“We were very happy with the quality of the data that we purchased. It will assist our efforts to improve our product. The team at Unidata was very responsive to our needs, and provided the data format needed to complete our tasks. We are happy to state that Unidata delivers a high quality product in an effective and efficient manner.”
Named Entity Recognition Services
Reliable, training-ready data for ML teams—delivered with clarity and control.
- Fully managed NER workflow — ready to launch, easy to scale
- Private from the start — protected by strict NDAs
- Adaptable annotation team for changing specifications
What is NER?
Named Entity Recognition (NER) is a core task in Natural Language Processing (NLP) that identifies and classifies entities—such as person names, organizations, locations, and dates—within unstructured text.
To do this well, models must first learn from reliable training data that reflects real-world language, custom entity types, and tagging logic. That’s why high-quality human annotation is essential for reliable NER results.
Accurate Entity Annotation is Hard — We De-Risk It
We help ML teams without internal annotation capacity turn unstructured data into training-ready NER outputs with our named entity recognition services —even for niche domains and shifting specs.
Without the right partner, it’s risky
- No internal annotation team and no capacity to build one
- Confusion around edge cases and inconsistent guidelines
- Risks of exposing sensitive data to untrusted vendors
- No stable workflow for shifting specs and evolving needs
With us, it’s under control:
- Trained annotation team with NER expertise—ready to launch fast
- Ensuring clarity, consistency, and high accuracy across tasks by team leads
- Confidentiality by default—NDA-backed, secured for enterprise data
- Proven annotation process—built to scale, designed to adapt
Get Your Custom NER Pilot
Types of Data We Work With
Not all data comes in clean, structured formats. We handle unstructured data from real-world sources — no matter the format.
We support formats such as:
- Chat transcripts
- Web-scraped content (HTML, JSON)
- OCR’d PDFs and screenshots
- Poorly formatted docs
- CSVs, JSON exports, and more
Whether it’s raw input or domain-specific content, we prepare it for reliable entity extraction at scale.
Why Leading Teams Trust Us with Complex NER Projects
The Value We Bring as a Human Annotation Partner for Named Entity Recognition
Ready for accurate, reliable entity recognition? Let’s connect!
Request a quote
Software & Methodology
Full-stack NER annotation service, fully compatible with top NER tools like Label Studio and CVAT.
Label Studio
Label Studio is a flexible, open-source annotation platform with strong support for Named Entity Recognition (NER). It’s ideal for custom workflows and secure, self-hosted deployments.
CVAT
CVAT is a powerful annotation tool built for scalability and team collaboration. Though known for visual data, it offers full support for NER and text-based annotation via task extensions.
Use-cases
Legal Tech
NER models identify custom entity types like medications, diagnoses, and treatments in unstructured texts—supporting research, compliance, and NLP applications.
Healthcare & Pharma
NER models extract custom entity types like medications, diagnoses, and treatments in unstructured texts—supporting research, compliance, and NLP applications by identifying key medical concepts.
Finance
Institutions apply NER systems to extract company names, people, and values from filings and earnings calls—improving information extraction through entity linking between sources.
Customer Support
Support teams use NER to extract key terms and tag people, product issues, and specific categories — powering faster routing, text classification, and insights.
E-commerce
Retailers extract product names and brands from listings and reviews. NER categorizes key information to boost search, organize results, and train custom models.
Media Monitoring
PR teams run entity extraction on news and social media to track named entities like locations, people, and companies—fueling reputation tracking and early detection systems.
Government & Public Sector
Public agencies extract relevant information from documents, complaints, and reports—improving workflows with entity recognition and labelled data.
Academic & Research
Researchers build labelled training data to train models that identify specific entity types in scholarly texts—powering NER tasks across disciplines.
How It Works: Our Process
A Clear, Controlled Workflow From Brief to Delivery
Your Sentiment Annotation Questions Answered
What types of entity schemas can you support?
We support custom entity types, nested structures, and domain-specific entity classification for any NER system
How fast can you deliver training data for our model?
Most projects start within days. We deliver clean, labelled data aligned with your NER tasks and model input format.
Can we make changes to the annotation logic mid-project?
Yes. We support evolving entity recognition specs with version control and edge case traceability.
Is our unstructured data secure and under NDA?
Absolutely. All text documents and extraction tasks are processed under NDA with full encryption and access controls.
Request a Custom Pilot
Why Companies Trust Unidata’s Services for ML/AI
Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.
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Tell us what you need — we’ll reply within 24h with a free estimate
- Andrew
- Head of Client Success
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