# Best Natural Language Understanding (NLU) Software for Small Business

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


Products classified in the overall Natural Language Understanding (NLU) category are similar in many regards and help companies of all sizes solve their business problems. However, small business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Small Business Natural Language Understanding (NLU) to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Small Business Natural Language Understanding (NLU) category.

In addition to qualifying for inclusion in the Natural Language Understanding (NLU) Software category, to qualify for inclusion in the Small Business Natural Language Understanding (NLU) Software category, a product must have at least 10 reviews left by a reviewer from a small business.






## G2 Grid® for Natural Language Understanding (NLU) Software
![G2 Grid® for Natural Language Understanding (NLU) Software plotting products by satisfaction and market presence](https://www.g2.com/categories/natural-language-understanding-nlu/grids.png?focus%5B%5D=1579500&focus%5B%5D=21473&focus%5B%5D=77169&focus%5B%5D=52116&focus%5B%5D=21472&focus%5B%5D=1375562&focus%5B%5D=334&focus%5B%5D=153261)
Highlighted products: Claude, Google Cloud Translation API, Deepgram, Amazon Comprehend, Google Cloud Natural Language API, Azure AI Language, InMoment Experience Improvement (XI) Platform, and scite.ai.
Underlying data: [Grid® JSON](https://www.g2.com/categories/natural-language-understanding-nlu/grids.json?focus%5B%5D=claude-2025-12-11&focus%5B%5D=google-cloud-translation-api&focus%5B%5D=deepgram&focus%5B%5D=amazon-comprehend&focus%5B%5D=google-cloud-natural-language-api&focus%5B%5D=azure-ai-language&focus%5B%5D=inmoment-experience-improvement-xi-platform&focus%5B%5D=scite-ai&segment=small-business)


## How Many Natural Language Understanding (NLU) Software Products Does G2 Track?
**Total Products under this Category:** 79

### Category Stats (Jul 2026)
- **Average Rating**: 4.43/5 (↑0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: Plasticity (+6.25%) - Among all products in this category, Plasticity recorded the largest rating increase compared to last month
*Last updated: July 16, 2026*


## How Does G2 Rank Natural Language Understanding (NLU) Software Products?

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

- 30 Analysts and Data Experts
- 2,400+ Authentic Reviews
- 79+ 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.



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---

## What Are the Top-Rated Natural Language Understanding (NLU) Software Products in 2026?
### 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:** 390
**How Do G2 Users Rate Claude?**

- **Summarization:** 9.8/10 (Category avg: 9.0/10)
- **Language Detection:** 9.4/10 (Category avg: 8.8/10)
- **Part of Speech Tagging:** 9.0/10 (Category avg: 8.7/10)
- **Quality of Support:** 8.0/10 (Category avg: 8.7/10)

**Who Is the Company Behind Claude?**

- **Seller:** [Anthropic](https://www.g2.com/sellers/anthropic-b3e27488-b6f4-49c9-a8c7-d860a4207ff3)
- **HQ Location:** San Francisco, California
- **Twitter:** @AnthropicAI (1,440,248 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/anthropicresearch/ (5,178 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Software Engineer, Data Analyst
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 52% Small-Business, 34% Mid-Market


#### What Are Claude's Pros and 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)


### What Do G2 Reviewers Say About Claude?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find Claude extremely **easy to use** , simplifying coding and enhancing productivity through supportive features and integrations.
- Users find Claude to be **significantly simplifying the coding process** , enhancing problem-solving and natural text generation.
- Users value the **helpful support and AI capabilities** of Claude, enhancing their web programming experience significantly.
- Users commend the **accuracy** of Claude, consistently delivering clear and concise responses to their prompts.
- Users appreciate the **conversational communication** of Claude, enhancing problem-solving and facilitating seamless workflows.

**Cons:**

- Users are frustrated by **usage limitations** , facing challenges with input size and content formatting while using Claude.
- Users find Claude's **overcautiousness and limitations** frustrating, especially with restricted question counts and inconsistent task performance.
- Users find Claude's **limited functionality** frustrating, especially with vague answers and coding challenges it can't resolve effectively.
- Users find Claude's **overly cautious and long-winded responses** frustrating, preferring direct answers instead.
- Users face **resource limitations** on Claude, with unclear limits impacting the experience even with paid plans.

