6 best named entity recognition APIs for entity detection
In this article, we’ll look at what exactly named entity recognition is, how it works, the best APIs for performing entity detection, and some of its top use cases.
Named Entity Recognition (NER) APIs have become essential for developers building applications that extract meaningful information from text. They automatically identify and categorize key details — names, organizations, locations, and more — turning unstructured text into structured, actionable data.
Whether you’re analyzing customer feedback, building conversation intelligence platforms, or processing transcripts from audio, NER gives you the foundation for understanding what matters in your data. The hard part isn’t finding an NER solution — it’s choosing the right one from a growing set of providers, each strong in different areas of accuracy, features, and implementation.
This article covers what named entity recognition is, how these APIs work, and the best NER APIs available as of 2026, plus practical guidance for integrating 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 “Apple will open a store in New York next month,” NER identifies Apple (organization), New York (location), and next month (date).
Teams use entity detection to 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 a two-step process:
- Identifying entities — detecting that “Apple” or “John Smith” are important elements in the text
- Classifying entities — determining whether “Apple” refers to the company (ORGANIZATION) or the fruit (PRODUCT), or that “John Smith” is a PERSON
This structured extraction lets applications understand who’s involved, what organizations are mentioned, where events happen, and when — without manual processing.
How does entity detection work?
NER analyzes text to identify notable objects and their relationships. It’s especially valuable when extracting entities from transcripts generated by speech-to-text APIs, where understanding spoken content at scale becomes critical.
NER must both identify and categorize. The process starts with text preprocessing — breaking sentences into tokens — then analyzes each token in context to decide whether it’s an entity and what type. Modern systems use context to disambiguate: “Washington” might be a person, state, or city, and advanced systems tell them apart by examining the surrounding text.
The goal stays constant: transform unstructured text into structured, actionable data your application can process.
NER methods and approaches
Named entity recognition has evolved through several approaches, each with tradeoffs.
Rule-based methods
Rule-based NER uses hand-crafted patterns and grammatical rules — regular expressions for phone numbers or emails, part-of-speech tagging for proper nouns. A rule might say any capitalized word followed by “Inc.” or “Corp.” is likely a company. Straightforward for narrow domains, but rule-based systems struggle with ambiguity, need constant maintenance, and don’t generalize to new contexts or languages.
Lexicon-based methods
Lexicon-based approaches match text against predefined dictionaries of known entities. They work well for domains with established terminology, like standardized drug names — but effectiveness depends entirely on how complete the lexicon is. New entities, misspellings, or unexpected contexts often go undetected.
Machine learning-based methods
Machine learning-based NER trains statistical models on large annotated datasets to learn patterns for identifying and classifying entities, including ones they’ve never seen. Traditional approaches like Conditional Random Fields and Support Vector Machines analyze word shape, surrounding words, and part-of-speech tags. Modern deep learning with transformer architectures does even better by capturing complex contextual relationships.
An AI model can recognize “Tesla” as a company near words like “earnings” or “CEO,” but as a person when discussing historical scientists. That contextual understanding makes machine learning approaches more accurate and flexible, especially with previously unseen entities, ambiguous references, multiple languages, and informal conversational text.
Most modern NER APIs, including AssemblyAI’s Entity Detection, use machine learning enhanced with transformer architectures.
Common entity types
Different NER APIs support different entity sets. Most handle PERSON, ORGANIZATION, and LOCATION; specialized providers add domain-specific entities like medical conditions or financial instruments. When evaluating APIs, check which entity types are critical for your use case.
AssemblyAI supports a wide range, from common types like names and locations to sensitive PII like credit card and social security numbers. For the complete, current list, see the Supported Entities table in the Entity Detection documentation.
How to evaluate NER APIs
Choosing the right NER API means weighing six factors that directly affect accuracy and outcomes.
Accuracy and performance: favor providers that publish benchmarks and offer free tiers. Evaluate on your own data — medical transcripts need different capabilities than social posts. For a deeper method, see how to evaluate speech recognition models.
