# Best Natural Language Understanding (NLU) Software

  *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*

   Natural language understanding (NLU) software uses machine learning algorithms and statistical methods to help applications better understand human text, providing outputs such as part-of-speech tagging, sentiment analysis, named entity recognition, automatic summarization, emotion detection, and language detection from language inputs.

### Core Capabilities of NLU Software

To qualify for inclusion in the Natural Language Understanding category, a product must:

- Provide a deep learning algorithm specifically for human language interaction
- Connect with language data pools to learn a specific solution or function
- Consume language as an input and provide an outputted solution

### Common Use Cases for NLU Software

Developers and AI teams use NLU software to add human language comprehension capabilities to applications and services. Common use cases include:

- Powering chatbots and virtual assistants with intent recognition and multi-turn conversation understanding
- Enabling social media monitoring tools to analyze brand sentiment and detect mentions automatically
- Supporting translation and language detection applications across diverse linguistic data sources

### How NLU Software Differs from Other Tools

NLU is a specialized form of [natural language processing (NLP)](https://www.g2.com/categories/natural-language-processing-nlp) focused specifically on language comprehension and intent understanding, rather than the full spectrum of text processing tasks. NLU algorithms are examples of deep learning and may be offered as prebuilt capabilities within broader AI platform solutions, making them more focused than general NLP platforms that cover text generation and classification alongside understanding.

### Insights from G2 on NLU Software

Based on category trends on G2, intent recognition accuracy and ease of integration into conversational applications stand out as top capabilities. These platforms deliver improvements in chatbot understanding and reduction in misclassified user inputs as primary outcomes of adoption.





## Category Overview

**Total Products under this Category:** 75


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,700+ Authentic Reviews
- 75+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Best Natural Language Understanding (NLU) Software At A Glance

- **Leader:** [Claude](https://www.g2.com/products/claude-2025-12-11/reviews)
- **Highest Performer:** [Google NotebookLM](https://www.g2.com/products/google-notebooklm/reviews)
- **Easiest to Use:** [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews)
- **Top Trending:** [Claude](https://www.g2.com/products/claude-2025-12-11/reviews)
- **Best Free Software:** [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews)


## Top-Rated Products (Ranked by G2 Score)
### 1. [Claude](https://www.g2.com/products/claude-2025-12-11/reviews)
  Claude is a state-of-the-art large language model (LLM) developed by Anthropic, designed to serve as a helpful, honest, and harmless AI assistant. With its advanced reasoning capabilities and conversational tone, Claude excels in tasks ranging from complex coding to in-depth financial analysis, making it a versatile tool for developers, enterprises, and financial professionals. Key Features and Functionality: - Advanced Coding Capabilities: Claude Opus 4 leads in coding performance, achieving top scores on benchmarks like SWE-bench and Terminal-bench. It supports sustained, long-running tasks, enabling continuous work for several hours, which is ideal for complex software development projects. - Financial Analysis Tools: Claude integrates seamlessly with financial data platforms such as Databricks and Snowflake, providing a unified interface for market analysis, research, and investment decision-making. It offers direct hyperlinks to source materials for instant verification, enhancing the efficiency of financial workflows. - Extended Context Windows: With an enhanced 500k context window available in Claude Sonnet 4, users can upload extensive documents, including hundreds of sales transcripts or large codebases, facilitating comprehensive analysis and collaboration. - Tool Use and Integration: Claude's extended thinking capabilities allow it to utilize tools like web search during reasoning processes, improving response accuracy. It also supports background tasks via GitHub Actions and integrates natively with development environments like VS Code and JetBrains for seamless pair programming. - Enterprise-Grade Security: The Claude Enterprise plan offers advanced security features, including Single Sign-On (SSO), Just-in-Time Provisioning (JIT), role-based permissions, audit logs, and custom data retention controls, ensuring data safety and compliance for organizations. Primary Value and User Solutions: Claude addresses the need for a reliable and intelligent AI assistant capable of handling complex tasks across various domains. For developers, it enhances productivity through advanced coding support and integration with development tools. Financial professionals benefit from its ability to unify and analyze diverse data sources, streamlining research and decision-making processes. Enterprises gain from its scalable solutions and robust security features, enabling efficient and secure deployment of AI capabilities within their operations. Overall, Claude empowers users to achieve higher efficiency, accuracy, and innovation in their respective fields.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 206

