Provides a full-stack Generative AI platform that enables businesses to build, fine-tune, and deploy custom Small Language Models (SLMs) using proprietary data in a secure environment. This platform addresses the need for tailored, production-ready AI solutions by offering tools for model adaptation, continuous optimization, and seamless integration with external data sources.
Funding
$14M 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
Many businesses struggle to create tailored AI solutions due to the complexity of adapting and optimizing large language models (LLMs) for specific use cases. This often requires significant machine learning expertise, computational resources, and time, hindering the adoption of AI in various industries.
Solution
Prem offers a full-stack Generative AI platform designed to simplify the development, fine-tuning, and deployment of custom Small Language Models (SLMs). The platform provides tools for autonomous fine-tuning, continuous optimization, and seamless integration with external data sources, enabling businesses to transform proprietary data into actionable insights. By leveraging SLMs, Prem delivers secure, reliable, and production-ready AI solutions tailored to specific business needs, ensuring data ownership and control. The platform's open-source-first approach allows users to build AI agents, implement Retrieval-Augmented Generation (RAG), and integrate external data sources with ease.
Target Audience
The primary target audience includes businesses across various industries looking to leverage custom AI models for specific use cases, as well as AI developers seeking a platform to streamline the development and deployment process.
Features
- Full-stack Generative AI platform for building, testing, and deploying AI applications
- Autonomous fine-tuning capabilities for customizing SLMs with proprietary data
- Tools for creating AI agents, implementing RAG, and integrating external data sources
- Continuous optimization features for integrating new domain-specific knowledge on the fly
- Support for both cloud and edge deployments
- Integration with AWS Bedrock and S3 for scalable and efficient solutions
- Built-in metrics and tools for evaluating model performance
- Playground environment for testing models
- Model performance tracking over time with stats