Cognitiva
About Cognitiva
We are a world-class AI company with core competency in machine learning and computer vision. We provide full-stack solutions to our customers. Our team of dedicated Algorithm Experts, Data Scientists, Machine Learning Specialists, Computer Vision Engineers and Technology Consultants work in tandem to create AI solutions that integrate with the technology of today to make it competitive in the markets of tomorrow.. So, if you are looking for reliable development partners - we would be happy to help you!
We are a world-class AI company with core competency in machine learning and computer vision. We provide full-stack solutions to our customers. Our team of dedicated Algorithm Experts, Data Scientists, Machine Learning Specialists, Computer Vision Engineers and Technology Consultants work in tandem to create AI solutions that integrate with the technology of today to make it competitive in the markets of tomorrow.. So, if you are looking for reliable development partners - we would be happy to help you!
Key Insights
Services & Focus
Industry Focus
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Portfolio
Gender, age, emotion detection
We've used Tensorflow and OpenCV to built this application. IMDB-2013 dataset and UTKFace dataset are used to train the custom model.This can be also used for eye color detection projects since it extracts eyes from a given image
Social distance measurement
We've built models which can measure the distance between pedestrians in public places. Using a traffic light indicator system, the algorithm is able to anonymously identify and label people who maintain safe distances, while flagging certain instances in red where social distancing measures are violated. Using this information, it is possible to identify bottlenecks where social distancing cannot be maintained, and how citizens adapt as restrictions are imposed or lifted.
ATM fraud detection
The engine was written in Python and C++.
If someone stays long in front of the ATM, the camera detects this event and creates a bookmark including timestamp, duration, etc.Then this bookmark is used to analyze the transaction. If there is no transaction, this means that he/she who stands in front of the ATM is not performing transactions. The YOLOv3 model is used to detect humans. For the camera video stream, OpenCV FFMPEG was used
Retail Traffic Analysis
We can deliver visitor analytics solutions that allow physical stores to collect and analyze in-store footfall data. We employ Computer Vision and AI to help brick-and-mortar retailers effectively manage store space and capitalize on customer insights gathered by camera-based people counting. Main features: people counting and visitor pathmaps building, visitor heatmap generation: crowd count heatmaps, dwell time heatmaps, overall traffic heatmaps, Indoor wayfinding (draw rate) assessment, queue monitoring and management, MAG (mood/age/gender) visitor profiling.
Product recognition for vending machines/smart fridges
We can build a computer vision based app for real-time detection and recognition of products from video. The solution can be used in stores to transform small self-serving shops into fully automated Amazon Go-like cashierless stores. It can help food delivery services track dispatched orders. For brick and mortar stores it can help streamline the purchasing process, reduces shoplifting, and cut costs on RFID tags
Ecommerce recommendation engine
We’ve built a custom product recommendation system for one of the largest social platform for shopping. The system allows our client to recommend the right products and increases upsell and cross-sell opportunities, without relying on generic external systems. A complex ensemble of trained ML models include collaborative filtering models, natural language processing using deep recurrent neural networks and image processing using deep convolutional neural networks.
Sales and upsell prediction modeling
In this project we analyzed customer database, purchase history, renewal history, and CLSV to find the customers that are the most likely to purchase or upgrade. This reduced churn and shortened sales funnel and time to upgrade. Also, we looked at what made customers buy in the past to repeat the same process with new customers and make existing customers buy again. Through analyzing available data, we built predictive models that help shorten the time to convert and increase conversion rates.
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