**User Satisfaction Scores:**

- **Summarization:** 9.8/10 (Category avg: 9.0/10)
- **Language Detection:** 9.5/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 9.2/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Anthropic](https://www.g2.com/sellers/anthropic-b3e27488-b6f4-49c9-a8c7-d860a4207ff3)
- **HQ Location:** San Francisco, California
- **Twitter:** @AnthropicAI (1,203,150 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/anthropicresearch/ (4,116 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** IT Manager, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 57% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (40 reviews)
- Useful (40 reviews)
- Helpful (33 reviews)
- Accuracy (25 reviews)
- Communication (23 reviews)

**Cons:**

- Usage Limitations (37 reviews)
- Limitations (19 reviews)
- Limited Functionality (19 reviews)
- AI Limitations (17 reviews)
- Resource Limitations (16 reviews)

### 2. [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews)
  Make your content and apps multilingual with fast, dynamic machine translation available in thousands of language pairs.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 326

**User Satisfaction Scores:**

- **Summarization:** 8.7/10 (Category avg: 9.0/10)
- **Language Detection:** 8.8/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.8/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 53% Small-Business, 24% Enterprise


#### Pros & Cons

**Pros:**

- Translation Services (69 reviews)
- Ease of Use (62 reviews)
- Multilingual Support (47 reviews)
- Language Support (42 reviews)
- Accuracy (41 reviews)

**Cons:**

- Translation Accuracy (37 reviews)
- Expensive (33 reviews)
- Accuracy Issues (22 reviews)
- Translation Issues (18 reviews)
- Limited Language Support (17 reviews)

### 3. [Google Cloud Natural Language API](https://www.g2.com/products/google-cloud-natural-language-api/reviews)
  Derive insights from unstructured text using Google machine learning.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Summarization:** 8.6/10 (Category avg: 9.0/10)
- **Language Detection:** 8.8/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.6/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 55% Small-Business, 24% Enterprise


#### Pros & Cons

**Pros:**

- Application Development (1 reviews)
- Cloud Computing (1 reviews)
- Features (1 reviews)

**Cons:**

- Not User-Friendly (1 reviews)

### 4. [Amazon Comprehend](https://www.g2.com/products/amazon-comprehend/reviews)
  Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 71

**User Satisfaction Scores:**

- **Summarization:** 8.6/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.7/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,223,984 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 40% Mid-Market, 38% Small-Business


#### Pros & Cons

**Pros:**

- Access (1 reviews)
- Content Creation (1 reviews)
- Ease of Use (1 reviews)
- Insights (1 reviews)
- Insights Analysis (1 reviews)

**Cons:**

- Accuracy Issues (1 reviews)
- Expensive (1 reviews)
- Insufficient Training (1 reviews)

### 5. [Azure AI Language](https://www.g2.com/products/azure-ai-language/reviews)
  Azure AI Language is a managed service for developing natural language processing applications. Identify key terms and phrases, analyze sentiment, summarize text, and build conversational interfaces. Use Language to annotate, train, evaluate, and deploy customizable AI models with minimal machine-learning expertise.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 77

**User Satisfaction Scores:**

- **Summarization:** 8.2/10 (Category avg: 9.0/10)
- **Language Detection:** 8.5/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.1/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.4/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,105,844 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Company Size:** 42% Small-Business, 32% Enterprise


### 6. [Google NotebookLM](https://www.g2.com/products/google-notebooklm/reviews)
  The ultimate tool for understanding the information that matters most to you, built with Gemini 2.0


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 14

**User Satisfaction Scores:**

- **Summarization:** 10.0/10 (Category avg: 9.0/10)
- **Language Detection:** 9.4/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 7.9/10 (Category avg: 8.7/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google-f3801d18-1641-4e22-99de-30e7422a874d)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 43% Mid-Market, 36% Small-Business


#### Pros & Cons

**Pros:**

- Content Creation (2 reviews)
- Efficiency (2 reviews)
- Insights (2 reviews)
- Understanding (2 reviews)
- User Interface (2 reviews)

**Cons:**

- Inefficient File Management (1 reviews)
- Language Limitations (1 reviews)
- Limited Language Support (1 reviews)
- Poor Response Quality (1 reviews)

### 7. [Google Cloud AutoML Natural Language](https://www.g2.com/products/google-cloud-automl-natural-language/reviews)
  The powerful pre-trained models of the Natural Language API let developers work with natural language understanding features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Summarization:** 9.4/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.6/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Company Size:** 53% Small-Business, 27% Enterprise


### 8. [scite.ai](https://www.g2.com/products/scite-ai/reviews)
  scite is an award-winning research tool that helps users better discover, understand, and evaluate research through Smart Citations. Smart Citations display the context of the citation and describe whether the article provides supporting or contrasting evidence.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 25