#### What Are Recent G2 Reviews of Claude?

**"[Exceptional Long-Context Reasoning That Boosts Productivity](https://www.g2.com/survey_responses/claude-review-13069341)"**

**Rating:** 5.0/5.0 stars
*— Supriya S.*

[Read full review](https://www.g2.com/survey_responses/claude-review-13069341)

---

**"[Step-by-Step Coding Help That Catches Bugs and Speeds Up Content Drafting](https://www.g2.com/survey_responses/claude-review-13068213)"**

**Rating:** 5.0/5.0 stars
*— Aꜱᴍɪᴛ B.*

[Read full review](https://www.g2.com/survey_responses/claude-review-13068213)

---



### 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:** 340
**How Do G2 Users Rate Google Cloud Translation API?**

- **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: 8.7/10)

**Who Is the Company Behind Google Cloud Translation API?**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

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


#### What Are Google Cloud Translation API's Pros and Cons?

**Pros:**

- Translation Services (59 reviews)
- Ease of Use (46 reviews)
- Accuracy (38 reviews)
- Multilingual Support (38 reviews)
- Language Support (33 reviews)

**Cons:**

- Translation Accuracy (30 reviews)
- Expensive (28 reviews)
- Accuracy Issues (18 reviews)
- Subscription Costs (16 reviews)
- Translation Issues (14 reviews)


### What Do G2 Reviewers Say About Google Cloud Translation API?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **impressive translation quality** of Google Cloud Translation API, admiring its speed and integration ease.
- Users find the **ease of use** of Google Cloud Translation API invaluable for both personal and professional projects.
- Users appreciate the **remarkable accuracy** of Google Cloud Translation API, enhancing their translation experience significantly.
- Users appreciate the **multilingual support** of Google Cloud Translation API, which enhances accessibility and versatility across languages.
- Users value the **automatic language detection** of Google Cloud Translation API for its high accuracy and broad reach.

**Cons:**

- Users find the **translation accuracy lacking** , especially with dialects and slang, leading to frustrating experiences.
- Users find the **service expensive** , especially with high volumes, impacting overall accessibility and affordability.
- Users report **accuracy issues** with translations, especially in languages like Russian, impacting overall satisfaction.
- Users find the **subscription costs** steep, especially for high volume requests and limited free tier customization.
- Users face **translation issues** with limited context for less common languages and problematic interface performance.

#### What Are Recent G2 Reviews of Google Cloud Translation API?

**"[Helps Me Communicate Better with Multilingual Students](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13072928)"**

**Rating:** 5.0/5.0 stars
*— Marquetta F.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13072928)

---

**"[Effortless Integration with Google Services, Accurate and Fast](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13108926)"**

**Rating:** 4.5/5.0 stars
*— Hans S.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13108926)

---


#### What Are G2 Users Discussing About Google Cloud Translation API?

- [What advice do you have for developers considering Google Cloud Translation API for multilingual applications?](https://www.g2.com/discussions/what-advice-do-you-have-for-developers-considering-google-cloud-translation-api-for-multilingual-applications)
- [What is Google Cloud Translation API used for?](https://www.g2.com/discussions/what-is-google-cloud-translation-api-used-for) - 1 upvote

### 3. [Deepgram](https://www.g2.com/products/deepgram/reviews)
Enterprise Voice AI platform designed for developers building voice-first products using speech-to-text, text-to-speech, or speech-to-speech APIs. Over 200,000 developers build with Deepgram's voice-native foundational models, accessed via APIs or self-managed software. Start building with $200 in free credits! Beyond that, developers can: 🔊 Process live-streaming or pre-recorded audio with superior accuracy 🗣️ Convert text into natural-sounding AI voices for enterprise use cases with text-to-speech ⚡️ Easily build voice agents with our unified Voice Agent API 🌎 Accurately transcribe audio in over 36+ languages ⚙️ Train custom models for unique use cases 🔑 Access deep NLU with a unified API 💻 Build in any programming language with our SDKs ✅ Deploy on-prem or on DG’s managed cloud 📈 Get scalable GPU infra for training and inference


**Average Rating:** 4.6/5.0
**Total Reviews:** 443
**How Do G2 Users Rate Deepgram?**

- **Quality of Support:** 8.8/10 (Category avg: 8.7/10)

