For Forward Thinking Companies
Our data scientists are working with manufacturers over the world to improve manufacturing processes, efficiency and operating costs, leveraging our Factory.360 IIoT platform. We use advanced Machine Learning algorithms to gather and analyze data, forecast failure and predict other events and incidents.
Our secret sauce for data science success lies in the intersection of years of domain expertise, hardcore Machine Learning, Statistics and Data Processing.
Working with Hyperledger, Ethereum and Nebulas means we get the tech. And our extensive business experience helps us pick the right use cases for adoption.
Fighting the tedium of cookie-cutter replies since 2016. Our AI models are putting the 'chat' back in chatbots.
We help businesses leverage AI, preparing them for the next decade of technical innovation. We consult with stakeholders across industries and ideate, build and deploy AI-powered products.
Our blockchain engineers work with the latest protocols, and our practice heads have decades of vertical experience. Our combined know-how helps us pick out winning use cases for blockchain.
We gather machine data and generate real-time predictions, enabling manufacturers to work better and create more value. Global manufacturers leverage our IIoT platform to save time and money.
We’ve had the pleasure of working with world-class organizations. Here are some of our success stories.
One of the biggest problems recruiters face to this day is the lack of a system to find deserving candidates for a specific job. We built a Machine Learning model that matches the right candidates to the right job using not just their resumes, but social data and a number of other parameters such as GitHub activity, LinkedIn recommendations, etc. The same matching algorithm is also used to find the ideal jobs for good candidates.
A leading cloud security provider wanted us to detect anomalies and abnormal peaks in outbound traffic, using event logs. We set up a continuous feed of raw data logs, using AWS Kinesis rom AWS Cloud-Trail. Data was then grouped by Time, Usertype and Logtype into multiple batches. From these batches, variables were generated to feed in to the Machine Learning model which finally predicted if the data entry is an anomaly or not, using logistic regression.
When a world-famous manufacturer approached us with seven years’ worth of sales data and a vague need to make sense of it, we built them a platform that leverages Machine Learning to help improve sales through channel partners. Our models help them predict the probability of a quote turning into an order, and our ML algorithms make sure they never leave money on the table during bids.
Over the course of the last couple of decades, we’ve worked with businesses in various industries, and have developed expertise in a few of them.
Readmission prediction. Genealogy. EMR analytics, and more.
We build products that help retailers sell better without breaking the bank.
Fraud detection, swap analytics, customer service chatbots among other things.
From attribution to spend optimization, marketers love the AI products we’ve built for them.
We’ve always believed in giving back to the community. A lot of our projects involve R&D, and when we hit upon something that we think is a winner, we always share.
A flexible client-server crawling framework written in Go. It was written to act as a layer on top of a regular crawling library, with a lot of glue code pre-written. Apart from standard crawler features, it makes it easy to do things like scaling across machines, crawling through VPNs, using cookies, using Chrome as crawling backend, etc.
At Ideas2IT, we learn every day. From product engineering to data science, from blockchain to chatbots, our experts discuss driving forces at the intersection of tech and business. We look at business through the lens of technology, and break down how cutting-edge tech will alter the way companies in various industries operate.
Chatbots have become ubiquitous. As the technology powering chatbots matures, building generative chatbots has become increasingly simple. We explore the anatomy of a typical project, from conversation design to KRR and voice.