Centric Group Inc. is a global digital transformation, AI, and data services company helping enterprises unlock value from technology, data, and customer experience. Our mission is to help organizations move faster and operate smarter by combining advanced engineering, AI driven automation, and high precision data operations.
Centric provides end to end services across AI data labeling and annotation, enterprise data platforms, web and mobile application development, AI enablement, master data management, and performance driven digital marketing. Our data labeling division supports computer vision, NLP, document intelligence, speech, and multimodal datasets through managed services or dedicated workforce models.
We differentiate through enterprise consulting discipline combined with scalable execution. We use structured delivery frameworks, multi layer QA validation, domain expert review, and AI assisted pre labeling to ensure quality, speed, and consistency. Our security approach aligns with enterprise standards including secure environments, role based access, and client specific compliance controls.
Centric serves energy, oil and gas, renewable energy, manufacturing, retail, financial services, healthcare data platforms, and government programs. We operate globally with leadership in the US and Middle East and scalable workforce delivery across emerging talent markets, enabling rapid ramp up and 24 hour delivery coverage.
Centric implements enterprise grade security and privacy controls to protect client data, intellectual property, and AI training datasets across all engagements.
Our facilities use controlled physical access, 24 by 7 CCTV monitoring, secure workstation policies, and visitor access logging. Workstations used for client projects follow restricted USB usage, screen lock enforcement, and role based environment access.
Our cybersecurity program includes secure network architecture, firewall protected infrastructure, endpoint protection, encrypted data transfer, and secure VPN access for remote teams. Client data environments are logically segregated and access is granted strictly based on least privilege principles.
All employees and contractors sign confidentiality and non disclosure agreements and complete mandatory data protection and privacy training. Project teams follow strict data handling SOPs covering data storage, processing, and deletion policies aligned with client requirements.
We conduct periodic internal audits, security reviews, and compliance checks. Where required, we support client audits and security assessments. Our security framework aligns with ISO 27001 principles and industry best practices for secure AI data operations.
Security Credentials
ISO 27001
Labeling Experience
Government Workforce CV and Resume Document Annotation Program
Aws SagemakerDocumentEntity Ner ClassificationClassification
Centric supported a government workforce data initiative focused on structuring large scale CV and resume datasets to enable document intelligence and AI driven talent analytics. The project involved annotation of structured and unstructured resume data including personal identifiers, education history, certifications, employment history, skills taxonomy mapping, and job role classification.
The scope included Named Entity Recognition for key candidate attributes, document layout tagging, and text classification to support downstream AI and search optimization models. The project operated within secure data environments with strict role based access controls and government compliant data handling protocols.
Quality was maintained through multi layer QA validation, sampling audits, and domain trained reviewers to ensure consistency across resume formats and language variations. The dataset was prepared for NLP model training and document intelligence pipelines.
Centric supported a government workforce data initiative focused on structuring large scale CV and resume datasets to enable document intelligence and AI driven talent analytics. The project involved annotation of structured and unstructured resume data including personal identifiers, education history, certifications, employment history, skills taxonomy mapping, and job role classification.
The scope included Named Entity Recognition for key candidate attributes, document layout tagging, and text classification to support downstream AI and search optimization models. The project operated within secure data environments with strict role based access controls and government compliant data handling protocols.
Quality was maintained through multi layer QA validation, sampling audits, and domain trained reviewers to ensure consistency across resume formats and language variations. The dataset was prepared for NLP model training and document intelligence pipelines.
