RunLocal provides an AI agent specifically designed to automate and optimize machine learning model inference on edge hardware. This platform streamlines the complex process from trained PyTorch models to production-ready edge deployment by handling optimization, profiling, and validation. The result is significantly faster timelines and more efficient models with reduced manual effort for developers.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Deploying AI/ML models across diverse edge devices presents challenges in ensuring optimal performance and quality due to varying hardware capabilities and operating systems. Engineering teams face difficulties in efficiently testing, evaluating, and optimizing models for specific device configurations, leading to prolonged development cycles.
Solution
Neuralize offers an optimization engine that automates the deployment of custom AI/ML models across a wide range of edge devices. The platform facilitates automatic testing and evaluation of models on real devices, enabling engineering teams to quickly identify and address performance bottlenecks. By applying post-training optimization methods and providing tools for assessing both objective and subjective quality, Neuralize significantly reduces model conversion and optimization time, ensuring optimal AI experiences for end-users.
Target Audience
The primary target audience includes ML teams and engineering teams deploying AI/ML models on edge devices, particularly those in companies developing on-device AI applications.
Features
- Automated application of post-training optimization techniques across various platforms
- Device cloud for performance and quality testing of optimized models
- Evaluation hub with charts and plots of test data for quality and performance metrics
- Integrated tools for inspecting model output and assessing subjective quality
- Reporting tools for deploying optimized models tailored to specific device constraints
- Bespoke PyTorch optimization service
- Capability to leverage existing device infrastructure for testing