Our Success Stories

SiliconMint’s portfolio mostly consists of large and complex solutions developed for Fortune 500 enterprises and promising startups. Below is a highlight of the clients we’ve worked with and the challenges we’ve addressed.

IoT cloud project for Verizon Wireless

IoT Cloud for Verizon

With over 140 million subscribers, Verizon was faced with three challenges in bringing their consumer IoT platform to market:

- How to create a cloud system that would scale to the size of their user base
- How to interoperate with their existing network and broadband technologies
- How to build a system that would be flexible enough to deploy consumer IoT services defined in the future

To overcome this challenge, Verizon’s IoT service was built on SiliconMint’s stream processing platform, helping it scale to tens of millions of homes.

AI Development for Yahoo

Artificial Intelligence for Yahoo

Faced with increasing demands on categorizing and classifying the content of ads in real-time, Yahoo Japan faced a unique challenge:

- How can the tasks be done in near real-time without increasing the size of their moderation workforce?
- How can the system guarantee both accuracy and precision of the resulting work?
- How can new employees be onboarded quickly, across a diverse range of skill sets and backgrounds?

A system that combined artificial intelligence with the power of the human mind was built, such that Yahoo Japan could categorize and classify information without increasing the size of their workforce.

iterative voice app generator

Iterative Voice Application Generator for Government Research Agency

A leading team of research scientists created a voice recognition engine that rivaled the accuracy of Apple's Siri technology. Their next challenge was to bring the solution to market. They needed:

- A rapid way to generate a myriad of mobile applications across a diverse set of verticals.
- A low latency mechanism to bring voice from an end-user to their cloud-hosted engine.
- A mechanism to gather metrics and drive generated application behavior in real-time.

To meet this challenge, a system was built that allowed scientists to generate mobile applications without code, deploy the applications to a specialized cloud environment enabled for voice, and to gather real-time metrics on application behavior to iteratively define the next set of experiments.

iGaming Solution with Machine Learning and Real-Time Stream Processing

iGaming Solution with Machine Learning and Real-Time Stream Processing

One of the region’s leading online casinos wanted to:

- Detect and uniquely incentivize the most profitable players
- Use machine learning models to drive automated, hyper-targeted per-player messaging
- Reduce churn rate and increase player loyalty

With the help of MintData platform, an iGaming solution was deployed ahead of schedule and included targeted in-game offers, tailored messaging and personalized content. The deployed system ultimately resulted in decreased customer churn rate by around 10% and increased average session time by 15-25%.

eTrade competitor project

1m EPS for eTrade Competitor

Backed by Goldman Sachs, a broker dealer was winning mindshare in the marketplace, but faced a set of technical challenges to deliver key features:

- A way to provide real-time stock quotes for the purchase of complex baskets of securities
- A way to detect and alarm on stock price information in real-time
- A way to execute automated trades based on what the market was doing in real-time

With SiliconMint’s stream processor, a system that could withstand the 1 million events per second at market peak was built, in addition to mining this information in real-time for alerting end-users and executing automated trades.

real time fraud detection dashboard

Real-time fraud detection with advanced machine learning

Online payment processor wanted to:

- Reduce chargeback rate
- Increase precision of fraud detection
- Enter new, high-risk markets where high precision fraud detection is required

The developed solution had the following key features:

- User interface to train machine learning models
- User interface to verify at-risk transactions
- Stream processing logic to run real-time fraud detection with machine learning

As a result, the SiliconMint’s team was able to dramatically reduce chargeback rate (~1.08% under typical conditions), increase fraud detection precision by over 20%, and deliver real-time capability to identify fraudulent transactions based on machine learning context.

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