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Top 116 Natural Language Understanding (NLU) Software SaaS Companies in May 2026

As of May 2026, there are 116 SaaS companies in Natural Language Understanding (NLU) Software. They have combined revenues of $2.4B and employ 16K people. They have raised $2.3B and serve 600.1K customers combined.

Natural Language Understanding (NLU) software is a subset of artificial intelligence that enables machines to comprehend and interpret human language in a meaningful way. This technology is crucial for applications that require interaction between computers and humans, such as chatbots, virtual assistants, and sentiment analysis tools. By processing and analyzing language data, NLU systems convert unstructured text and speech into structured data that machines can understand. Typical features of NLU software include intent recognition, entity extraction, sentiment analysis, and language translation. These systems often leverage machine learning models to continuously improve their interactions based on user input. Common buyer personas for NLU solutions include IT professionals looking to enhance customer service interfaces, product managers seeking to integrate advanced communication capabilities, and data analysts aiming to extract actionable insights from textual data sources.

Companies
116
Revenue
$2.4B
Funding
$2.3B
Employees
16K

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Top Natural Language Understanding (NLU) Software Companies

Showing 10 of 2 companies ranked by annual revenue.

2
Mercor

San Francisco, California, United States

Our AI vetting identifies the best people in the world for a specific role. For example, our AI interviewer joins a video call and has a real-time conversation based on full context about a candidate's background.

Revenue
$500M
Customers
-
Year founded
2023
Funding
$485.6M
Team size
30
Growth
-

Inclusion Criteria

- Software must enable understanding and interpretation of human language. - Must include capabilities for intent recognition and entity extraction. - Should offer sentiment analysis as a standard feature. - Must support integration with user-facing applications like chatbots or virtual assistants. - Not just focused on text processing; must also handle spoken language inputs. - Must provide analytics or reporting features for usage insights.