Entity coverage: basic APIs detect people, places, and organizations; advanced ones add dates, monetary values, and industry-specific entities.
Processing speed: for real-time applications like voice assistants, latency matters most; batch workloads prioritize throughput.
Developer experience: strong docs, client libraries, and clear examples speed up integration.
Security and compliance: for sensitive data, verify certifications (SOC 2, ISO 27001), data handling, and compliance support like GDPR and, for healthcare, a HIPAA Business Associate Addendum (BAA).
Pricing model: some providers charge per request, others by character count or processing time.
Best entity detection APIs for named entity recognition (2026)
Now that we’ve covered the criteria, here’s how the leading entity detection APIs compare as of 2026. Some specialize in existing text; others perform entity detection on audio or video while transcribing it.
1. AssemblyAI
AssemblyAI’s Entity Detection, part of its suite of Speech Understanding models, detects a wide range of entities from transcribed audio. It excels at conversational speech, which makes it a strong fit for call centers, meeting platforms, and voice applications, and it covers PII like drivers_license and banking_information (including account and routing numbers).
Entities extracted from audio are only as accurate as the transcript they come from. AssemblyAI runs entity detection on top of Universal-3.5 Pro, its most accurate speech-to-text model, and returns transcript plus entities in a single API call — so names, phone numbers, and locations aren’t lost to transcription errors. On the Pipecat open speech-to-text benchmark, AssemblyAI posts a 15.31% entity error rate versus 50.50% for Deepgram, with a 3.55% error rate on phone numbers and 6.28% on places (see the benchmarks).
That single-call design reflects AssemblyAI’s positioning as a Voice AI infrastructure platform: entity detection sits alongside PII redaction, sentiment analysis, and topic detection as Speech Understanding models on the same request — not a separate NER service to bolt on. Developers use the API across Revenue Intelligence Platforms and Conversation Intelligence Platforms.
2. Dandelion
Dandelion offers entity extraction for documents and social media. The European-based API supports English, Italian, French, German, Portuguese, Spanish, and Russian with varying accuracy, and focuses on web and social content. Developers can test it free up to a threshold, which makes it accessible for prototyping.
3. Google Natural Language
Google’s Natural Language API provides entity analysis alongside sentiment, syntax, and content classification, with two components: Entity Analysis (identifies entities using pre-trained models) and Custom Entity Extraction (train custom models on your labeled data). Pricing runs higher than some alternatives, but there’s a free tier up to 5,000 units monthly. You can combine it with Google Cloud Speech-to-Text for audio, though that means managing two services.
4. Azure Cognitive Services
Azure Cognitive Services offers AI analysis across speech, language, vision, and decision applications. Entity Recognition is part of the Language service and detects common and custom entities in text or transcribed audio. There’s a free tier, though initial setup can be complex when combining speech recognition with entity detection. It suits teams already in the Azure ecosystem.
5. TextRazor
The TextRazor API extracts entities and relationships from documents, focusing on “who, what, why, and how.” Its NER identifies people, places, and companies and uses disambiguation to improve accuracy, though performance typically trails specialized providers. Pricing starts at $200/month for 6,000 daily requests, positioning it for medium-scale use.
6. Allganize
Allganize provides an NLU API for customer-interaction analysis. Its NER classifies keywords and extracts information about people, places, and events from customer communications. The Growth tier includes a free trial, then costs $0.02 per call for up to 10,000 calls — cost-effective for moderate-volume customer service applications.
Top use cases for NER APIs
Entity detection is a valuable data-collection and analysis tool for teams across industries.
Telephony and CRM platforms
In telephony and CRM, platforms use NER to identify people, companies, or competitor names from call transcripts and automatically populate CRM fields. That speeds up response times by categorizing conversations and surfacing critical information for sales and support teams.