**Who Is the Company Behind Deepgram?**

- **Seller:** [Deepgram](https://www.g2.com/sellers/deepgram)
- **Company Website:** https://deepgram.com
- **Year Founded:** 2015
- **HQ Location:** San Francisco, California
- **Twitter:** @DeepgramAI (10,837 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/deepgram/ (325 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Software Engineer, CEO
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 80% Small-Business, 19% Mid-Market


#### What Are Deepgram's Pros and Cons?

**Pros:**

- Accuracy (38 reviews)
- Speed (36 reviews)
- Ease of Use (35 reviews)
- Quality (34 reviews)
- Real-time Transcription (28 reviews)

**Cons:**

- Limited Language Support (17 reviews)
- Pricing Issues (14 reviews)
- Expensive (13 reviews)
- Inaccuracy Issues (9 reviews)
- Limited Languages (8 reviews)


### What Do G2 Reviewers Say About Deepgram?
*AI-generated summary from verified user reviews*

**Pros:**

- Users praise the **high accuracy** of Deepgram's transcriptions, making it a reliable choice for speech-to-text tasks.
- Users value the **fast and accurate transcriptions** of Deepgram, making their transcription tasks effortless and efficient.
- Users appreciate the **ease of use** of Deepgram, enjoying its straightforward API and fast, accurate transcription capabilities.
- Users highlight the **excellent transcription accuracy** of Deepgram, making audio processing quick and efficient.
- Users enjoy the **real-time transcription** capabilities of Deepgram, benefiting from fast, accurate, and customizable speech recognition.

**Cons:**

- Users express frustration with the **limited language support** in Deepgram, wishing for greater versatility in transcription.
- Users feel that **pricing issues** might pose challenges for larger projects and college students, impacting accessibility.
- Users find the **service expensive** , especially for large projects and tight budgets, which can hinder accessibility.
- Users face **inaccuracy issues** with Deepgram, struggling with missed words and limited support for diverse accents.
- Users find the **limited language support** of Deepgram restrictive, though improvements are being made to enhance options.

#### What Are Recent G2 Reviews of Deepgram?

**"[Very Good for Transcripts, Summaries, and Content Preparation](https://www.g2.com/survey_responses/deepgram-review-12926548)"**

**Rating:** 5.0/5.0 stars
*— Ishan S.*

[Read full review](https://www.g2.com/survey_responses/deepgram-review-12926548)

---

**"[From Raw Audio to Actionable Insights in Seconds](https://www.g2.com/survey_responses/deepgram-review-12858309)"**

**Rating:** 4.5/5.0 stars
*— Hitesh J.*

[Read full review](https://www.g2.com/survey_responses/deepgram-review-12858309)

---


#### What Are G2 Users Discussing About Deepgram?

- [What is Deepgram used for?](https://www.g2.com/discussions/what-is-deepgram-used-for) - 1 comment

### 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:** 4.3/5.0
**Total Reviews:** 82
**How Do G2 Users Rate Amazon Comprehend?**

- **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: 8.7/10)

**Who Is the Company Behind Amazon Comprehend?**

- **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 (2,232,483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Accounting
- **Company Size:** 41% Mid-Market, 40% Small-Business


#### What Are Amazon Comprehend's Pros and 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)


### What Do G2 Reviewers Say About Amazon Comprehend?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **valuable insights** Amazon Comprehend provides, enhancing their ability to analyze diverse text sources effectively.
- Users value the **insightful text analysis** capabilities of Amazon Comprehend, making data extraction easy and efficient.
- Users appreciate the **ease of use** of Amazon Comprehend, enabling insight extraction without needing machine learning expertise.
- Users value the **valuable insights** from Amazon Comprehend, enabling easy text analysis without needing machine learning expertise.
- Users find **valuable insights extraction** from various texts easy and effective, with robust data protection features.

**Cons:**

- Users face **accuracy issues** with Amazon Comprehend, needing more training data for reliable insights and results.
- Users find the **cost can be prohibitive** when dealing with large volumes of data for Amazon Comprehend.
- Users find that **insufficient training** leads to lower accuracy in insights, especially with larger datasets.

