Master AI® Consulting Services
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Project Highlights
We can build anything. Whether it's AI, standard optimization software, or electrical engineering—we got you.
An AI chatbot for the AutoAuto product reduced customer support escalations by 50%.
Real-time, automated "bad batch" detection using AI, reducing downstream operational risk.
Details intentionally omitted. Image shown is an abstracted and meaningless AI-generated stand-in.
Automated calibration of a large mechanical system via onboard cameras and computer vision (CV) algorithms.
Details intentionally omitted. Image shown is an abstracted and meaningless AI-generated stand-in.
Leveraged publicly available data to develop global GNSS signal quality monitoring systems.
Details intentionally omitted. Image shown is an abstracted and meaningless AI-generated stand-in.
Redesign of the AutoAuto custom PCB based on user feedback. Improvements include charger ease-of-use, battery life, user accessibility, manufacturability, and cost.
Helped monitor telemetry from two major LEO satellite constellations. Developed AI-based anomaly detection and event prediction systems.
Details intentionally omitted. Image shown is an abstracted and meaningless AI-generated stand-in.
Decreased print time in large-scale additive manufacturing by up to 50%.
Details intentionally omitted. Image shown is an abstracted and meaningless AI-generated stand-in.
1. SYSTEM AND METHOD FOR PROGRAMMING AN EMBEDDED SYSTEM
(claimed 2017, issued 2019)
2. Continuation of SYSTEM AND METHOD FOR PROGRAMMING AN EMBEDDED SYSTEM
(claimed 2017, issued 2020)
A homegrown Master AI product! We've manufactured and sold over 1,000 cars and taught more than 10,000 students how to program in Python!
One of Master AI's founders trained a Convolutional Neural Network to detect leukocoria—a symptom of retinoblastoma—and deployed it in free iOS and Android apps.
⚠️ As seen on NPR and TEDx!
Image credit: Autonomous early detection of eye disease in childhood photographs
One of Master AI's founders researched uncertainty quantification models using a Markov Chain Monte Carlo (MCMC) method to estimate porous media fields based on initial oil production curves. Pictured is the velocity field, with the log-permeability from the SPE10 model shown in the background.
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