Hiring and recruitment platforms
Recruitment platforms extract roles, companies, skills, and salary details from resumes, job postings, and interview conversations to match candidates and build searchable talent databases. Recruiting intelligence platform Metaview is one team building on AssemblyAI here — its co-founder and CTO Shahriar Tajbakhsh describes the impact:
“Since moving to AssemblyAI, we’ve seen a meaningful improvement in the confidence tail of our production transcripts….What stands out is not just the model quality, but the way [they] let us bring real meeting context into transcription, from calendar titles to organizations, domains, and participant names, so recruiting conversations come through with the nuance our customers depend on.” — Shahriar Tajbakhsh, Co-founder and CTO, Metaview
Virtual meeting and collaboration tools
Meeting platforms use NER to identify participants, companies, and discussion topics from transcripts, powering automatic summaries, action-item extraction, and knowledge management. Companies in this space include Recall.ai, which provides meeting-recording infrastructure to developers.
Voice assistants and conversational AI
Voice bots use entity detection to identify people, companies, or products mentioned in conversation, triggering contextually appropriate actions and personalizing responses.
Healthcare and medical documentation
In environments that handle protected health information, NER identifies medical conditions, medications, procedures, and patient details from clinical notes, with some systems spotting these entities directly from conversational speech. AssemblyAI enables covered entities and their business associates subject to HIPAA to use its services to process protected health information (PHI). AssemblyAI is considered a business associate under HIPAA and offers a Business Associate Addendum (BAA), which is required under HIPAA to ensure PHI is appropriately safeguarded. For more on this use case, see AssemblyAI’s medical transcription solution.
Media monitoring and brand intelligence
News and brand-monitoring services use NER to track mentions of companies, products, and public figures across articles, broadcasts, and social posts. Market-intelligence platforms like AlphaSense operate in this space, applying entity extraction for real-time reputation management and competitive intelligence.
Getting started with NER API integration
Integrating an NER API follows a straightforward path. Details vary by provider, but the workflow is consistent.
Step 1: Obtain API credentials
Sign up with your provider to get an API key. Most offer free tiers or trial credits.
Step 2: Prepare your data
Ensure text is UTF-8 encoded. For audio or video, transcribe it first with a speech-to-text service.
Step 3: Make the API request
With AssemblyAI, enable Entity Detection by setting entity_detection to true (docs):
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:
{
entities: [
{
entity_type: "location",
text: "Canada",
start: 2548,
end: 3130,
},
{
entity_type: "location",
text: "the US",
start: 5498,
end: 6382,
},
{
entity_type: "location",
text: "Maine",
start: 7492,
end: 7914,
},
{
entity_type: "location",
text: "Maryland",
start: 8212,
end: 8634,
},
{
entity_type: "location",
text: "Minnesota",
start: 8932,
end: 9578,
},
{
entity_type: "person_name",
text: "Peter de Carlo",
start: 18948,
end: 19930,
},
{
entity_type: "occupation",
text: "associate professor",
start: 20292,
end: 21194,
},
{
entity_type: "organization",
text: "Department of Environmental Health and Engineering",
start: 21508,
end: 23706,
},
{
entity_type: "organization",
text: "Johns Hopkins University Varsity",
start: 23972,
end: 25490,
},
{
entity_type: "occupation",
text: "professor",
start: 26076,
end: 26950,
},
{
entity_type: "location",
text: "the US",
start: 45184,
end: 45898,
},
{
entity_type: "nationality",
text: "Canadian",
start: 49728,
end: 50086,
},
{
entity_type: "location",
text: "Pennsylvania",
start: 51680,
end: 52326,
},
{
entity_type: "location",
text: "Mid Atlantic",
start: 52624,
end: 53178,
},
{
entity_type: "location",
text: "Northeast",
start: 53428,
end: 53866,
},
{
entity_type: "location",
text: "Baltimore",
start: 65064,
end: 65534,
},
{
entity_type: "occupation",
text: "science",
start: 101168,
end: 101446,
},
{
entity_type: "location",
text: "New York City",
start: 125768,
end: 126274,
},
{
entity_type: "medical_condition",
