Gain a competitive edge with data-driven insights through high-quality annotation. We label key moments, player actions, and object tracking to provide real-time analysis and performance breakdowns
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Harness the power of AI to revolutionize gaming experiences and sports analytics. From tracking player movements and performance to enhancing gameplay mechanics, precise data annotations enable smarter AI systems that drive innovation and enhance user engagement across both industries.
Utilizing advanced data annotation techniques, player tracking provides real-time insights into athletes' movements and performance metrics.
By analyzing position data, coaches can tailor training programs to individual needs, enhancing overall team performance and strategic decision-making.
Pose estimation leverages AI to analyze athletes' postures during training and competition.
This data-driven approach enables coaches to provide targeted feedback, ensuring athletes maintain optimal form and reduce the risk of injury.
Crowd monitoring systems use data annotations to analyze audience behavior and density during events.
Effective monitoring not only enhances safety but also improves the overall spectator experience, ensuring timely responses to crowd dynamics.
Fitness analysis tools utilize data annotation to track athletes physical metrics and performance over time.
These insights enable personalized training programs, empowering athletes to achieve their peak performance efficiently.
Advertisement monitoring involves annotating media content to assess the effectiveness of promotional campaigns in sports.
By analyzing viewer engagement and ad placements, brands can optimize their marketing strategies and maximize return on investment.
We adapt quickly to your evolving project needs. Whether you're launching a proof of concept or scaling to millions of annotations, our agile workflow ensures speed, flexibility, and consistent quality — without the bottlenecks
Your data is your asset — and we treat it that way. We follow strict data privacy protocols, secure infrastructure practices, and industry-grade compliance standards to ensure your information stays protected at every step.
From emerging startups to global enterprises, we offer scalable pricing models that align with your budget and goals. You get top-tier annotation quality — without the enterprise-only price tag.
Leverage our advanced image annotation, labeling, and NLP expertise to develop AI-powered surveillance tools and real-time analytics for threat detection, crowd monitoring, facial recognition, and behavior analysis in security and surveillance.
Bounding box annotation is our go-to technique for tracking players during matches.
By drawing rectangles around athletes in video footage, we help analytics platforms monitor player movements and interactions, enabling teams to strategize more effectively and gain insights into their gameplay.
Semantic segmentation is key to understanding the complex dynamics of crowded sporting events. We label each pixel in images, distinguishing between athletes, spectators, and staff.
This approach enhances crowd monitoring, enabling event organizers to maintain safety and provide a seamless experience for attendees.
Our pose estimation annotation captures the intricacies of athletic movements by identifying key body points. This is particularly useful in fitness analysis, where we empower coaches with precise data on posture and technique.
This detailed feedback allows athletes to refine their performance and minimize the risk of injury.
Utilizing OCR annotation, Dodeed AI convert visual text from images and videos into digital data.
In sports broadcasting, this allows us to capture essential information such as scores and player statistics in real time. By providing instant updates, we enhance viewer engagement and keep fans informed throughout events.
Our 3D/Lidar annotation is essential for creating realistic simulation training environments in gaming and sports. By capturing and labeling high-precision spatial data, we enable developers to build immersive scenarios that closely mimic real-world conditions.
This technology enhances training programs for athletes and teams, allowing them to practice in virtual settings that accurately reflect various situations, improving decision-making skills and tactical awareness.
Our efficient data annotation process guarantees quality at every stage. We prepare and clean datasets, apply precise labeling through skilled annotators, and conduct thorough quality checks. Finally, we deliver annotated datasets ready for AI model training and deployment.
Requirement Analysis is a crucial step where we collaborate closely with clients to fully understand their project goals and annotation needs. This phase allows us to define the types of annotations (e.g., bounding boxes, polygons, 3D cuboids) and quality benchmarks that align with their AI model objectives. We assess the data, develop detailed guidelines, and establish clear workflows to ensure that our annotations meet the highest standards.
By conducting thorough requirement analysis, we deliver tailored, accurate data labeling solutions that accelerate AI training and deployment.
In the Annotation Process, we leverage our expert annotators and advanced tools to label data with precision and accuracy. Depending on the project requirements, we apply various annotation techniques such as bounding boxes, semantic segmentation, or 3D cuboids. Our team follows the established guidelines to ensure consistency across all annotations, while adhering to quality standards.
Throughout the process, we maintain a seamless workflow, ensuring that the labeled data is ready for immediate use in AI model training and development.
In the Quality Assurance phase, we conduct thorough evaluations to guarantee the accuracy and uniformity of annotations. Our team uses both manual inspections and automated tools to catch any errors or inconsistencies. By implementing multi-layered reviews, we uphold stringent quality standards, ensuring all annotations align with project specifications.
This meticulous process is vital for delivering high-quality data that enhances the effectiveness and reliability of AI model training.
The Feedback Loop is an integral part of our process, where we continuously refine and improve annotations based on client feedback and quality assessments. By analyzing the results of quality checks and incorporating insights from clients, we adjust guidelines and workflows to enhance accuracy and efficiency.
This iterative approach allows us to respond quickly to evolving project needs, ensuring the final annotated data consistently meets the highest standards for AI model development.
In the Finalization and Delivery stage, we ensure all annotations are polished and meet the agreed-upon standards. After passing thorough quality checks and revisions, the annotated data is formatted and packaged according to client specifications. We prioritize timely delivery, providing the final dataset ready for immediate use in AI model training and deployment.
This phase ensures the project is completed efficiently, with accurate and high-quality data that drives optimal AI performance.
Our clients share how Intellisane AI’s precise and reliable annotation services boosted their AI projects, showcasing our commitment to quality and trust.
Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.S. RagavanSr. ML Engineer
Our fashion AI model required pixel-level segmentation across 72 garment categories—and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.Valerio ColamatteoSr. AI Scientist & Team Lead
For our ADAS project, Intellisane AI delivered precise vehicle annotations across diverse traffic scenes, including multiple object classes and occlusion scenarios. Their expertise in automotive data workflows and quality-first mindset helped us pass all validation checks, with timely delivery and professional communication throughout.Raphael LopezCo-Founder & CTO
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