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Nagpur, India
CVAT
LabelImg
Roboflow
Supervisely
Labelbox
V7 Labs
AppenNo subject matter listed
Image
Text
3D Sensor
Bounding Box
Segmentation
Classification
Cuboid
RLHFAKAMOS (OPC) Private Limited is an India-based AI data annotation and platform engineering company delivering high-quality training data solutions for machine learning, computer vision, NLP, speech AI, and autonomous systems. Founded in 2024, AKAMOS specializes in end-to-end data annotation services across multiple modalities including text, image, video, audio, 3D LiDAR point clouds, and geospatial datasets. We help AI companies, startups, enterprises, and research organizations transform raw data into accurate, production-ready datasets for model training and validation. Our services include: • Text Annotation (Named Entity Recognition, Sentiment Analysis, Intent Classification, Semantic Annotation, Content Categorization) • Image Annotation (Bounding Boxes, Polygon Annotation, Semantic Segmentation, Instance Segmentation, Keypoint Annotation, Image Classification) • Video Annotation (Object Tracking, Frame-by-Frame Labeling, Action Recognition, Event Annotation) • Audio Annotation (Speech Transcription, Speaker Identification, Emotion Annotation, Sound Event Detection, Phonetic Labeling) • 3D Point Cloud & LiDAR Annotation (Cuboids, Semantic Segmentation, Object Classification, Temporal Tracking, QA Validation) • Geospatial & Drone Data Annotation • Medical Data Annotation • E-commerce Data Labeling and Categorization AKAMOS follows structured quality assurance workflows, secure data handling practices, and scalable delivery processes to ensure accuracy, consistency, and fast turnaround times. Our team supports custom annotation guidelines, multi-pass QA reviews, and project-specific taxonomy requirements. The company is recognized under Startup India (DPIIT), registered as an MSME, and operates from Maharashtra, India. Our mission is to accelerate AI innovation by providing reliable, scalable, and cost-effective data annotation services for organizations worldwide.
AKAMOS follows strict data security and confidentiality practices to protect client data and intellectual property. Access to project data is restricted on a need-to-know basis and governed through role-based permissions. Team members and freelancers working on client projects are required to comply with confidentiality requirements and project-specific guidelines. Project files are stored using secure cloud-based systems with controlled access, password protection, and regular backups. Client data is never shared with unauthorized parties. AKAMOS follows secure workflow management, quality assurance procedures, and data handling protocols to maintain accuracy, privacy, and compliance throughout the annotation lifecycle. As the company grows, AKAMOS is committed to adopting industry-standard security frameworks and continuously improving its security and privacy controls.
Supervisely3D SensorCuboidClassificationPerformed 3D point cloud annotation using cuboid labeling, object classification, segmentation, and spatial data validation techniques. Supported autonomous vehicle and robotics datasets through structured annotation workflows, object tracking, and multi-layer quality assurance processes.
SuperviselyImageSegmentation
PolygonCreated pixel-level annotations for objects, environments, and scene elements used in robotics and computer vision applications. Conducted segmentation review, annotation refinement, and quality assurance checks to ensure dataset consistency and model training readiness.
AppenTextClassification
Question AnsweringAnnotated and classified multilingual datasets for natural language processing and generative AI applications. Tasks included intent classification, sentiment analysis, content categorization, relevance assessment, data quality review, and AI evaluation workflows.
AppenTextRLHF
Evaluation/RatingContributed to AI training and evaluation projects involving prompt-response assessment, response ranking, content quality evaluation, instruction-following analysis, and reinforcement learning from human feedback (RLHF). Supported large language model improvement through structured evaluation workflows and quality-focused review processes.
CVAT
Video
Object DetectionBounding BoxAnnotated video datasets involving object tracking, movement analysis, and event recognition across sequential frames. Conducted frame-by-frame annotation, consistency validation, and quality review to support machine learning models for video understanding and surveillance applications.