**User Satisfaction Scores:**

- **Summarization:** 8.5/10 (Category avg: 9.0/10)
- **Language Detection:** 8.5/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 6.9/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [scite.ai](https://www.g2.com/sellers/scite-ai)
- **Year Founded:** 2018
- **HQ Location:** New York, US
- **LinkedIn® Page:** https://www.linkedin.com/company/sciteai/ (4 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Research, Higher Education
  - **Company Size:** 50% Small-Business, 12% Mid-Market


#### Pros & Cons

**Pros:**

- Useful (8 reviews)
- Ease of Use (7 reviews)
- Accuracy (5 reviews)
- Efficiency (5 reviews)
- Helpful (5 reviews)

**Cons:**

- Slow Performance (3 reviews)
- AI Limitations (2 reviews)
- Context Understanding (2 reviews)
- Poor Response Quality (2 reviews)
- Repetitive Content (2 reviews)

### 9. [Stanford CoreNLP](https://www.g2.com/products/stanford-corenlp/reviews)
  Stanford CoreNLP provides a set of natural language analysis tools that can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 10

**User Satisfaction Scores:**

- **Quality of Support:** 6.7/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Stanford NLP Group](https://www.g2.com/sellers/stanford-nlp-group)
- **HQ Location:** Stanford, CA
- **Twitter:** @stanfordnlp (183,666 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Small-Business, 20% Enterprise


### 10. [InMoment Experience Improvement (XI) Platform](https://www.g2.com/products/inmoment-experience-improvement-xi-platform/reviews)
  InMoment, the leader in improving experiences and the highest recommended CX platform and services company in the world is renowned for helping clients collect and integrate customer experience data to uncover the insights that enable the smartest actions. As the pace setters in applying award-winning AI, its global clients activate every byte of their experience data—from structured surveys and social reviews to unstructured conversations from call logs, emails, support tickets, and chat transcripts to breakdown data silos. This unique technology combined with in-house industry experts empower brands to gain ROI from their CX programs in half the time as its competitors. Unlock the true potential of every piece of customer data with InMoment. To learn more, visit inmoment.com


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 314

**User Satisfaction Scores:**

- **Quality of Support:** 9.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [PG Forsta](https://www.g2.com/sellers/pg-forsta)
- **HQ Location:** Salt Lake City, Utah
- **LinkedIn® Page:** https://www.linkedin.com/company/weareinmoment/ (502 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Product Manager, Customer Success Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 47% Small-Business, 39% Mid-Market


### 11. [MITIE: MIT Information Extraction](https://www.g2.com/products/mitie-mit-information-extraction/reviews)
  MITIE: MIT Information Extraction is a tool that include performing named entity extraction and binary relation detection for training custom extractors and relation detectors.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Summarization:** 8.3/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.9/10 (Category avg: 8.7/10)
- **Quality of Support:** 9.4/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [MITIE](https://www.g2.com/sellers/mitie)
- **Year Founded:** 1987
- **HQ Location:** London, UK
- **LinkedIn® Page:** https://www.linkedin.com/company/mitie (18,722 employees on LinkedIn®)
- **Ownership:** LON: MTO

**Reviewer Demographics:**
  - **Company Size:** 42% Enterprise, 33% Small-Business


### 12. [Level AI](https://www.g2.com/products/level-ai/reviews)
  Level AI is the intelligence and orchestration layer for customer experience. We analyze 100% of customer interactions across voice, chat, email, and messaging to turn unstructured conversations into measurable insights and automation. From Voice of Customer and journey insights to automated quality, real-time coaching, and AI agents, Level AI helps teams improve customer outcomes, operational performance, and profitable growth.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 194

**User Satisfaction Scores:**

- **Summarization:** 9.7/10 (Category avg: 9.0/10)
- **Language Detection:** 8.9/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 9.2/10 (Category avg: 8.7/10)
- **Quality of Support:** 9.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Level AI](https://www.g2.com/sellers/level-ai)
- **Company Website:** https://thelevel.ai/
- **Year Founded:** 2018
- **HQ Location:** Mountain View, US
- **Twitter:** @TheLevelAI (201 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/level-ai (210 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Quality Analyst, Supervisor
  - **Top Industries:** Consumer Services, Food & Beverages
  - **Company Size:** 58% Mid-Market, 30% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (78 reviews)
- Helpful (55 reviews)
- Efficiency (43 reviews)
- Accuracy (37 reviews)
- User Interface (34 reviews)

**Cons:**

- Inaccuracy (23 reviews)
- Slow Performance (17 reviews)
- Accuracy Issues (15 reviews)
- AI Inaccuracy (13 reviews)
- Translation Accuracy (13 reviews)

