6 best named entity recognition APIs for entity detection

Named Entity Recognition (NER) APIs have become essential tools for developers building applications that need to extract meaningful information from text, and are considered by some to be an extremely valuable tool for data collection and analysis. These APIs automatically identify and categorize key information—like names, organizations, locations, and other entities—transforming unstructured text into structured, actionable data.

Whether you're analyzing customer feedback, building conversation intelligence platforms, or processing transcripts from audio streams, NER APIs provide the foundation for understanding what matters most in your text data. The challenge isn't finding an NER solution—it's choosing the right one from the growing ecosystem of providers, each with different strengths in accuracy, features, and implementation approaches.

In this article, we'll explore what Named Entity Recognition is, how these APIs work under the hood, and evaluate the best NER APIs available today. We'll also cover practical implementation guidance to help you integrate entity detection into your applications.

What is Named Entity Recognition, or entity detection?

Named Entity Recognition (NER) automatically identifies and categorizes specific information—like names, organizations, locations, and dates—from text or audio. For example, in the sentence "Apple will open a store in New York next month," NER identifies Apple (organization), New York (location), and next month (date).

Product teams and developers use entity detection to automatically extract key information from unstructured data:

• People and organizations - Names, companies, brands
• Locations - Cities, addresses, geographic references
• Sensitive data - Phone numbers, social security numbers, credit cards
• Temporal information - Dates, times, durations

Entity detection is fundamentally what's often described as a two-step process:

1. Identifying entities – detecting that "Apple" or "John Smith" are important elements in the text
2. Classifying the entities – determining whether "Apple" refers to the company (ORGANIZATION) or the fruit (PRODUCT), or that "John Smith" is a PERSON

This structured extraction enables applications to automatically understand who's involved, what organizations are mentioned, where events occur, and when they happen—all without manual processing.

How does entity detection work?

Named Entity Recognition works by analyzing text to identify notable objects and their relationships. This process proves particularly valuable when extracting entities from transcripts generated by speech-to-text APIs, where understanding spoken content at scale becomes critical for businesses.

As mentioned above, NER must both identify and categorize information. The process begins with text preprocessing, where the system breaks down sentences into individual words or tokens. Then, it analyzes each token within its context to determine if it represents an entity and, if so, what type of entity it is.

Modern NER systems analyze context to make accurate predictions. For instance, "Washington" might refer to a person, state, or city—advanced systems distinguish between these meanings by examining surrounding text.

The goal remains consistent: transforming unstructured text into structured, actionable data that applications can process and analyze.

NER methods and approaches

Named Entity Recognition has evolved through several methodological approaches over the years, each with distinct advantages and limitations. Understanding these different methods helps you choose the right NER solution for your specific needs.

Rule-based methods

Rule-based NER relies on manually crafted patterns and grammatical rules to identify entities. These systems use techniques like regular expressions to match text patterns (like phone numbers or email addresses) and part-of-speech tagging to identify proper nouns that might be entities.

For example, a rule might state that any capitalized word followed by "Inc." or "Corp." is likely a company name. While straightforward to implement for specific domains, rule-based systems struggle with ambiguity, require constant maintenance, and fail to generalize to new contexts or languages.

Lexicon-based methods

Lexicon-based approaches use predefined dictionaries or gazetteers containing lists of known entities. The system matches text against these comprehensive databases of names, locations, organizations, and other entity types.

This approach works well for domains with well-established terminology—like medical fields with standardized drug names or geographic applications with finite location lists. However, its effectiveness depends entirely on the completeness of the underlying lexicons. New entities, misspellings, or entities mentioned in unexpected contexts often go undetected.

Machine learning-based methods

Machine learning-based NER uses statistical models trained on large annotated datasets to learn patterns for identifying and classifying entities. These systems can recognize entities they've never seen before by learning from context and linguistic features.

Traditional machine learning approaches like Conditional Random Fields (CRF) and Support Vector Machines (SVM) analyze features such as word shape, surrounding words, and part-of-speech tags. Modern deep learning approaches, particularly those using transformer architectures, achieve even better results by capturing complex contextual relationships.

