Datawizz AI enables companies to transition from reliance on large, generic language models to their own specialized language models (SLMs) that are 50x-1000x smaller and more cost-effective. By recording LLM interactions and utilizing them for fine-tuning, Datawizz allows businesses to maintain ownership and control over their AI models while achieving comparable performance at a fraction of the cost.
Funding
Funding not disclosed
Founders
Product
Problem
Companies are increasingly reliant on large, generic language models (LLMs) like GPT-4 and Claude, which can be expensive, slow, and lack the specialization needed for specific business tasks. This dependence creates vendor lock-in and limits control over AI model performance and data ownership.
Solution
Datawizz AI enables businesses to transition from generic LLMs to their own specialized language models (SLMs) that are significantly smaller, faster, and more cost-effective. The platform records LLM interactions and uses them to fine-tune custom SLMs, giving businesses ownership and control over their AI models. Datawizz offers an OpenAI-compatible solution that allows companies to distill custom SLMs that can be 10x-100x cheaper while maintaining comparable accuracy and running on their own infrastructure. The platform also provides tools for model routing, policy enforcement, and deployment to any cloud or on-device.
Target Audience
Datawizz AI targets companies that want to reduce their reliance on large, expensive LLMs and gain more control over their AI models, including those in industries with specific data and performance requirements.
Features
- Plug-and-play integration with full OpenAI and Anthropic compatibility
- LLM data management for collecting, labeling, and owning LLM conversation history
- Fine-tuning of SLMs that are 50x-1000x smaller than frontier LLMs
- Model routing and policies to enhance AI with smart routing and secure it with policies
- Deployment of SLMs to any cloud or customer devices
- AI analytics to understand AI consumption and performance, including model quality, user feedback, token consumption, and inference costs
- Smart routing to direct AI requests to the right model based on content, topic, task, or size
- LLM guardrails to secure against abuse with smart policies, protecting against hallucinations and prompt injections
- On-device inference to bring SLMs closer to customers with support for browsers and mobile devices