Power your Retail and E-commerce solutions with the highest quality training data and accelerate ML developments.
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Developing Retail AI solutions heavily relies on machine learning to achieve the best outcome and outdo the competitors. The computer vision system of all Retail and E-commerce has to be trained and tuned with a large amount of structured, annotated & labeled data. We at Dodeed AI end-to-end data labeling services paired with full-time data annotation experts deliver high-quality, error-free, human-labeled, and cost-effective AI training data for Retail and E-commerce AI solutions..
Product relevance enhances the shopping experience by ensuring AI algorithms match user queries with relevant items.
Through precise data annotation, Dodeed AI boosts your AI product development to optimize search relevance, reduce product discovery time, and improve sales performance.
We leverage precise product annotation to analyze stock trends and product demand, facilitating timely restocking and minimizing excess inventory.
This approach enhances your team's ability to focus on strategic initiatives; and boosts operational efficiency and customer satisfaction.
Through precise data labeling, Computer vision can provide traffic, and behavior analytics using CCTV footage in shops, allowing AI systems to detect suspicious activities.
This proactive method reduces theft and ensures a safer shopping experience, empowering retailers to address potential threats and enhance overall security measures swiftly.
Self-checkout systems enhance retail efficiency but face challenges like item scanning accuracy and theft prevention.
Dodeed AI specializes in precise data annotation, ensuring high-quality labeled images for accurate item identification and pricing to improve customer experiences that will generate more sales using fewer resources.
Augmented Reality (AR) and Virtual Reality (VR) staging of products allows customers to visualize items in real-world settings before making a purchase. Our data labeling services accurately annotate products for AR/VR applications, enabling more immersive and interactive customer experiences.
This helps retailers enhance product engagement, reduce returns, and boost conversion rates by offering a virtual try-before-you-buy experience.
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 exceptional image annotation, labeling, and NLP expertise to create AI-driven automation tools and predictive analytics for demand planning, inventory management, order processing, and self-checkout in retail and e-commerce.
For retail and e-commerce datasets, accurate labeling is vital for detecting and understanding complex objects like products, shelves, and aisles. Our expertise in polygons, instance, and semantic segmentation ensures precise labeling of complex retail objects, enhancing the accuracy and reliability of AI models for tasks like product recognition and shelf analysis.
This meticulous approach contributes directly to the success of our clients' AI models, helping them achieve superior performance and customer insights.
Bounding box and object tracking techniques are essential for in-store analytics, helping identify customer movement patterns, product interactions, and store traffic.
With accurate labeling, we enhance the AI models that power heatmaps, optimize store layouts, and improve customer experience, ultimately driving data-driven decision-making for our clients.
OCR annotation helps extract and label text data from images, receipts, and product labels, providing structured information for retail AI solutions.
By accurately tagging text, we support clients in improving data accessibility, automating inventory updates, and enhancing personalized customer experiences.
NLP annotation In Retail & E-commerce plays a vital role in understanding customer sentiment by labeling textual data such as reviews and feedback. This helps retail AI models understand customer emotions, preferences, and trends, enabling businesses to enhance customer experiences, optimize marketing strategies, and boost satisfaction.
Our expertise in data labeling ensures accurate and insightful sentiment analysis for your AI solutions.
Landmarking, keypoint, and dot annotation techniques enable AI models to recognize and analyze items accurately by precisely labeling key features on products or images. This empowers retailers to improve search functionalities, enable virtual try-ons, and streamline inventory management, ultimately enhancing the shopping experience for customers.
Our expertise in data labeling ensures that your AI models achieve optimal performance, driving better customer engagement and satisfaction.
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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