### 13. [Gensim](https://www.g2.com/products/gensim/reviews)
  Gensim is a Python library that analyze plain-text documents for semantic structure and retrieve semantically similar document.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Summarization:** 7.6/10 (Category avg: 9.0/10)
- **Language Detection:** 7.6/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 9.1/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [RaRe Consulting](https://www.g2.com/sellers/rare-consulting)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 53% Small-Business, 27% Enterprise


### 14. [NLTK](https://www.g2.com/products/nltk/reviews)
  NLTK is a platform for building Python programs to work with human language data that provides interfaces to corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 46

**User Satisfaction Scores:**

- **Summarization:** 7.4/10 (Category avg: 9.0/10)
- **Language Detection:** 7.0/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 7.4/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.2/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [NLTK Project](https://www.g2.com/sellers/nltk-project)
- **HQ Location:** N/A
- **Twitter:** @NLTK_org (2,316 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Scientist, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 52% Small-Business, 29% Enterprise


### 15. [openNLP](https://www.g2.com/products/opennlp/reviews)
  Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text that supports the common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution these tasks are usually required to build more advanced text processing services and includes maximum entropy and perceptron based machine learning.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Quality of Support:** 7.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF (66,116 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 64% Small-Business, 18% Mid-Market


### 16. [Tungsten TotalAgility](https://www.g2.com/products/tungsten-totalagility/reviews)
  The Industry’s Only Low‑Code, Integrated, End‑to‑End Intelligent Automation Solution Tungsten TotalAgility is a powerful all-in-one solution that combines document and process intelligence using the industry's leading capture, OCR, and process orchestration technology. Harness the Tungsten Intelligent Automation Platform, Tungsten TotalAgility, to go beyond AI-powered RPA by unlocking document intelligence, connecting disparate systems, and orchestrating human and digital workers to execute and automate workflows across your high-value business processes. • Document Intelligence: Apply cognitive capture and artificial intelligence to unstructured data to  automate and extract information and unlock data insights. • Process Orchestration: Orchestrate digital workflows in collaboration with users, systems , and data. • Connected Systems: Bring together your critical business systems— enterprise applications, legacy systems, mobile, chatbots, and more—across internal and external business processes. :: Successful Organizations Rely on TotalAgility :: Tungsten TotalAgility® streamlines building and deploying intelligent process automation so you can expand human and digital workforce capacity. Receive, execute, route, and report on workflow tasks from a single platform. Why customers choose TotalAgility? • Industry-leading document intelligence: Our intelligent document processing technology processes documents and data with the highest accuracy and speed. • Low-code automation: Powerful tools to build, deploy and accelerate enterprise automation. • End-to-end business process handling: A central intelligent automation platform to handle dynamic tasks, trigger automated rules and deploy on-demand workforce capacity. • Mobile engagement: Deliver enhanced customer experiences across any channel and any device.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 42

**User Satisfaction Scores:**

- **Summarization:** 10.0/10 (Category avg: 9.0/10)
- **Language Detection:** 10.0/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 10.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Tungsten Automation](https://www.g2.com/sellers/tungsten-automation)
- **Year Founded:** 1985
- **HQ Location:** Irvine, US
- **Twitter:** @TungstenAI (6,444 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/tungstenautomation/ (1,513 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Banking, Information Technology and Services
  - **Company Size:** 53% Enterprise, 31% Mid-Market


### 17. [Marvin AI](https://www.g2.com/products/marvin-ai/reviews)
  Marvin processes structured data for software development, enhancing your software development process.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Summarization:** 7.5/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 6.7/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Askmarvinai](https://www.g2.com/sellers/askmarvinai)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Simple (3 reviews)
- Useful (3 reviews)
- AI Technology (2 reviews)
- Easy Integrations (2 reviews)

**Cons:**

- AI Limitations (2 reviews)
- Limitations (2 reviews)
- Usage Limitations (2 reviews)
- Complex Implementation (1 reviews)
- Complex Setup (1 reviews)

### 18. [GlobalLink](https://www.g2.com/products/globallink/reviews)
  GlobalLink is a translation management platform offered by TransPerfect, the world’s largest provider of localization services. Designed to be able to scale based on the needs of a variety of use cases and industry verticals, GlobalLink Enterprise is currently deployed by over 6,000 organizations around the globe to streamline content creation, localization and delivery. GlobalLink offers 65+ connectors to integrate into a variety of content repositories, including CMS, CCMS, eCommerce, CRM, digital marketing and databases, and also offers a robust set of API’s for bespoke integration requirements. Key features of GlobalLink include: Enterprise Translation Automation Global Supply Chain Management Neural MT/AI integration Generative AI workflows Integrated Translation Memory & Terminology Management Cloud and Desktop Based CAT Environments Cloud-based Content Validation with In-Context Preview Capabilities Advanced, Secure File Sharing Support for Media-Based Assets Support for Mobile Application Localization Support for Continuous Localization Processes Synchronous/asynchronous third-party platform integration Accessibility Certification Web-proxy and JS Injection Capabilities Advanced Business Analytics Customized Dashboard Views Widest Portfolio of Connectors in the Industry Broad REST APIs


