulatory data requirements in relevant industries - Not just for basic data collection; must also provide means for data analysis or reporting