Text Document Annotation Using NER and Segmentation
Client
Our client is a leading AI solutions provider specializing in the automated analysis of legal and financial documents.
Client Goals
Train an AI model for Named Entity Recognition (NER)
Create a dataset for automatic contract classification
Develop structured annotation with text segmentation
Company Profile
The client specializes in AI-driven automation for legal and financial document analysis. Their solutions help businesses process large volumes of contracts with greater speed and accuracy, reducing manual review efforts.
Challenges
High Accuracy Requirements
≥98% precision in text annotation.
Strict Data Security Measures
Handling confidential contracts under SOC 2 & GDPR compliance.
Tight Deadline
15,000 documents had to be fully annotated in 3 months.
Solutions
We structured the project into 5 phases to ensure high-quality, precise medical image annotation while maintaining data security and efficiency. Each phase addressed specific challenges and progressively refined the annotation process for optimal accuracy.
Refining Annotation Guidelines
Standardized entity categories: ORG, PER, DATE, MONEY, CONTRACT_TYPE.
Added real document examples for clarity.
Structured annotations in JSON for seamless AI training.
Secure Annotation Environment
Used Prodigy (on-premise) to ensure SOC 2 & GDPR compliance.
Implemented role-based access control and automated updates.
Iterative Quality Calibration
Pilot 1: 150 documents → Error analysis.
Pilot 2: 600 documents → Consistency check.
Pilot 3: 1,500 documents → Achieved <2% annotation errors.
Full-Scale Annotation & Multi-Level QA
15,000 documents labeled in 2 months.
4-tier QA process:
✅ Self-check: Annotators reviewed 5% of their own work.
✅ Cross-check: Peers validated 5% of annotations.
✅ Internal QA: 20% manual verification by specialists.
✅ Final Audit: 10% dataset validation before submission.
Results
Our data labeling services successfully addressed the client's challenges and delivered exceptional results:
Project completed ahead of schedule
15,000 documents in 2.5 months.
Final annotation accuracy
98.00%, exceeding industry benchmarks
Zero security breaches
Fully SOC 2 & GDPR compliant.
Optimized workflow
Standardized annotation reduced errors by 87%
Faster AI deployment
Client accelerated AI model training by 3 months.
We partnered with UTOR AI to improve how we process and analyze documents. They helped us implement NER and segmentation, and it’s been a game-changer. The solution accurately pulled out important details like names, dates, and locations, and made complex documents so much easier to work with.Thanks to them, we’ve saved tons of time and can now handle a lot more data without losing accuracy.
Data Operations Manager
Get in touch
To schedule a call with our team, simply complete the form and click “Submit.” We’ll arrange a meeting at the earliest convenient time for you.
Serhiy Smaglyuk, Founder of UTOR
What You Can Expect on the Consultation:
We’ll take the time to understand your unique project needs and challenges.
You’ll get a realistic estimate of how long your project will take.
We’ll provide clear pricing information based on your project’s scope and complexity.
You’ll get a realistic estimate of how long your project will take.
There’s no obligation to move forward. If you're not sure, we won’t rush or push you into any decisions.