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 186

**User Satisfaction Scores:**

- **Quality of Support:** 9.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [TransPerfect](https://www.g2.com/sellers/transperfect)
- **Company Website:** https://transcend.transperfect.com/
- **Year Founded:** 1992
- **HQ Location:** New York
- **Twitter:** @DigitalReef (570 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/transperfect/ (17,439 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Pharmaceuticals
  - **Company Size:** 58% Enterprise, 28% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (46 reviews)
- Features (29 reviews)
- Translation Services (23 reviews)
- Customer Support (22 reviews)
- Helpful (22 reviews)

**Cons:**

- Poor Customer Support (7 reviews)
- Slow Performance (7 reviews)
- Difficult Navigation (6 reviews)
- Learning Curve (6 reviews)
- Missing Features (6 reviews)

### 19. [Abacus.ai](https://www.g2.com/products/abacus-ai/reviews)
  Abacus.ai is an AI Super Assistant and Generative AI platform designed to help individuals and businesses build, automate, and scale intelligent applications with ease. The platform enables users to create AI-powered workflows, deploy autonomous AI agents, and build full-stack applications using natural language. From app development and data analysis to content generation and automation, Abacus.ai brings multiple AI capabilities into a single unified environment. With tools like Deep Agent, users can turn simple prompts into fully functional applications complete with backend, database, and user interfaces. This significantly reduces development time and eliminates the need for complex technical setup. Abacus.ai focuses on productivity, automation, and real-world use cases, making it accessible for developers, teams, and non-technical users alike. By combining Generative AI, AI agents, and workflow automation, it empowers users to move from idea to execution faster than ever before.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Summarization:** 10.0/10 (Category avg: 9.0/10)
- **Part of Speech Tagging:** 10.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Abacus.ai](https://www.g2.com/sellers/abacus-ai-d51997db-0593-4bfa-8d46-7702af464544)
- **HQ Location:** United States
- **Twitter:** @abacusai (95,587 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/abacus/ (1,015 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 54% Mid-Market, 38% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (2 reviews)
- Artificial Intelligence (1 reviews)
- Automation (1 reviews)
- Features (1 reviews)
- Time-saving (1 reviews)

**Cons:**

- Expensive (2 reviews)
- Limitations (1 reviews)
- Limited Customization (1 reviews)

### 20. [NewSci AI-Readines Services](https://www.g2.com/products/newsci-ai-readines-services/reviews)
  All the talk about qualitative data analysis is for naught if you can’t understand language as it is spoken. That is what Natural Language Processing (NLP) is all about. NewSci NLP brings this power to organization’s seeking to extract insights from their unstructured data. Just as you know what a person is saying when you hear, “I’m hungry, I want an apple” vs. “I really want an Apple™ instead of a PC,” so now can a computer. NewSci NLP enables a computer to understand the people, places, and things important to your organization. This, in turn, allows your unstructured data to be analyzed just like your structured data. With NewSci NLP your organization will enjoy qualitative analysis (the Why behind the numbers) alongside your quantitative analytics. Uses models customized to your organization; the domain in which you operate; the quality of your recordings; and even local and regional dialects to deliver the highest level of transcription accuracy. Captures your organization’s domain and unique characteristics to enable deep Natural Language Understanding analysis and Natural Language Generation. Your NewSci Ontology will be your Rosetta Stone for unlocking the value hidden in your unstructured data. The NewSci Insight Reservoir™ brings governance and insight to the data lake. You enjoy all the benefits of a state-of-the-art Big Data lake including access to hundreds of data connectors for ingesting information; transformation tools for quality assurance and data enhancement; and cataloging of your data down to the field level while at the same time having unmatched data governance capabilities: Unlike a passive data lake, the NewSci Insight Reservoir™ is a powerful cognitive computing platform where you can perform machine learning; deep learning; and natural language processing on all your structured and unstructured data. NewSci NLP connects directly to your NewSci Insight Reservoir™ to extract meaning from your text and make it available for analysis. Machine and Deep Learning algorithms can be created, and perfected, as data enters the Insight Reservoir™, increasing the value in real-time. And all of the insights can easily be made available for visualization tools including Tableau®, Qlik®, and MS Power- BI®. Jump out of the data lake and get your organization into the NewSci Insight Reservoir™


