Ganesh Krishnan

Ganesh Krishnan

Melbourne, Victoria

AiHello helps ecommerce sellers increase their sales

Artificial Intelligence


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 :!Am-DHg55IDBMgQfKvewouOFju_ms

You can reach us at



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Main Website

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Srihari J. created project Novel approach to avoid glare while driving cars in Highways at night.

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Novel approach to avoid glare while driving cars in Highways at night.

Abstract:APT i.e "Accident Prevention Technology".All we see in the second page of any News paper is about unfortunate incidents ,The Accidents.We are coming up with amazing features for all four wheelers which on support with statistical reports, prevent 50% of the accidents. That's the minimum margin we put, as our Arduino, Ultrasonic and Servo-motor based quick responsive systems, and android map application, taking care of the obstructions caused for the drivers which leads to accidents(glare hitting the eyes when other vehicles zoom in with high beams "on" highways in night), and about the junctions, which are very difficult to spot when you are driving at 40+mph and none to warn you about the nearest intersection(our android application using G-Maps API takes care of it)!! Aren't intersections ,glare, and drowsiness main reasons for accidents?! We are taking at most care to address each and every aspect. We prevent the glare hitting the drivers eyes (completely automated systems , implementable for every lower end/economical cars),using Robotic hands, which is guided by an image processing system along with Light Detecting Resistors value. Also keep the vehicles speed in check, mostly at junctions. We will also want to take care about the "Care" part of our project. We make sure the emergency wheels reach out soon to the hospitals, using IoT and GPS system! We are automating the communication system, using the best of the resources available. We are using cost effective sensors, and connecting each and every device, bringing out a working prototype. Our total expenses comes under a thousand rupees , for the completely working and ready to implement systems. We feel this is the perfect platform to bring out our innovations and make them working models, and reach the world. We await anxiously to execute/implement our thoughts and "SAVE LIVES!!!".

Objectives: Preventing accidents and saving lives, by preventing the glare of opposite direction travelling vehicle disturb the driver. Warning about the very next intersection/junction when the drivers speed is above the limit for that specific road. Automated signal systems which create green corridor for emergency vehicles.

Outcomes: Reduction in accident rate by 50%.

The overall block diagram :

Brief description of the Methodology: a) Glare prevention system: We have used a small camera, fixed on the dashboard, which clicks the picture of the driver every second (automated) and sends the result to the servo motor via Arduino board. Using matlab , we have coded for drowsiness and glare detection. If the output gives to be drowsy, an alarm (beep) rings. If the glare is detected, servo motor is given the right value for the amount of inclination to obstruct the light from falling on drivers eyes. We have used X-ray sheet as a translucent material which obstructs the light in one way direction. The communication between different devices is automated and wireless. b) Junction alarms: If the vehicle is travelling more than prescribed speed for that highway/road ,our android application shouts/alarms that there a junction ahead of 600m and advices the driver to slow down. c) Emergency vehicle systems: Pure IoT concepts, to inform the cop about its arrival to the next signal. One application which also communicates with the signal system, hence creating green corridor and pushing the chances of saving lives.

Expected Results: The human kind is the end user! Ideas themselves call to be philanthropic. None wishes to end his/her life on roads. Everyone cannot afford for automated systems which come in Benz/BMW's. Our glare prevention technology can be installed in less than 2k rupees. The junction app is free and will always be. Ambulance system app again is free, but interacting with the government officials will do the required job for us!

Conclusion: This is an essential implementation which should not be ignored at all. Statistics says 50% of accidents happen summing up of intersections and glare. We single handedly prevent both. We await to implement it at the very best platform we have, ABB Makeathon.

Future Scope :Research and development takes things to next level. Our glare prevention system, having mechanical constraints, work in less than 1.2 seconds. Higher level of automation and accuracy can be confronted. This kind of a concept does not exist anywhere till date.

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KUSHIKA A. updated status

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Hello guys !! I am working on Lung cancer detection using CT scans of patient and using DICOM. This involves concept of machine learning and big data.

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Antonio D. updated status

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Antonio Dantas

Oi meu nome é junior tenho 16 anos e moro em são paulo capital, e eu tenho uma ideia muito boa para evolucionar o mundo dos oculos e eu atenho certeza que sera a empresa que me ajudara a desenvolver esses oculos futurista, e eu quero ter a oportunidade de mostrar esses oculos para os voceis esses oculos ainda estão no papel mas eu sei que com ajuda de voceis poderei colocar essa ideia em pratica, do mais informaçoes se voceis marcarem uma reuniao comigo pra mim apresentar minha ideia obri

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Daniel T. created project E-MOTIV

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This work presents the development of a prototype for the recognition of universal facial expressions. This technic provides an alternative way of gathering data from the user, being able for usage as an input way for information systems. For faces detection and extraction of their characteristics technics of Computer Vision and Digital Image Processing are employed, implemented by the dlib library with Intel RealSense. The classification into facial expressions is performed by an Artificial Neural Network, of the multilayer perceptron kind.

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Adam M. created project TASS PVL Computer Vision Hub

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TASS PVL Computer Vision Hub


TASS PVL is a sister project to the original TASS Hub project. As with TASS Hub, TASS PVL is a local server which homes an IoT connected A.I. powered by the Intel® Computer Vision SDK Beta. The hub can connect to multiple IP cameras and two Realsense cameras. First, the program detects if there is a face, or faces, present in the frames, and if so passes the frames through the trained model to determine whether the face is a known person or an intruder. In the event of a known person or intruder, the server communicates with the IoT JumpWay which executes the relevant commands that set by rules, for instance, controlling other devices on the network or raising alarms in applications etc.


TASS PVL uses the following Intel technologies:

  • Intel® Core i7 NUC
  • Intel® Computer Vision SDK Beta
  • Intel® Realsense (R200,F200)


The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.


TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in the sister project TASS PVL, follow this link.


The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

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