text: "respiratory conditions",
start: 152964,
end: 153786,
},
{
entity_type: "medical_condition",
text: "heart conditions",
start: 153988,
end: 154506,
},
{
entity_type: "location",
text: "New York",
start: 171448,
end: 171938,
},
{
entity_type: "location",
text: "New York",
start: 176008,
end: 176322,
},
{
entity_type: "location",
text: "the US",
start: 201824,
end: 202202,
},
{
entity_type: "location",
text: "mid Atlantic",
start: 209010,
end: 209866,
},
{
entity_type: "location",
text: "Northeast region",
start: 210196,
end: 211082,
},
{
entity_type: "location",
text: "Western US",
start: 257364,
end: 258046,
},
{
entity_type: "location",
text: "eastern US",
start: 258484,
end: 259054,
},
{
entity_type: "person_name",
text: "Peter De Carlo",
start: 268298,
end: 269194,
},
{
entity_type: "occupation",
text: "associate professor",
start: 269242,
end: 270186,
},
{
entity_type: "organization",
text: "Department of Environmental Health and Engineering",
start: 270404,
end: 272762,
},
{
entity_type: "organization",
text: "Johns Hopkins University",
start: 273156,
end: 274850,
},
{
entity_type: "occupation",
text: "Sergeant",
start: 274970,
end: 275298,
},
{
entity_type: "person_name",
text: "Carlo",
start: 275314,
end: 275634,
},
],
}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 — conversation intelligence platforms that process thousands of hours daily, real-time extraction of customer names, products, and competitive mentions.
Building these applications requires a solid foundation. Your speech-to-text has to transcribe diverse accents, handle background noise, and stay accurate across audio conditions. Then entity detection has to work on that transcript, catching entities even in casual conversation or industry jargon.
AssemblyAI’s platform combines accurate speech-to-text with entity detection in a single API call, giving teams:
- Unified processing: transcription and entity detection happen together, reducing latency and complexity
- Contextual accuracy: entity detection that understands conversational speech
- Scalable infrastructure: process millions of hours without performance degradation
- Comprehensive coverage: from basic names to sensitive PII
Whether you’re building a voice-powered CRM integration, a meeting assistant, or a compliance monitoring system, pairing Voice AI with entity detection turns raw audio into structured business intelligence. Teams building conversational agents can add entity detection on top of the Voice Agent API.
Frequently asked questions
What is the best NER API for audio or call transcripts?
For audio, AssemblyAI is the strongest choice because it performs transcription and entity detection in a single API call, running entity detection on top of its most accurate speech-to-text model. That keeps names, organizations, and PII tied to what was actually said — critical for calls and meetings.
How much does a named entity recognition API cost?
Pricing varies by provider and volume. Text-only APIs range from free tiers to $200+/month (TextRazor starts at $200/month; Allganize is $0.02/call). Audio-based NER is priced by usage — AssemblyAI charges per second at $0.21/hr for its flagship speech-to-text, with Entity Detection included as a Speech Understanding model.
How accurate are modern NER systems?
Modern NER systems typically achieve 85–95% accuracy on common entities, with some providers reporting verified 99%+ precision on fully trained corpora for major languages. Specialized medical and legal systems can reach 90–98% in their domains, though accuracy varies by language and audio quality.
What is the difference between NER and entity linking?
NER identifies and categorizes entities — labeling “Apple” as an organization — while entity linking disambiguates which specific Apple it is and connects it to knowledge base entries.
Can NER work with multiple languages?
Yes. Many modern NER APIs support multiple languages, some covering more than a dozen, though accuracy and entity coverage vary by language. Some providers offer multilingual models; 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 conversation. Providers like AssemblyAI offer integrated solutions that perform both transcription and entity detection in a single API call.
Ready to add entity detection to your application? Try AssemblyAI free and extract entities from audio or text in a single API call.
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