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 2

**User Satisfaction Scores:**

- **Summarization:** 10.0/10 (Category avg: 9.0/10)
- **Language Detection:** 10.0/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 10.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 7.5/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [NewSci](https://www.g2.com/sellers/newsci)
- **Year Founded:** 2013
- **HQ Location:** Tampa, US
- **Twitter:** @New_Sci (68 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/newsci-llc (2 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 50% Mid-Market


### 21. [NLP Studio](https://www.g2.com/products/nlp-studio/reviews)
  SparkCognition has developed a solution that automates workflows of unstructured data within organizations so humans can focus on high value business decisions. DeepNLP uses advanced machine learning techniques to automate the retrieval of information, classification of documents, and content analytics.


  **Average Rating:** 3.8/5.0
  **Total Reviews:** 2

**User Satisfaction Scores:**

- **Summarization:** 8.3/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.3/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Avathon](https://www.g2.com/sellers/avathon)
- **Year Founded:** 2013
- **HQ Location:** Austin, Texas
- **LinkedIn® Page:** https://www.linkedin.com/company/5155679 (253 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Enterprise, 50% Small-Business


### 22. [SQL Ease](https://www.g2.com/products/sql-ease/reviews)
  SQL Ease transforms natural language input into SQL queries effortlessly, making database management more accessible.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 4

**User Satisfaction Scores:**

- **Summarization:** 9.2/10 (Category avg: 9.0/10)
- **Language Detection:** 9.2/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.3/10 (Category avg: 8.7/10)
- **Quality of Support:** 5.8/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [BuildNShip](https://www.g2.com/sellers/buildnship)
- **HQ Location:** Ernamkulam, IN
- **LinkedIn® Page:** https://www.linkedin.com/company/buildnship/ (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 75% Small-Business, 25% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (3 reviews)
- Time Management (2 reviews)
- Time-saving (2 reviews)
- User Interface (2 reviews)
- Customer Support (1 reviews)

**Cons:**

- Slow Performance (2 reviews)
- Accuracy Issues (1 reviews)
- Complex Setup (1 reviews)
- Improvement Needed (1 reviews)
- Insufficient Training (1 reviews)

### 23. [Tinq.ai](https://www.g2.com/products/tinq-ai/reviews)
  Tinq.ai is a simple, yet powerful natural language processing tool. It helps you easily implement text analysis within your projects.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 2

**User Satisfaction Scores:**

- **Summarization:** 9.2/10 (Category avg: 9.0/10)
- **Language Detection:** 8.3/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 8.3/10 (Category avg: 8.7/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Tinq.ai](https://www.g2.com/sellers/tinq-ai)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/tinq-ai/ (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Enterprise, 50% Small-Business


### 24. [Convai](https://www.g2.com/products/convai/reviews)
  Enable your characters with human-like conversation capabilities in games and virtual world applications.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1

**User Satisfaction Scores:**

- **Summarization:** 10.0/10 (Category avg: 9.0/10)
- **Language Detection:** 10.0/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 10.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 10.0/10 (Category avg: 8.6/10)


**Seller Details:**

- **Seller:** [Convai](https://www.g2.com/sellers/convai)
- **HQ Location:** Melbourne , NZ
- **LinkedIn® Page:** https://www.linkedin.com/company/convai-au/ (17 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Mid-Market


#### Pros & Cons

**Pros:**

- Chatbot Communication (1 reviews)
- Customization (1 reviews)
- Setup Ease (1 reviews)


### 25. [Expert.ai](https://www.g2.com/products/expert-ai/reviews)
  Expert.ai Studio is a fully integrated, low-code development environment for building and deploying custom AI-based text models to address any linguistic challenge. Our solution helps organizations and developers to create advanced and unique solutions to extend the scope of intelligent process automation and make knowledge discovery more effective. Expert.ai Studio applies natural language understanding (NLU) capabilities and a fine-grained text processing configuration to achieve precise comprehension of your content. As a result, you gain complete control over your data so you can use it more efficiently and at scale in support of your business operations.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1


**Seller Details:**

- **Seller:** [Expert.ai](https://www.g2.com/sellers/expert-ai)
- **HQ Location:** Modena, IT
- **LinkedIn® Page:** https://www.linkedin.com/company/expert-ai/ (266 employees on LinkedIn®)
- **Ownership:** BIT:EXSY
- **Total Revenue (USD mm):** $31

**Reviewer Demographics:**
  - **Company Size:** 100% Enterprise




## Parent Category

[Natural Language Processing (NLP) Software](https://www.g2.com/categories/natural-language-processing-nlp)




---

## Buyer Guide

### What You Should Know About Natural Language Understanding Software

### What is Natural Language Understanding Software?

Natural language understanding, a subset of natural language processing (NLP), makes predictions or decisions based on text data. These learning algorithms can be embedded within applications to provide automated artificial intelligence (AI) features. A connection to a data source is necessary for the algorithm to learn and adapt over time. 

