Marple AI

Use Case 1: Real-Time Performance Optimization in Motorsports

Problem: Race engineers and Data Acquisition (DAQ) teams often struggle to process massive volumes of high-frequency telemetry data (like suspension travel, engine temps, and tire pressure) quickly enough to make setup changes between practice sessions or during a race.
Solution: Marple Insight’s dedicated motorsport package provides specialized tools like lap triggers, distance mode, and track maps. It allows engineers to instantly visualize MDF/MF4 files and compare telemetry across different laps or setup parameters without manual data cleaning.
Example: During a testing session at the track, a DAQ engineer uses Marple to overlay the telemetry of two different wing configurations. Because the tool handles high-frequency data instantly, the team identifies a specific corner where the new setup loses downforce and adjusts the car's aero balance before the next run, reducing downtime.

Use Case 2: Post-Flight Analysis for Aerospace Prototyping

Problem: Flight testing for new aircraft (like eVTOLs or drones) generates billions of data points across hundreds of sensors. Reviewing this data traditionally requires long wait times for ingestion and complex custom scripts to synchronize flight stages with sensor readings.
Solution: Marple Insight offers an aerospace-specific package that includes 3D aircraft visualization, Primary Flight Displays (PFD), and flight stage markers (e.g., take-off, maneuver, landing). It can ingest data at up to 10 million points per second, making post-flight review nearly instantaneous.
Example: After an experimental flight, a flight test engineer uses Marple to jump directly to the "transition" phase of a vertical takeoff. They use the interactive 3D visualization to correlate physical pitch and roll with battery current spikes, allowing the team to validate the flight control laws within minutes of landing.

Use Case 3: AI-Powered Predictive Maintenance in Manufacturing

Problem: In high-speed manufacturing, equipment failure leads to expensive downtime. Identifying the subtle "noise" or vibration patterns that precede a mechanical failure is difficult for human engineers to spot manually across thousands of hours of sensor logs.
Solution: Marple leverages AI-powered fault detection and deep data search to automatically identify anomalies in time series data. Its "Marple DB" acts as a high-performance lakehouse that cleans and unifies data, making it ready for machine learning workflows.
Example: A factory manager monitors high-frequency vibration sensors on a robotic assembly arm. Marple’s AI detects a frequency shift that matches a known "pre-failure" signature for a specific bearing. The system alerts the maintenance team to replace the part during a scheduled break, preventing an unmanaged 4-hour line stoppage.

Use Case 4: Centralized Collaboration for Distributed R&D Teams

Problem: Engineering data is often siloed in individual MAT or CSV files on different engineers' laptops. When a problem occurs, sharing specific data snippets for troubleshooting usually involves sending large files back and forth, leading to version control issues.
Solution: Marple DB provides a centralized, high-performance storage solution (based on Apache Iceberg) with a web-based interface (Marple Insight) that allows for "point-of-reference" sharing.
Example: A hardware engineer at a hyperloop startup identifies a thermal anomaly in a sub-system. Instead of exporting and emailing a data clip, they share a direct Marple link with the software team. The software engineers open the link to see the exact timeframe, zoomed-in view, and calculated metrics the hardware engineer was looking at, allowing them to collaborate on a fix in real-time.