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Description
Apollo is a streamlined mobile application that facilitates completely on-device, cloud-independent AI interactions, allowing users to interact with sophisticated language and vision models in a secure, private manner with minimal delays. It features a collection of compact foundation models sourced from the company's LEAP platform, enabling users to compose messages, send emails, converse with a personal AI assistant, create digital characters, or utilize image-to-text functions, all while maintaining offline capabilities and ensuring no data is transmitted beyond the device. Optimized for immediate responsiveness and offline functionality, Apollo guarantees that all inference occurs locally, eliminating the need for API calls, external servers, or logging of user data. This application acts as both a personal AI exploration tool and a development environment for those utilizing LEAP models, allowing users to effectively assess a model's performance on their specific mobile devices prior to more widespread implementation. Additionally, Apollo's design emphasizes user autonomy, ensuring a seamless experience free from external interruptions or privacy concerns.
Description
LiteRT, previously known as TensorFlow Lite, is an advanced runtime developed by Google that provides high-performance capabilities for artificial intelligence on devices. This platform empowers developers to implement machine learning models on multiple devices and microcontrollers with ease. Supporting models from prominent frameworks like TensorFlow, PyTorch, and JAX, LiteRT converts these models into the FlatBuffers format (.tflite) for optimal inference efficiency on devices. Among its notable features are minimal latency, improved privacy by handling data locally, smaller model and binary sizes, and effective power management. The runtime also provides SDKs in various programming languages, including Java/Kotlin, Swift, Objective-C, C++, and Python, making it easier to incorporate into a wide range of applications. To enhance performance on compatible devices, LiteRT utilizes hardware acceleration through delegates such as GPU and iOS Core ML. The upcoming LiteRT Next, which is currently in its alpha phase, promises to deliver a fresh set of APIs aimed at simplifying the process of on-device hardware acceleration, thereby pushing the boundaries of mobile AI capabilities even further. With these advancements, developers can expect more seamless integration and performance improvements in their applications.
API Access
Has API
API Access
Has API
Integrations
C++
Google AI Edge Gallery
JAX
Java
Kotlin
Liquid AI
Objective-C
OpenRouter
PyTorch
Python
Integrations
C++
Google AI Edge Gallery
JAX
Java
Kotlin
Liquid AI
Objective-C
OpenRouter
PyTorch
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Liquid AI
Founded
2023
Country
United States
Website
www.liquid.ai/apollo
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.google.dev/edge/litert
Product Features
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)