Pulling out actionable insights from numerical data housed in ERP systems, CRM software, or accounting software is one thing, but gaining insights from unstructured data sources is invaluable. Without dedicated software for this task, businesses must spend significant time and resources building natural language understanding models or haphazardly investigating the data.

These algorithms may be developed with supervised learning or unsupervised learning. Supervised learning involves training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment.

End users of intelligent applications may not be aware that an everyday software tool utilizes a machine learning algorithm to provide automation of some kind. Additionally, machine learning solutions for businesses may come in a machine learning as a service (MLaaS) model.

**What Does NLU Stand For?**

NLU stands for Natural Language Understanding, which is a subset of natural language processing (NLP).

#### What Types of Natural Language Understanding Software Exist?

Natural language understanding, at its core, allows machines to understand human language in spoken or written form. There are two key methods this can be accomplished.

**Machine learning-based systems**

Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they are fed and adjust their methods as more data is processed. Using a combination of machine learning, deep learning, and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning.

**Rules-based systems**

This system uses carefully designed linguistic rules. This approach was used early in the development of natural language processing and is still used.

### What are the Common Features of Natural Language Understanding Software?

The following are some core features within natural language understanding software that can help users better understand text data:

**Part-of-speech (POS) tagging:** With POS tagging, users can parse text by parts of speech. This can help break down sentences into component parts to understand them.

**Named entity recognition (NER):** Sentences are comprised of various entities, from street names to surnames, places, and more. With NER, one can extract these entities. These extracted entities can then be fed into other systems automatically.

**Sentiment analysis:** Language can be positive, negative, or neutral. Using sentiment analysis techniques, one can input text and be given the sentiment (positive or negative) of that text.

**Emotion detection:** Similar to sentiment analysis, emotion detection can detect the emotion of human language, whether written or spoken. Despite the research supporting it, this method has come under scrutiny, and its veracity has been challenged.

### What are the Benefits of Natural Language Understanding Software?

Natural language understanding is useful in many different contexts and industries.

**Application development:** NLU drives the development of AI applications that streamline processes, identify risks, and improve effectiveness.

**Efficiency:** NLU-powered applications are constantly improving because of the recognition of their value and the need to stay competitive in the industries in which they are used. They also increase the efficiency of repeatable tasks. A prime example of this can be seen in eDiscovery, where machine learning has created massive leaps in the efficiency with which legal documents are looked through, and relevant ones are identified.

**Scalability:** Humans are great at analysis, but their analysis skills can break down when the amount of data is vast and when they need to produce results in record time. NLU-powered technology does not get stressed, pressured, or tired. It can analyze a (relatively) small amount of data or a large text corpus with ease, speed, and accuracy. This can be scaled across a business’ text datasets and various use cases.

**Discovering trends:** NLU can do a great job at finding trends and patterns in text data. Through word clouds, graphs and charts, and more, NLU can provide users with deep insight into what is happening beneath the surface.

**Empowering non-technical users:** Much NLU technology in the market is no-code or low-code, which allows non-technical users to benefit from the technology. Gone are the days when one needed to go to a data scientist or IT professional to understand language data.

### Who Uses Natural Language Understanding Software?

NLU has applications across nearly every industry. Some industries that benefit from NLU applications include financial services, cybersecurity, recruiting, customer service, energy, and regulation.

**Marketing:** NLU-powered marketing applications help marketers identify content trends, shape content strategy, and personalize marketing content. 

**Finance:** Financial services institutions are increasing their use of NLU-powered applications to stay competitive with others in the industry who are doing the same. Some examples may include trawling through thousands of insurance claims and identifying ones with a high potential to be fraudulent. The process is similar, and the machine learning algorithm can digest the data to achieve the desired outcome quicker.

**Human resources:** Resumes are long and filled with words. As such, natural language understanding technology can help recruiters comb through large amounts of resumes and other text data to better understand candidates.

### What are the Alternatives to Natural Language Understanding Software?

Alternatives to natural language understanding software can replace this type of software, either partially or completely:

[Machine learning software](https://www.g2.com/categories/machine-learning#learn-more) **:** Natural language understanding (NLU) software is specifically connected to and used for text data. If one is looking for more general-use machine learning algorithms, machine learning software would be a good category to pursue.

