Proxis develops AI email agents that automate email management by ingesting full email history and company knowledge bases. These agents automatically draft and send replies, integrating with tools like Notion and Google Drive to maintain full context. The platform offers tiered plans supporting auto-drafting up to full auto-sending with customizable guardrails for sales and support workflows.
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
Enterprises face challenges in deploying large language models (LLMs) due to high computational costs, latency issues, and concerns about data privacy and security. Fine-tuning and deploying these models often require significant infrastructure investments and specialized expertise.
Solution
Proxis offers a platform for LLM distillation, fine-tuning, and optimization, enabling enterprises to deploy customized models on-premises or in the cloud with improved efficiency and security. The platform utilizes kernel-level enhancements and Triton integration to optimize model performance, resulting in faster inference speeds and reduced computational costs. Proxis also provides tools for automated AI agent creation, specifically for managing and responding to emails, by ingesting email history and company knowledge bases. The platform supports both hosted and on-premises deployments, ensuring data confidentiality and control.
Target Audience
The primary target audience includes enterprises and developers looking to deploy and optimize LLMs for various applications, with a focus on those requiring on-premises deployment for data security, as well as sales teams looking to automate email communication.
Features
- LLM distillation and fine-tuning platform for customized model creation
- Kernel-level optimizers that rewrite kernels into Triton for accelerated compilation times
- Automated AI agent creation for email management, including drafting and sending capabilities
- Integration with company knowledge bases for context retrieval
- On-premises deployment options to ensure data privacy and security
- VSCode extension for optimization function calling
- Support for multiple models