For instance, an AI model can recognize "Tesla" as a company when it appears near words like "earnings" or "CEO," but classify it as a person when discussing historical scientists. This contextual understanding makes machine learning approaches significantly more accurate and flexible, allowing them to identify recurring entities for further analysis, especially when dealing with:

• Previously unseen entities
• Ambiguous references
• Entities in multiple languages
• Informal or conversational text

Most modern NER APIs, including AssemblyAI's Entity Detection, use advanced machine learning approaches enhanced with transformer architectures that power today's most sophisticated language understanding systems.

Common entity types

Different NER APIs support varying sets of entities. While most handle common categories like PERSON, ORGANIZATION, and LOCATION, specialized providers offer domain-specific entities such as medical conditions, financial instruments, or technical terminology. When evaluating APIs, consider which entity types are critical for your use case.

For example, AssemblyAI supports a wide range of entities, from common types like names and locations to sensitive PII like credit card and social security numbers. For the complete, up-to-date list, please see the Supported Entities table in our documentation.

Top use cases for NER APIs

Why is entity detection important? Entity detection can be an extremely valuable data collection and analytical tool for product teams and developers across a wide range of industries.

Modern applications leverage NER APIs across diverse industries:

Telephony and CRM platforms

Companies like CallSource and Ringostat use NER to identify specific people, company, or competitor names from call transcripts and automatically populate CRM fields. This automation improves response times by instantly categorizing conversations and surfacing critical information to sales and support teams.

Hiring and recruitment platforms

Recruitment platforms extract roles, companies, skills, and salary information from resumes and job postings. This enables recruiters to quickly match candidates with opportunities and build searchable talent databases without manual data entry.

Virtual meeting and collaboration tools

Platforms like Recall and Dyte leverage NER to identify participants, companies, and discussion topics from meeting transcripts. This data powers features like automatic meeting summaries, action item extraction, and knowledge management systems.

Voice assistants and conversational AI

Voice bots use entity detection to identify people, companies, or products mentioned in conversations. This enables them to trigger contextually appropriate actions and personalize responses based on extracted information.

Healthcare and medical documentation

In environments that handle protected health information, NER identifies medical conditions, medications, procedures, and patient information from clinical notes, with some systems designed to spot these entities directly from conversational speech in transcribed consultations. Companies like T-Pro use this technology to automate medical documentation while maintaining regulatory compliance. AssemblyAI enables covered entities and their business associates subject to HIPAA to use our services to process protected health information (PHI). AssemblyAI is considered a business associate under HIPAA, and we offer a Business Associate Addendum (BAA) that is required under HIPAA to ensure that we appropriately safeguard PHI.

Media monitoring and brand intelligence

News organizations and brand monitoring services use NER to track mentions of companies, products, and public figures across thousands of articles, broadcasts, and social media posts. This enables real-time reputation management and competitive intelligence.

By collecting entity information systematically, product teams gain invaluable insights into customer behavior, market trends, and operational patterns. These insights drive better decision-making across marketing campaigns, product development, and strategic planning.

Organizations implementing NER APIs report measurable business outcomes:

• 30-50% reduction in manual data entry time
• 25% improvement in customer response times through automated CRM population
• 40% decrease in compliance violations through automated PII detection
• ROI of 300-400% within 12 months for conversation intelligence platforms

Implementation typically takes 2-4 weeks for basic integration, with advanced use cases requiring 6-8 weeks for full deployment.

How to evaluate NER APIs

Choosing the right NER API requires evaluating six critical factors that directly impact accuracy and business outcomes:

Accuracy and performance benchmarks

Look for providers that publish accuracy metrics and offer free tiers for testing. Evaluate performance on your specific data type—medical transcripts require different capabilities than social media posts.

Entity coverage and customization

Review the supported entity types against your requirements. Basic APIs might only detect people, places, and organizations, while advanced solutions include dates, monetary values, and industry-specific entities.

Processing speed and scalability

For real-time applications like voice assistants, latency matters most. Batch processing applications prioritize throughput over speed.

