Artificial Intelligence, Simplified
Developing cognitive products that leverage the recent advancements in Artificial Intelligence can be daunting. We enable startups and enterprises to harness the power of AI and deliver innovative products in an effective manner.
We are a Gold partner on the Microsoft partner nertwork wit specialisation on Data Analytics and AI. We can help you design and build your future products with the best AI technologies. Our certified Data Scientists will help you all the way, at every step and at every stage of your growth.
Machine Learning, Computer Vision, Natural Language Processing, Data Science and Analytics are the areas of our expertise. We look forward to partnering with you and enabling your business to evolve using AI.
- Machine Learning
- Data Science
- Computer Vision
- Deep Learning
- Predictive Analytics
- Business Forecasting and Analytics
- Business Intelligence
- Chatbots
- Natural Language Processing
- Tabular Data Analytics
- OCR
- Time Series Data

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other locations
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Portfolio
Thrift+ , Dueelli , Floord, Qumec, Zetwerk , Neptune Robotics , Intuision , Indriya , WinSystems

Traffic Video Analytics
In this demo we showcase the low latency vehicle counting use case for traffic analysis and has the following key features
* Vehicle make and model recognition
* Vehicle type classification
* Traffic pattern analysis
The customer introduced themselves with an interesting problem of analysing CCTV video recordings of traffic videos on roadways. They wanted to leverage the CCTV video recordings and generate insightful analytics about the traffic on residential and commercial properties. The challenge was to generate the results of the video analytics in a time-efficient and a power-efficient manner.
We developed a Deep Neural Network model to recognise six categories of vehicles namely cars, motorcycles, buses, trucks, bicycles and pedestrians. We first collected the CCTV footage and generated a customised data set containing 2000 images for each class of vehicle. We then trained a Deep Neural Network to classify each class of vehicle and localize them on a video frame. Since this was a video processing application, performance was a key metric in addition to the accuracy of the vehicle finder.

Machine vision inspection for heavy industry
Manufacturing industries continuously monitor their products and artifacts for their precision and quality. It is imperative that they detect defects during their manufacturing process as this eliminates wastage in terms of raw material and the time to produce the same. It also paves way for good quality control during the production process. The client had a heavy industry manufacturing plant and they wanted to improve their manufacturing process by adding a customised machine vision system. They wanted us to build a system that can measure the dimensions and detect defects on the manufactured artifacts. In order to achieve this, we were required to study the manufacturing process and product based on their metrics of manufacturing, namely length, thickness, surface texture etc.
The key goals of the project are to identify the object of interest on the camera feed and to measure the object. Since the object of interest is not a standard shape, conventional shape recognition methods like Hough Transforms are not suitable for object detection. So we started the data acquisition phase of the project by collecting the video recording from the camera feeds and trim the video segments pertaining to the object of interest. This was then followed by an object annotation phase with the CVAT tool to generate a segmentation dataset of 5000 images. We implemented a neural network training process derived from the CNN architectures including the Xception, Inception and ResNet. From our RoC studies, it was observed that the ResNet model was performing adequately in terms of both accuracy and execution time. We then ported the ResNet inference program to an edge computing platform that interfaces along with the existing camera module. The edge computing platform read the camera feed and performed real time object measurement and reported the measurements over a network connection

OCR on expense receipts
Accounting is an integral part of business finance management. Keeping track of manual bills is an exhausting task and errors are likely to be introduced while handling large numbers of the same. Our client is an accounting company who wanted to automate some parts of their bills reconciliation process. Automation will help them perform the accounting quicker with less or no errors. The customers of our client will submit the manual bills for the accounting. The client wanted to build an OCR system to convert expense receipt stubs stored as scanned documents and images. In order to achieve this, we were requirement to extract elements and fields from the expense receipt stub, namely, date, total price, tax etc

Pedestrian Tracking and Counting
In this demo, we identity individual pedestrians as seen on a video footage and track the movements of the pedestrian
* Pedestrian tracking
* Pedestrian counting
* Safe distance validation

Computer Vision Android Demo
This Android application showcases the features of the Computer Vision library developed at Cognitive Machines. The features include
- Pre processing filters
- Shape finders
- Feature detectors

Custome Object Recognition for Lumberyards
The customer approached us in search of an efficient way to manage their raw timber warehouse. The existing system involved tedious manual counting of the multi-sized logs which exacerbated the painful process of maintaining daily records. The challenge here was to design and develop an effective system to automate the counting with accuracy and correctness.
We came up with a Computer Vision solution that can automatically count timber logs in a single image and generate a report on the inventory. First, we developed a Machine Learning system for training of timber logs based on common properties like shape, colour, size etc. The trained model was then deployed on a mobile application that allowed the warehouse inspector to take a picture of the timber pile and generate a report on the number of logs, size of the cluster etc
Reviews
the project
Android Dev Services for Facial Recognition App
"Cognitive Machines is professional and reliable, and they're specialists in their segments."
the reviewer
the review
The client submitted this review online.
Please describe your company and your position there.
I am a Co-Founder, Dueelli has been created to solve global problems faced by small optical segment companies that are generally most affected by problems of imprecise measurements in the manufacture of eye lenses.
For what projects/services did your company hire Cognitive Machines?
For a android APP development.
What were your goals for this project?
To implement a efficient and reliable facial recognition system embedded to generate a affordable final product.
How did you select this vendor?
We search for a specialist on facial recognition and a reliable company.
Describe the project in detail.
we started with several meetings in relation to the project and determined which would be the path to be followed, which would be or the schedule applied, many calibration tools were applied to be made or developed for applications related to applications some models were elaborated and tested in the field for when it came to MVP.
What was the team composition?
We only spoke with Cognitive Machines project manager, but we knew that was some developers working in background.
Can you share any outcomes from the project that demonstrate progress or success?
https://www.youtube.com/watch?v=khT8-dBGRko&t=8s
How effective was the workflow between your team and theirs?
Great.
What did you find most impressive about this company?
Cognitive Machines is professional and reliable, and they're specialists in their segments.
Are there any areas for improvement?
No.
Cognitive Machines followed a great workflow, and they're professionals in their field.