#### What Are Recent G2 Reviews of Amazon Comprehend?

**"[Efficient text analysis with minimal setup](https://www.g2.com/survey_responses/amazon-comprehend-review-12938148)"**

**Rating:** 4.5/5.0 stars
*— jahan a.*

[Read full review](https://www.g2.com/survey_responses/amazon-comprehend-review-12938148)

---

**"[Accurate Pre-Trained NLP Models with Seamless PII Redaction](https://www.g2.com/survey_responses/amazon-comprehend-review-12955644)"**

**Rating:** 4.5/5.0 stars
*— Ruchi P.*

[Read full review](https://www.g2.com/survey_responses/amazon-comprehend-review-12955644)

---



### 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:** 79
**How Do G2 Users Rate Azure AI Language?**

- **Summarization:** 8.3/10 (Category avg: 9.0/10)
- **Language Detection:** 8.6/10 (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: 8.7/10)

**Who Is the Company Behind Azure AI Language?**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small-Business, 32% Enterprise



#### What Are Recent G2 Reviews of Azure AI Language?

**"[Azure AI Language Helps Us Understand Customer Sentiment and Respond More Warmly](https://www.g2.com/survey_responses/azure-ai-language-review-12862917)"**

**Rating:** 4.5/5.0 stars
*— Somashekar N.*

[Read full review](https://www.g2.com/survey_responses/azure-ai-language-review-12862917)

---

**"[Powerful, Easy-to-Implement NLP Insights with Azure AI](https://www.g2.com/survey_responses/azure-ai-language-review-13017464)"**

**Rating:** 4.5/5.0 stars
*— Rafee N.*

[Read full review](https://www.g2.com/survey_responses/azure-ai-language-review-13017464)

---


#### What Are G2 Users Discussing About Azure AI Language?

- [What is Azure QnA Maker API used for?](https://www.g2.com/discussions/what-is-azure-qna-maker-api-used-for)
- [What is API in Microsoft Azure?](https://www.g2.com/discussions/what-is-api-in-microsoft-azure) - 1 comment

### 6. [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:** 98
**How Do G2 Users Rate Google Cloud Natural Language API?**

- **Summarization:** 8.6/10 (Category avg: 9.0/10)
- **Language Detection:** 8.9/10 (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: 8.7/10)

**Who Is the Company Behind Google Cloud Natural Language API?**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Who Uses This Product?**
- **Who Uses This:** Software Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 55% Small-Business, 23% Enterprise


#### What Are Google Cloud Natural Language API's Pros and Cons?

**Pros:**

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

**Cons:**

- Not User-Friendly (1 reviews)


### What Do G2 Reviewers Say About Google Cloud Natural Language API?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **powerful database integration** of Google Cloud Natural Language API for enhancing decision-making in healthcare.
- Users value the **ability to upload large databases** with Google Cloud Natural Language API, enhancing decision-making in healthcare.
- Users value the **ability to upload large databases** with Google Cloud Natural Language API for informed decision-making.

**Cons:**

- Users find the API **not user-friendly** , making it difficult to understand and effectively utilize its features.

#### What Are Recent G2 Reviews of Google Cloud Natural Language API?

**"[Evolving AI Software That’s Becoming Incredibly Helpful](https://www.g2.com/survey_responses/google-cloud-natural-language-api-review-12836718)"**

**Rating:** 5.0/5.0 stars
*— Lisa S.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-natural-language-api-review-12836718)

---

**"[Various machine learning to n number of peopleople](https://www.g2.com/survey_responses/google-cloud-natural-language-api-review-10993983)"**

**Rating:** 5.0/5.0 stars
*— CA. Dishi T.*

[Read full review](https://www.g2.com/survey_responses/google-cloud-natural-language-api-review-10993983)

---



### 7. [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
**How Do G2 Users Rate InMoment Experience Improvement (XI) Platform?**

- **Quality of Support:** 9.0/10 (Category avg: 8.7/10)

**Who Is the Company Behind InMoment Experience Improvement (XI) Platform?**

- **Seller:** [PG Forsta](https://www.g2.com/sellers/pg-forsta)
- **HQ Location:** Salt Lake City, Utah
- **LinkedIn® Page:** https://www.linkedin.com/company/weareinmoment/ (411 employees on LinkedIn®)