Developer experience and integration

Strong documentation, client libraries, and clear code examples accelerate implementation. Look for APIs with straightforward authentication and consistent response formats.

Data security and compliance

For sensitive applications, verify security certifications (SOC 2, ISO 27001), data handling practices, and compliance with regulations like GDPR or HIPAA.

Pricing model and total cost

Compare pricing structures carefully. Some providers charge per request, others by character count or processing time.

What are the best entity detection APIs for Named Entity Recognition?

Now that we've covered evaluation criteria, let's examine the leading entity detection APIs available today. Note that some APIs specialize in processing existing text, while others perform entity detection on audio or video streams while simultaneously transcribing them.

1. AssemblyAI

AssemblyAI's Entity Detection, part of its suite of Speech Understanding models, detects a wide range of entities from transcribed audio at industry-leading accuracy. The API excels at processing conversational speech, making it ideal for call centers, meeting platforms, and voice applications. AssemblyAI's entity detection capabilities include PII like driver's_license and banking_information (including account and routing numbers).

Developers and product managers use AssemblyAI's Entity Detection API across diverse AI applications, including Revenue Intelligence Platforms and Conversation Intelligence Platforms. The API integrates seamlessly with AssemblyAI's speech-to-text capabilities, allowing you to transcribe and analyze audio in a single API call.

2. Dandelion

Dandelion offers entity extraction for documents and social media content. The European-based API supports multiple languages including English, Italian, French, German, Portuguese, Spanish, and Russian with varying accuracy levels. While specific entity types aren't publicly documented, the service focuses on web content and social media analysis.

Developers can test the Entity Detection tool free up to a certain threshold, making it accessible for prototyping and small projects.

3. Google Natural Language

Google's Natural Language API provides entity analysis and extraction alongside sentiment analysis, syntax analysis, and content classification. Their service offers two components:

1. Entity Analysis - identifies entities in documents like contracts and receipts, labeling them by type using Google's pre-trained models
2. Custom Entity Extraction - allows training custom models to identify domain-specific entities using your own labeled data

While Google's Natural Language API has higher pricing than some alternatives, it offers a free tier for up to 5,000 units monthly. Developers can combine it with Google's Cloud Speech-to-Text for audio processing, though this requires managing two separate services.

4. Azure Cognitive Services

Azure Cognitive Services provides AI-based analysis across speech, language, vision, and decision applications. Entity Recognition is part of their Language service, detecting both common and custom entities in text or transcribed audio.

While Azure offers a free tier for testing, the initial setup can be complex, particularly when combining speech recognition with entity detection. The platform suits enterprises already invested in the Azure ecosystem.

5. TextRazor

The TextRazor API extracts entities and relationships from documents, focusing on understanding "who, what, why, and how" from text. Their Named Entity Recognition identifies people, places, companies, and uses disambiguation techniques to improve accuracy, though performance typically trails specialized NER providers.

Pricing starts at $200 monthly for 6,000 daily requests, positioning it for medium-scale applications rather than high-volume processing.

6. Allganize

Allganize provides an NLU API designed for customer interaction analysis. Their Named Entity Recognition automatically classifies keywords and extracts information about people, places, and events from customer communications.

Their Growth tier includes a free trial, then costs $0.02 per call for up to 10,000 calls, making it cost-effective for customer service applications with moderate volume.

Getting started with NER API integration

Integrating an NER API into your application follows a straightforward process. While implementation details vary between providers, the workflow generally follows these steps:

Step 1: Obtain API credentials

Sign up with your chosen provider to get an API key or authentication token. Most providers offer free tiers or trial credits for testing.

Step 2: Prepare your text data

Ensure your text is UTF-8 encoded. If working with audio or video content, you'll first need transcription using a speech-to-text service.

