Adaptive ML develops a platform that enables companies to privately tune and deploy language models using reinforcement learning from human feedback. This technology allows businesses to enhance model performance while maintaining data privacy and optimizing outputs based on specific user metrics.
Funding
$20M 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 companies struggle to effectively tailor and optimize large language models (LLMs) for specific use cases while maintaining data privacy and control. Traditional methods often require sharing sensitive data with third parties or lack the flexibility to adapt models based on real-world user feedback and business metrics.
Solution
Adaptive ML offers a platform, Adaptive Engine, that enables organizations to privately tune and deploy open-source LLMs using reinforcement learning techniques. The platform allows businesses to optimize model performance based on specific user metrics and feedback, all within the security of their own cloud environment. By implementing Adaptive Engine as a drop-in replacement for existing LLM APIs, companies can achieve frontier-level performance without sharing data with external parties, retaining control over model updates, uptime, and latency. The platform incorporates automated A/B testing and reinforcement learning from human feedback (RLHF) and AI feedback (RLAIF), along with safety guardrails and fast inference capabilities.
Target Audience
The primary target audience includes companies across various industries looking to enhance their GenAI applications with customized LLMs while maintaining data privacy and control over model performance.
Features
- Reinforcement learning from human feedback (RLHF) and AI feedback (RLAIF) for model adaptation
- Fast inference engine for optimized performance
- Automated A/B testing for continuous model improvement
- Integrated feedback collection mechanisms
- Safety guardrails to ensure responsible AI outputs
- Comprehensive performance tracking on business KPIs and user metrics
- Drop-in replacement for existing LLM APIs
- Unified Rust+Python codebase for inference, training, and RL