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6 to 8 days
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Python is one of the most prevalent technologies that we harnessed quite well. We have dealt with various projects where we have integrated Python in the best way. 

One of the projects was associated with the development of a tool that was capable of incorporating textual similarity algorithms to short texts and making that tool as accessible to fellow researchers as possible. The tool was almost prepared but it needed a lot of alterations and efficiency. Some tools were required to be re-implemented without changing the current code. The primary application code is written in Python 3.6 with use of the SciPy stack, NLTK, and AioHTTP; the database code is written in PostgreSQL; the web frontend uses bootstrap, jquery, and HTML; deployment of code has been managed through Docker; and Redis, NGINX, and R are also used.


The client was looking to put together a demo of a product. It would be a camera at a cash register that could identify people as they walked into a cafe/restaurant. The camera could identify people as they stood in a line at the register. The camera would then report to a tablet. 

  • We built a “people counter” with OpenCV and Python
  • Using OpenCV, we counted the number of people who are heading “in” or “out” of a department store in real-time.
  • We classified images with deep learning and OpenCV 3.3’s deep neural network module.
  • Object detection was programmed using deep learning and Single Shot Detectors and MobileNets.
  • We combined both the MobileNet architecture and the Single Shot Detector (SSD) framework, to create an efficient deep learning-based method to process the object detection.

In another project, a client had WhatsApp APIs to send and receive messages by using those APIs. They reached out to build an integration with Stack where at the end they would be able to send and receive WhatsApp messages with the Slack Interface

The client wanted us to:

  • Build a lambda function (X) where (if it is triggered) it sends a slack message to a specific channel.
  • Make Lambda trigger with complete details in case the Slack users replied back to the messages. 

Our Expertise 

  • Solid experience in integrating with Slack RTM. and Massaging events.
  • Expertise in Python programming.
  • Skills and prior experiences with AWS lambda and cloud formation scripting.

Our Approach 

  1. By default we added all the members on the slack channel
  2. For every new communication, we created a new channel
  3. We made every user to reply to one particular channel

Our Process

Our objective was to prioritize our clients’ complete satisfaction and we don’t rest until we achieve that. Project understanding comes right after that on which our first objective solely depends on. Following Agile methodology, we welcome changes and try to reflect that successfully on the SDLC and SMLC and well-designed version controls.

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