Step 3: Make the API request

With AssemblyAI, you enable Entity Detection on an audio file by setting the entity_detection parameter to true in a POST request to the /v2/transcript endpoint. Here's a typical request structure:

curl https://api.assemblyai.com/v2/transcript \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL",
  "entity_detection": true
}'

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Step 4: Process the response

The API returns detected entities with types, text, and timestamps. A typical response looks like:

{
  "entities": [
    {
      "entity_type": "location",
      "text": "Canada",
      "start": 2548,
      "end": 3130
    },
    {
      "entity_type": "person_name",
      "text": "John Smith",
      "start": 4102,
      "end": 4180
    },
    {
      "entity_type": "organization",
      "text": "Apple",
      "start": 5230,
      "end": 5280
    }
  ]
}

‍


Implementation best practices

For a hands-on example of entity detection with audio files, check out the tutorial below or explore AssemblyAI's documentation for comprehensive code samples.

Entity detection tutorial

Want to learn how to perform Entity Detection on audio files in Python? While the video tutorial below walks through the process using our API directly, the easiest way to get started is with our Python SDK. Here's a quick example:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

# Create a transcriber object
transcriber = aai.Transcriber()

# Configure the transcription with Entity Detection enabled
config = aai.TranscriptionConfig(entity_detection=True)

# Transcribe the audio file
transcript = transcriber.transcribe("./my-audio.mp3", config)

# Print the detected entities
if transcript.entities:
    for entity in transcript.entities:
        print(f"Text: {entity.text}, Type: {entity.entity_type}")
else:
    print("No entities detected.")

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For a more detailed walkthrough, see the video tutorial below:

Build Voice AI applications with entity detection

Voice AI applications combine speech-to-text with entity detection to build systems that understand both content and meaning.

Real-world impact:

• Conversation intelligence platforms process thousands of hours daily
• Real-time extraction of customer names, products, competitive mentions
• Business outcomes: Faster response times, improved customer understanding
• Measurable results: 25% improvement in sales conversion rates

Companies like CallSource and Dyte use this technology to automatically surface critical insights to sales and support teams.

Building Voice AI applications with entity detection requires a robust foundation. Your speech-to-text system must accurately transcribe diverse accents, handle background noise, and maintain high accuracy across different audio conditions. Then, your entity detection must work seamlessly with the transcribed text, identifying entities even when they're mentioned in casual conversation or industry-specific jargon.

AssemblyAI's platform streamlines this process by combining industry-leading speech-to-text with sophisticated entity detection in a single API call. Companies building Voice AI applications benefit from:

• Unified processing: Transcription and entity detection happen together, reducing latency and complexity
• Contextual accuracy: Entity detection that understands conversational speech patterns
• Scalable infrastructure: Process millions of hours of audio without performance degradation
• Comprehensive entity coverage: From basic names to sensitive PII detection

Whether you're building a voice-powered CRM integration, a meeting assistant, or a compliance monitoring system, combining Voice AI with entity detection transforms raw audio into structured, actionable business intelligence.

Frequently asked questions about Named Entity Recognition APIs

What is the difference between NER and entity linking?

NER identifies and categorizes entities like "Apple" as an organization, while entity linking disambiguates which specific Apple and connects it to knowledge base entries.

How accurate are modern NER systems?

While modern NER systems typically achieve 85-95% accuracy on common entities, some providers report verified 99%+ precision on fully trained corpora for major languages, and specialized medical/legal systems can reach 90-98% in their domains.

Can NER work with multiple languages?

Yes, many modern NER APIs support multiple languages, with some providers covering more than a dozen, though accuracy and entity type coverage do vary by language. Some providers offer multilingual models that can process text in various languages, while others provide language-specific models optimized for particular languages.

How is NER used with speech-to-text?

NER processes transcribed audio to identify entities like speaker names, companies, or locations mentioned in conversations. Some providers like AssemblyAI offer integrated solutions that perform both transcription and entity detection in a single API call.

What's the typical cost of NER API services?

Pricing varies significantly across providers and depends on volume, features, and support levels. Most providers offer free tiers for testing, with production pricing ranging from $0.001 to $0.05 per API call or $50-$500 monthly for moderate usage.

Ready to add entity detection to your application? Try AssemblyAI's API free and extract entities from audio or text with industry-leading accuracy.