[Text analysis software](https://www.g2.com/categories/text-analysis#learn-more) **:** NLU software is geared toward incorporating NLU capabilities into other applications or systems. Text analysis software, however, is an all-purpose solution built to analyze any text data. Businesses looking to focus on analyzing their text data, such as from surveys, review sites, social media, and customer service tools, can leverage text analysis software to achieve this goal. This software enables businesses to consolidate and analyze their text data within a single platform. 

#### Software Related to Natural Language Understanding Software

Related solutions that can be used together with natural language understanding software include:

[Chatbots software](https://www.g2.com/categories/chatbots) **:** Businesses looking for an off-the-shelf conservational AI solution can leverage chatbots. Tools specifically geared toward chatbot creation helps companies use chatbots off the shelf, with little to no development or coding experience necessary.

[Bot platforms software](https://www.g2.com/categories/bot-platforms) **:** Companies looking to build their own chatbot can benefit from bot platforms, which are tools used to build and deploy interactive chatbots. These platforms provide development tools such as frameworks and API toolsets for customizable bot creation.

[Intelligent virtual assistants (IVAs)](https://www.g2.com/categories/intelligent-virtual-assistants) **:** Businesses that want conversational AI with strong natural language understanding capabilities should consider IVAs. IVAs understand a range of different intents from a singular utterance and can even understand responses they are not explicitly programmed to using natural language processing (NLP). With the use of machine learning and deep learning, IVAs can grow intelligently and understand a wider vocabulary and colloquial language, as well as provide more precise and correct responses to requests.

### Challenges with Natural Language Understanding Software

Software solutions can come with their own set of challenges. 

**Data preparation:** A potential concern is preparing the data to be ingested by the NLU tool. The data needs to be stored properly, whether that is in a database or data warehouse. Users may require IT or a dedicated admin to ensure the text analytics tool can consume the data.

**Automation pushback:** One of the biggest potential issues with machine learning-powered applications, such as NLU, lies in removing humans from processes. This is particularly problematic when looking at emerging technologies like self-driving cars. By completely removing humans from the product development lifecycle, machines are given the power to decide in life-or-death situations.

**Data security:** Companies must consider security options to ensure the correct users see the correct data. They must also have security options that allow administrators to assign verified users different levels of access to the platform.

### Which Companies Should Buy Natural Language Understanding Software?

Pattern recognition can help businesses across industries. Effective and efficient predictions can help these businesses make data-informed decisions, such as dynamic pricing based upon a range of data points.

**Retail:** An e-commerce site can leverage an NLU application programming interface (API) to create rich, personalized experiences for every user.

**Entertainment:** Media organizations can leverage NLU to comb through their scripts and other content to catalog and categorize their material.

**Finance:** Financial institutions can analyze contracts and conduct sentiment analysis and named entity recognition to better understand these documents and to scale operations.

### How to Buy Natural Language Understanding Software

#### Requirements Gathering (RFI/RFP) for Natural Language Understanding Software

If a company is just starting out and looking to purchase their first NLU software, wherever they are in the buying process, g2.com can help select the best machine learning software for them.

Taking a holistic overview of the business and identifying pain points can help the team create a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a machine learning platform.

#### Compare Natural Language Understanding Software Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after the demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is advisable to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

#### Selection of Natural Language Understanding Software

**Choose a selection team**

Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.

**Negotiation**

Prices on a company's pricing page are not always fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Does Natural Language Understanding Software Cost?

NLU software is generally available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will usually lack features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, either unlimited or capped at a certain number of hours per billing cycle.

Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

#### Return on Investment (ROI)

Businesses decide to deploy machine learning software with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. 

More users will naturally translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

### Natural Language Understanding Software Trends

**Automation**

With the adoption of NLU and the automation of repetitive tasks, businesses can deploy their human workforce to more creative projects. For example, if a machine learning algorithm automatically displays personalized advertisements based on a user’s text, the human marketing team can work on producing creative material.

**Voice technology**

Voice is a primal method of interacting with others. It is only natural that we now converse with our machines using our voice and that the platforms for said voicebots have seen great success. Voice makes technology feel more human and allows people to trust it more. Voice will prove to be a crucial natural interface that mediates human communication and relationships with devices within an AI-powered world.

**Artificial intelligence (AI)**

AI is quickly becoming a promising feature of many, if not most, types of software. With machine learning, end users can identify patterns in data, allowing them to make sense of content and help them understand what they are seeing. This pattern recognition is fueling the rise of more powerful, contextually-aware chatbots.