DeepAuto provides an agentic intelligence platform that transforms enterprises by centralizing fragmented data into an AI-ready infrastructure. The platform deploys autonomous agents to run enterprise workflows, requiring no dedicated AI engineering team for deployment. This solution offers rapid deployment and full data sovereignty by operating entirely within the client's existing infrastructure.
Funding
Funding not disclosed
NDFounders
Product
Problem
Enterprises face challenges in building, training, and deploying customized generative AI models due to the high costs and complexity associated with managing the entire LLMOps lifecycle. Optimizing GPU resource utilization and model management remains a significant hurdle, hindering widespread adoption of GenAI solutions.
Solution
DeepAuto provides an automated AI platform, AutoAIOps, designed to streamline the entire LLMOps cycle, from model selection and training to deployment and continuous optimization. The platform includes Agent Builder for automated model development, ScaleServe for efficient model serving, and AutoEvolve for continuous performance tuning. DeepAuto's solutions, including Lite LLMOps and Lite Space, aim to reduce costs associated with training and serving GenAI models by optimizing infrastructure and model layers. By automating key processes and providing tools for efficient resource management, DeepAuto enables enterprises to build and deploy high-performing, customized GenAI models more easily and at a lower cost.
Target Audience
DeepAuto targets enterprises seeking to leverage generative AI for business workflow automation and AI R&D, as well as AI researchers looking to maximize GPU usage.
Features
- **AutoAIOps Platform:** Automates model selection, training, deployment, and optimization.
- **Lite LLMOps:** Automates and optimizes the process of selecting, training, evaluating, and deploying language models.
- **Agent Builder:** Enables enterprises to build self-improving Agentic AI systems from natural language.
- **ScaleServe:** Reduces operating costs by routing queries to the most cost-effective models.
- **Query Router:** Optimizes model usage to reduce serving costs.
- **Model Compressor & Accelerator:** Reduces serving costs by optimizing model efficiency.
- **Model Evolver:** Maintains stable model performance through continuous tuning.
- **Lite Space:** AI research and development platform designed to help AI researchers utilize limited GPUs more efficiently.
- **Cost Reduction Technology:** Reduces training costs by up to 99% and serving costs by up to 96.5%.