Next-Level Analytics Use Cases
Predicting EV Public Charging Station Demand
We used: Charging station data
residing in Snowflake database plus external vehicle route patterns
Our results: Discovered various correlations and created a predictive demand model to enable a much more intelligent infrastructure rollout. ROI is less capital spend and an increase in revenue per station as utilization rate is maximized
Lumen Insights Solutions
Partner: Lumen Card Services (LCS)
Solution: Working with our partner LCS, we help merchants leverage their payment processing data in other areas of their business -- providing next-level analytics and actionable insights to optimize customer service, product presentation, inventory management and marketing campaigns.
Object Population Detection from Drone Imagery for Conservation Study Purposes in Costa Rica
We used: Drone imagery data
Our results: Our model detected 8% more turtles than manual counts while effectively reducing the manual validation burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on a relatively small set of turtle examples (N = 944), implying that this method can be easily bootstrapped for other applications, and is practical with real-world drone datasets.
Predicting Drill Bit Failure
We used: Sensors that generated terabytes of drilling time series data from 100 channels of sensor logs
Our results: Built and trained a neural network (hybrid CNN-LSTM) enabling a Fortune 100 oil major to predict rig failures saving hundreds of thousands of dollars per well in downtime expense
2predict’s team are experts in, computer vision, speech recognition, sensor data fusion, oil and gas, and image and audio processing across various field operations environments.
Leverage neural networks to predict equipment failures, optimal points of presence, target customers, production and yield performance.
Build insightful visualizations and extract features from image and video data collected via drone, satellite, aircraft or fixed location. Experience with noisy and intermittent data from harsh or remote environments.
Detect, classify and identify objects or events utilizing multi-spectral cameras, LiDAR or other imagery.
Integrate diverse sensor and signal data – audio, vibration, location, accelerometer, environment, etc. – to enhance insights and predictive models.