Artificial Intelligence

We provide high quality and accurate AI solutions to build cost effective digital products.

Progressive Innovation Through Artificial Intelligence (AI)

Artificial Intelligence is increasing in demand at a lightning pace. The amount of data that is generated today, outperforms the human’s ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence is the basis for all computer learning and it is also referred to as the future of all complex decision making.

Machine Learning is an application of Artificial Intelligence. ML teaches a machine a way to inferences and decisions set on past experience. It helps to identify patterns, analyses past data to conclude the meaning of these data points.Computer vision algorithms try to understand an image by breaking down an image and studying different parts of the objects. This helps the machine to detect and study from a set of images, to make a better output decision.

As pioneers in AI service providers, we help other businesses to build innovative AI solutions. We provide ML Image processing services to our business clients and partners.


Advanced image detection technology is designed in such a way that it successfully identifies the objects with its location even if it has a complex background and complex patterns. Our researches and implementation strategy ensure good accuracy for image processing.

Case study:

Client Location : Japan

Client Requirements:
  1. A retail business company wants to find out the current market price of the products which they sell, to fix a competitive pricing for their product. This market study is a regular activity of their business, depending on their business, this activity can be either daily/weekly/bi-weekly basis.
  2. A manufacturing company plans to add a new item into their array of products. As a part of initial feasibility study, they want to identify the price of similar products in the market. This will help to identify whether they want to do a make or buy decision, depending on the cost of producing that item.
  3. In a factory, finished items are sent to the Quality Monitoring department for quality checking. These items are printed with that product code symbol stamp which is either one or more various shape symbols with its own color. These items are then sent to the Package and Delivery department for shipment to the customer site.The current problem is, items without or partially printed product code symbol stamps are getting delivered to the customer site, which they cannot use.
  4. An e-catalog publishing company wants to regularly modify its customer’s product catalog by updating the contents such as price, offers and discounts of various items, order in which items are listed in the catalog page etc. The turnaround time is more for manually implementing the changes which is the difficulty to handle additional volumes.
Solution we provided:
  1. Used Machine Learning to build a model based on individual product images, which can be used to identify the products along with its price of other competitors in the business. This will help them to do a comparative study and fix a reasonable price for their product
  2. With ML we build a model on individual products. This can be used to identify the products along with its price of other competitors in the business.
  3. Implementing a machine learning model based on individual product symbol images, which used to automate the verification of the product code symbol stamp on the product. Those products which are passed the symbol verification checking will be pasted with a ‘Verified’ sticker and will be moved into the packing section.
  4. Used powerful object detection and localization architecture in Machine Learning to orchestrate a best solution. Individual product images with its metadata such as product name and bounding box coordinates are fed into a neural network to train and build a machine learning model. When we provide an image of a catalogue page into the model, it identifies the product, and reads its textual information such as description, price and specification etc with its location and size in the catalogue image. These identified details with its location are extracted out from the page and saved into the database, can be modified its contents and location, and re-populate these as a new catalogue page.
Technologies used:

The technology we use here is a Deep Learning algorithm called Convolutional Neural Network or CNN, is a very powerful and efficient neural network model which is specially designed to process images by performing automatic feature extraction.