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



#### What Are Recent G2 Reviews of InMoment Experience Improvement (XI) Platform?

**"[Easy to use tools. Supportive Visionary Staff.](https://www.g2.com/survey_responses/inmoment-experience-improvement-xi-platform-review-9832822)"**

**Rating:** 5.0/5.0 stars
*— Beth W.*

[Read full review](https://www.g2.com/survey_responses/inmoment-experience-improvement-xi-platform-review-9832822)

---

**"[Seamless integration with POS for survey](https://www.g2.com/survey_responses/inmoment-experience-improvement-xi-platform-review-9337685)"**

**Rating:** 5.0/5.0 stars
*— Lakshay  D.*

[Read full review](https://www.g2.com/survey_responses/inmoment-experience-improvement-xi-platform-review-9337685)

---


#### What Are G2 Users Discussing About InMoment Experience Improvement (XI) Platform?

- [What is InMoment Experience Intelligence (XI) Platform used for?](https://www.g2.com/discussions/what-is-inmoment-experience-intelligence-xi-platform-used-for)

### 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:** 27
**How Do G2 Users Rate scite.ai?**

- **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: 8.7/10)

**Who Is the Company Behind scite.ai?**

- **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/ (5 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Research, Higher Education
- **Company Size:** 52% Small-Business, 11% Enterprise


#### What Are scite.ai's Pros and 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)


### What Do G2 Reviewers Say About scite.ai?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find scite.ai's **powerful literature review tools** invaluable, simplifying citation tracking and enhancing research efficiency.
- Users praise the **ease of use** of scite.ai, enhancing their research efficiency and simplifying literature reviews.
- Users appreciate the **accuracy of scite.ai's search results** , which significantly aids in locating relevant scientific literature efficiently.
- Users commend scite.ai for its **efficiency in literature reviews** , significantly saving time and enhancing research precision.
- Users appreciate the **accurate and relevant search results** of scite.ai, greatly enhancing their literature review experience.

**Cons:**

- Users experience **slow performance** on scite.ai, which can interrupt workflow and affect user efficiency.
- Users experience **slow interface response** and incomplete answers, highlighting limits in scite.ai's performance and capabilities.
- Users express frustration with the **lack of precise context understanding** , leading to irrelevant responses and redundancy in literature reviews.
- Users experience **poor response quality** with scite.ai, receiving repetitive and generic answers that disrupt their workflow.
- Users experience **repetitive content** issues with scite.ai, leading to frustration and delays in finding relevant information.

#### What Are Recent G2 Reviews of scite.ai?

**"[Your Personal Research Assistant—A Must-Have for Students and Researchers](https://www.g2.com/survey_responses/scite-ai-review-11931827)"**

**Rating:** 5.0/5.0 stars
*— Melike G.*

[Read full review](https://www.g2.com/survey_responses/scite-ai-review-11931827)

---

**"[Essential for Academic Citations with Minor Token Limitation](https://www.g2.com/survey_responses/scite-ai-review-12729378)"**

**Rating:** 5.0/5.0 stars
*— Myrto P.*

[Read full review](https://www.g2.com/survey_responses/scite-ai-review-12729378)

---



### 9. [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
**How Do G2 Users Rate NLTK?**

- **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: 8.7/10)

**Who Is the Company Behind NLTK?**

- **Seller:** [NLTK Project](https://www.g2.com/sellers/nltk-project)
- **HQ Location:** N/A
- **Twitter:** @NLTK_org (2,305 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

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



#### What Are Recent G2 Reviews of NLTK?

**"[Beast in language processing](https://www.g2.com/survey_responses/nltk-review-6885408)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Telecommunications*

[Read full review](https://www.g2.com/survey_responses/nltk-review-6885408)

---

**"[NLTK - A useful toolkit to start with NLP](https://www.g2.com/survey_responses/nltk-review-6637986)"**

**Rating:** 4.0/5.0 stars
*— Deepanshu D.*

[Read full review](https://www.g2.com/survey_responses/nltk-review-6637986)

---


#### What Are G2 Users Discussing About NLTK?

- [What is NLTK used for?](https://www.g2.com/discussions/what-is-nltk-used-for)


## What Is Natural Language Understanding (NLU) Software?

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



---

## How Do You Choose the Right Natural Language Understanding (NLU) Software?

### 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.