MoogleLabs

Earlier, companies were trying to save time, cost, and simpler infrastructure of their business workflows. With the help of the deployment of AWS within our solution, we've cut down the time that was needed to build a new application structure by around 99%. Our Mooglelabs team considers the building blocks to bring out the actual intent as we'd drive instantaneous virtual infrastructure with the AWS.

Creating unique solutions that far exceed the scalability and viability of traditional systems is now a reality thanks to the advancements in technologies like Artificial Intelligence and Machine Learning. One of the latest applications of the two technologies that our company was able to create includes the Face Recognition Attendance System. Companies can use the system to have accurate identification for attendance or security access. Read more..

With most of the shopping activities moving online, the skincare industry is looking for ways to replicate the in-person shopping and suggestion system to garner people's trust and make appropriate recommendations that yield results for customer retention. We at MoogleLabs took on the challenge and created a system that can do exactly this through Artificial Intelligence and Machine Learning. To ensure that we were using the most credible sources, we took a significant chuck of initial data from Sephora and other resources to understand customers' needs as per the various demographic factors like skin texture, problems, etc. It helped us understand people's sentiments. Watch the video to understand the process we used to create the skincare recommendation system. As direct product recommendations can be too on the nose for some customers, our system makes ingredient recommendations for the clients. Then, they can choose the product they want to purchase with the appropriate active ingredient. Moreover, the current system is dynamic and flexible. So, it can incorporate new information to upgrade recommendations as further information gets fed into the system. The project offers scalability and can be changed to meet specific businesses' desires for specific products. Moreover, the same system can be applied to other industries to make further recommendations, like using the same procedure to make book recommendations per each individual's previous preferences and purchases Read more..

Our client is an educational website owner who runs a business named trans neuron. He offers skills development courses to working professionals and students.Our client is an educational website owner who runs a business named trans neuron. He offers skills development courses to working professionals and students.To create an ML model that offers relevant suggestions based on students' and working professionals' requirements for courses and jobs, respectively.Read more..

To begin, we needed to create a system that could fetch feed from the camera.After appropriate Research and Development, we opted for the OpenCV HAAR CASCADE algorithm as the means to detect faces.Additionally, the detection system is aligned with the task of recognizing the subscriber through image analysis to enable access to the web application.Lastly, we custom-trained our model with CNN of LSTM to classify Yoga Poses.Read more

The automated screen damage detection system was trained on multiple different brands of smart phones. Hence, as an adjunct information, the algorithm also identifies the Make and Model of the phone from its image. This helps in categorizing the device for further workflows and also in assessing the repair costs for the damage detected.The model has been trained to detect and grade screen damage across various mobile phone models and screen types. Thereby providing efficiencies of scale and higher throughput from the assessment stations. The model also provides an automatic assessment of the extent of the screen damage as a function of the screen size and phone type.Read More

The Automated approach reduces the manual effort required To generate, Validate And Check The Product Descriptions. Moreover, It Ensures That The Resulting Copy Doesn’t Have Any Grammatical Or Spelling Errors. A Trained NLP Model Generates the Descriptions Using Only the Product Images As Inputs.The idea was to be able to generate copies, track the resultant customer engagement with the product and continuously improve the copy to create higher interaction and sales. All without having to train human writers in the company’s preferred style of writing the descriptions.This ensures that all product-related copies on the site are generated automatically. Consequently, it helps increase the speed with which the client can refresh his online inventory. Read More