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Wisepl Pvt Lt

Wisepl Pvt Lt

Agency
INDIA flag
Perinthalmanna, India
$8.00/hrExpert50+ISO 27001HIPPAGDPR

Key Skills

Software

AppenAppen
CVATCVAT
DataloopDataloop
HiveMindHiveMind
LabelboxLabelbox
LabelImgLabelImg
Label StudioLabel Studio
SuperAnnotateSuperAnnotate
SuperviselySupervisely
VoTT

Top Subject Matter

No subject matter listed

Top Data Types

Geospatial Tiled ImageryGeospatial Tiled Imagery
ImageImage
TextText

Top Label Types

Bounding Box
Cuboid
Point Key Point
Polygon
Segmentation

Company Overview

Wisepl Pvt Ltd is a privately-held AI data services company founded in 2020 and headquartered in Pattikkad, Malappuram, Kerala, India. It specializes in high-quality data annotation, data labeling, and dataset generation for machine learning and artificial intelligence applications across image, video, text, and audio modalities. Wisepl supports diverse industries—such as autonomous vehicles, healthcare, retail & e-commerce, agriculture & drone vision, robotics, and legal/document processing , leveraging a human-in-the-loop workforce, quality assurance processes, and secure infrastructure to ensure data confidentiality and high annotation precision. The company has completed hundreds of projects for global startups, enterprises, and research teams, emphasizing transparent pricing, domain expertise, and responsive client engagement. With a growing team and decade-plus industry experience collectively, Wisepl positions itself as a trusted data partner for organizations building intelligent systems powered by clean, labeled data.

ExpertEnglishHindiMalayalam

Security

Security Overview

Wisepl Pvt Ltd implements comprehensive physical, technical, and administrative safeguards to protect client data and project confidentiality. Physical Security: Our facilities maintain controlled access to workstations and operational areas. Only authorized personnel are permitted entry to project-specific environments. Systems are password-protected, and access is restricted based on project allocation. CCTV monitoring and supervised workspace protocols are implemented where applicable to ensure operational security. Cybersecurity Infrastructure: We operate within secure network environments protected by firewalls, endpoint protection, and updated antivirus systems. Data transfers are conducted via encrypted channels, and secure storage practices are enforced. Role-based access control (RBAC) ensures that team members only access data necessary for their assigned tasks. We also support client-required environments such as VPN access, Virtual Desktop Infrastructure (VDI), and client-hosted annotation platforms. Employee Confidentiality & Data Handling: All employees and contractors sign legally binding Non-Disclosure Agreements (NDAs). We provide internal training on data privacy, secure handling of sensitive datasets, and responsible AI data practices. Access is granted strictly on a need-to-know basis, and project data is not shared outside authorized workflows. Audits & Compliance Practices: We follow structured internal review processes to monitor adherence to security and quality standards. Operational workflows align with globally recognized data protection principles, including GDPR-aligned privacy standards where applicable. We are prepared to provide documentation and comply with additional client-specific security requirements.

Security Credentials

ISO 27001HIPPAGDPR

Labeling Experience

CVAT

Autonomous Vehicle Image & Video Annotation Project

CVATImageBounding BoxSegmentation
Led a large-scale computer vision data annotation project supporting ADAS model training for an autonomous mobility client. The project involved high-precision bounding box annotation, semantic and instance segmentation of road elements, lane markings, pedestrians, vehicles, traffic signs, and object tracking across video sequences. The dataset included over 250,000 images and 3,000+ video sequences captured across diverse environmental conditions (day/night, rain, low-light, urban and highway scenarios). Implemented a multi-layer quality assurance workflow including peer review, senior QA validation, and periodic accuracy audits to maintain >98% annotation accuracy. The team operated in structured day and night shifts to ensure continuous delivery and faster turnaround times while adhering to strict data security and NDA compliance protocols.

Led a large-scale computer vision data annotation project supporting ADAS model training for an autonomous mobility client. The project involved high-precision bounding box annotation, semantic and instance segmentation of road elements, lane markings, pedestrians, vehicles, traffic signs, and object tracking across video sequences. The dataset included over 250,000 images and 3,000+ video sequences captured across diverse environmental conditions (day/night, rain, low-light, urban and highway scenarios). Implemented a multi-layer quality assurance workflow including peer review, senior QA validation, and periodic accuracy audits to maintain >98% annotation accuracy. The team operated in structured day and night shifts to ensure continuous delivery and faster turnaround times while adhering to strict data security and NDA compliance protocols.

2023 - 2023