AiHello

AiHello

AiHello helps ecommerce sellers increase their sales

Artificial Intelligence

Description

AiHello uses Machine Learning to analyze existing inventory,past product sales and competitor information to help sellers optimize their inventory for better sales by geography.

Online ecommerce is $2 trillion dollars industry and growing in double digits but it is incredibly fragmented - There are 12 million ecommerce sites globally including Amazon, Ebay, FlipKart, Independent Shopify & Woocommerce sites etc. An online retailer faces multiple challenges and decisions starting up and most of the decisions made are either by guessing or gut feeling. The retailer also spends huge amount of money trying to market his products online & price his product correctly.

Inventory overhead costs are roughly 25% of the product while marketing especially for new sellers far exceeds the cost of the product.

Online e-commerce, in spite of being a trillion dollar industry, is just 6% of global retail industry and these problems will be magnified with the passage of time as ecommerce catches up with retail.

Our technology addresses these problems by 1) Inventory management AiHello manages your inventory across channels. The system can be used to list a product on any one channel or a combination of channels. A retailer selling on one channel can push his products to other channels in a click.

2) Optimizing inventory: AiHello uses deep learning to optimize your inventory by geography. Using its pre-existing knowledge of similar products combined with current inventory & sales , AiHello can optimize the price of a product depending on the channel and location of selling. The system will also suggest the best channel and the geo-location to target your product for maximum success

There is efficiency in learning when humans are not involved. The whole process is extremely cheap and the system learns and becomes smarter as time progresses.

We help to keep the startup business costs low, streamlined and extremely efficient.

Our pitch deck is at : https://1drv.ms/p/s!Am-DHg55IDBMgQfKvewouOFju_ms

You can reach us at https://www.aihello.com

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Aravindhan N. created project Automatic attendance management system using face detection

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Automatic attendance management system using face detection

Automatic attendance management system will replace the manual method, which takes a lot of time and is difficult to maintain.There are many bio metric processes ,in that face recognition is the best method. In our campus staff attendance is taken with the help of Gesture recognition /attendance sheet .We can take this to next level by implementing Artificial Intelligence based Face Recognition using Convolution Neural Network(CNN). We have to train our neural net using COCO (large Image dataset designed for object detection) and Staff Dataset (Several images of individual staffs). Since we don't have the photos of the staffs,we have trained our neural net using our own photos.Our Neural net consists of 20 neurons in the hidden layer which help us to diagnose the pixels of the image and compares the result with the trained dataset .By using our advanced system the staffs can use their own mobile/laptop [camera] for registering their presence in their own place which is possible only if they are connected to our college Network (WiFi).

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Melisa M. updated status

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melisa Mendoza

I’m a Technical Collaborator at the Innovation Lab Network at my University, the Technical University of Queretaro (UTEQ). Here I work with a group of students and teachers to create solutions for real world problems by joining hardware and software technologies. We use databases like Mongo, Maria and SQL. I believe that the junction between Information technologies and hardware will create amazing solutions for the industry and my community, with the use of IoT.

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