6 best named entity recognition APIs for entity detection

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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.

How to evaluate NER APIs

When choosing a Named Entity Recognition API, success depends on finding a solution that matches your specific technical requirements, use case, and scale.

Key evaluation criteria include:

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, lets 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, youll 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
}'

Step 4: Process the response

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

{1 item
"entities":[33 items
0:{4 items
"entity_type":"location"
"text":"Canada"
"start":2548
"end":3130
}
1:{...}4 items
2:{...}4 items
3:{...}4 items
4:{...}4 items
5:{...}4 items
6:{...}4 items
7:{...}4 items
8:{...}4 items
9:{...}4 items
10:{...}4 items
11:{...}4 items
12:{...}4 items
13:{...}4 items
14:{...}4 items
15:{...}4 items
16:{...}4 items
17:{...}4 items
18:{...}4 items
19:{...}4 items
20:{...}4 items
21:{...}4 items
22:{...}4 items
23:{...}4 items
24:{...}4 items
25:{...}4 items
26:{...}4 items
27:{...}4 items
28:{...}4 items
29:{...}4 items
30:{...}4 items
31:{...}4 items
32:{...}4 items
]
}

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 or Named Entity Recognition on audio files in Python? This tutorial walks through the complete process:

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 goes further by disambiguating which specific Apple (company vs. fruit) and connecting it to a knowledge base entry.

How is NER used with speech-to-text?

NER processes transcribed audio to identify entities like speaker names, companies, or locations mentioned in conversations, commonly used in call analytics and conversation intelligence platforms.

Can I detect custom entities with an API?

Yes, advanced NER APIs from providers like Google and Azure support custom entity training for industry-specific terminology, though this typically requires labeled training examples and additional costs.

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