Augento provides a platform for Reinforcement Learning (RL) fine-tuning to adapt foundational Large Language Models (LLMs) to specific use cases. This process relies on defining a reward function rather than collecting curated prompt-response pairs, leading to more reliable model behavior. The service automates the RL training jobs, hosts the resulting fine-tuned models, and provides API access for deployment.
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
Large language models (LLMs) often require adaptation to specific use cases to improve performance and reduce failure rates, but supervised fine-tuning requires extensive curated prompt-response pairs. Prompt engineering can be unreliable and may not be sufficient for complex tasks.
Solution
Augento provides a reinforcement learning (RL) fine-tuning platform that allows users to adapt LLMs to specific tasks without needing curated datasets. The platform connects to existing LLMs, captures data flow to identify error cases, and uses a user-defined reward function to optimize the model through RL training. Fine-tuned models are automatically hosted and can be accessed via a standard API.
Target Audience
The primary users are startups and individual developers who need to adapt LLMs for specific use cases, as well as enterprises with high-volume requirements.
Features
- Reinforcement learning fine-tuning using a user-defined reward function
- Automated data capture to identify error cases for training
- Hosting of fine-tuned models with a standard API endpoint
- Integration with existing LLMs through API key modification
- Support for coding agents, MCP tool specialization, browser agent navigation, and VLA robot control
- Access to Qwen 2.5 32B Instruct model