Accurate, human-in-the-loop entity annotation for NLP, LLM and RAG training — secure, scalable, and delivered in 60+ languages.
Named Entity Recognition (NER), identifies and classifies entities such as people, organizations, and locations within unstructured text. NER enhances data extraction, simplifies information retrieval, and powers advanced AI applications, making it a vital tool for businesses to leverage. With NER, organizations can gain valuable insights, improve customer experiences, and streamline processes.
Shaip NER is designed to allow organizations to unlock critical information in unstructured data & lets you discover relationships among entities from financial statements, insurance documents, reviews, physician notes, etc. NER can also help identify relationships among entities of the same type, such as multiple organizations or individuals mentioned in a document, which is important for consistency in entity tagging and improving model accuracy. With rich experience in NLP & linguistics, we are well equipped to deliver domain-specific insights to handle annotation projects of any scale.
Why teams outsource NER to Shaip
Turning raw, messy text into training-ready NER data is hard when you don’t have an annotation team of your own. We handle it end to end — accurately, securely, and at the scale your models need.
What we do
Full-service named entity recognition annotation — from general entities to the most specialized, regulated domains.
Named Entity Recognition in Machine Learning is a part of Natural Language Processing. The primary objective of NER is to process structured and unstructured data and classify these named entities into predefined categories. Some common categories include name, person entity, location, company, time, monetary values, events, and more.
Identification of people, places, organizations, dates and other common entities in general-domain text such as news, web content, emails and customer conversations, labeled consistently to train NER models for broad language understanding.
Extraction of key entities from insurance documents, including sums insured, limits of indemnity and policy limits, estimates such as wage roll, turnover and fee income, vehicle schedules, and policy extensions with inner limits.
Identification of problems, anatomical structures, medicines and procedures in unstructured medical records such as EHRs and physician notes, annotated by healthcare domain experts to turn complex clinical text into structured, usable data.
It identifies a discrete noun phrase in a text. A noun phrase may be either simple (e.g. single head word like noun, proper noun or pronoun) or complex (e.g. a noun phrase that has a head word along with its associated modifiers)
PII refers to Personally Identifiable Information. This task involves annotation of any key identifiers which can relate back to a person’s identity.
PHI refers to Protected Health Information. This task involves annotation of 18 key patient identifiers as identified under HIPAA, in order to de-identify a patient record/identity.
Identification of information like who, what, when, where about an event e.g. Attack, kidnapping, Investment etc. This annotation process has following steps:
Entity Identification(e.g. Person, place, organization, etc.)
Identification of word denoting the main incident(i.e. trigger word)
Identification of relation between a trigger and entity types
Depth & complexity
Whatever your entity schema demands, our annotators handle it — including the hard cases most vendors avoid.
Core entity types labeled to your schema.
Bespoke entity types for your industry and use case.
Sub-types and hierarchies, not just broad buckets.
Entities inside entities, handled accurately.
Multiple valid labels per span where context demands.
Enrich entities with attributes and relationships.
Map variants to a single canonical entity.
Adjudicated, audit-ready benchmark data.
Built for Modern AI
Entity-labeled data does more than power classic NLP pipelines. It grounds retrieval, structures knowledge for RAG, and sharpens fine-tuned models on the entities that matter to your business.
We build the datasets that make your models extract the right entities — reliably, in your domain, at production scale.
Industries we serve
Extract diseases, drugs and procedures from clinical notes and research to power medical AI.
Pull companies, amounts and instruments from filings and statements for risk and compliance.
Identify parties, clauses and obligations across contracts and case files.
Route and classify tickets by extracting products, issues and intent from messages.
Tag brands, products and attributes to improve search, recommendations and catalogs.
Track people, organizations and places for monitoring, research and document workflows.
Scale, Expertise & Accuracy You Can Trust
Create, curate, and collect custom-built datasets (text, speech, image, video) from across the globe based on custom guidelines.
Leverage 10,000+In-house global workforce and 500K+ Crowd-scale credentialed contributors. Real-time workforce capacity and efficiency.
Our proprietary platform & skilled workforce use multiple quality control methods to meet or exceed quality standards.
Our process streamlines, the collection process through easier task distribution, & data capture directly from the app & web.
Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.
Every collected dataset can be annotated, labeled, transcribed, and validated in the same workflow, delivering model-ready training data.
How it works
A clear, controlled workflow from brief to delivery — with quality built in at every step.
We align on your entity schema, edge cases and domain requirements, then set annotation guidelines.
Domain-trained annotators label your data, guided by team leads to keep decisions consistent.
Multi-stage review, adjudication and inter-annotator agreement checks before training-ready delivery.
How Shaip turns complex, sensitive data into training-ready datasets — securely and at scale.
Problem: Millions of daily search queries — full of typos, ambiguity, and overlapping categories — were hurting search relevance and customer experience.
Solution: An 11-class annotation framework with clear precedence rules and a 3-step workflow: annotation, QA review, and SME arbitration for edge cases.
Result: 50,000+ queries classified with high precision — improving relevance, product discoverability, and user satisfaction.
Problem: A healthcare leader needed to mine large volumes of oncology records for NLP — without compromising patient privacy or clinical accuracy.
Solution: Curated data from 5M+ health records, HIPAA Safe Harbor de-identification, and oncology-trained annotators labeling clinical entities, relationships, and negation.
Result: 10,000+ high-quality, de-identified labeled records — a secure, training-ready dataset for the client’s NLP model development.
Contact us now to learn how we can collect a custom NER dataset for your unique AI/ML solution
NER annotation services provide expert-labeled training data for named entity recognition — a team tags entities such as people, organizations, locations, dates and domain-specific terms in your text so your NLP or LLM model can learn to extract them accurately. Shaip provides this end to end, including guidelines, annotation, and multi-pass quality control.
Specialist AI data providers like Shaip build custom NER datasets. Shaip delivers multilingual, domain-specific entity data — including clinical, financial and PII/PHI — with enterprise security and human-in-the-loop QA, ready for NLP, LLM, RAG and fine-tuning workloads.
Cost depends on data volume, the number and complexity of entity types, languages, domain expertise required, and quality and turnaround targets. Shaip scopes each project to your needs and can start with a free pilot so you see quality and price before committing.
Yes. Shaip annotates entities consistently across 60+ languages and scripts using native-speaker annotators and language-specific QA.
Yes. Shaip offers clinical and medical NER, and PII/PHI annotation covering all 18 HIPAA identifiers, handled by domain-trained annotators under HIPAA-aligned, secure workflows.
Clear annotation guidelines, domain-trained annotators, team-lead oversight, and multi-stage QA with adjudication and inter-annotator agreement checks before delivery.
Security is the default: NDA by default, access controls, and HIPAA, SOC 2, ISO 27001, GDPR and CCPA-aligned processes. Your data stays protected throughout the engagement.
Most projects can start within days. A free pilot lets you validate quality quickly, after which Shaip scales to your full volume on an agreed timeline.
