What is Datawizz?
Datawizz enhances artificial intelligence applications by intelligently routing user requests between various Large Language Models (LLMs) and specially trained Specialized Language Models (SLMs). This dynamic routing mechanism aims to achieve optimal results by selecting the best model for each specific task, potentially reducing AI operational costs by up to 95% and improving accuracy compared to using a single LLM.
The platform facilitates easy integration into existing applications with minimal code changes, supporting compatibility with major providers like OpenAI and Anthropic. It logs LLM interactions, enabling users to build proprietary datasets for analysis and model improvement. Datawizz also offers capabilities for fine-tuning SLMs based on collected data, evaluating model performance based on cost and accuracy metrics, and deploying these custom models securely across different environments.
Features
- Plug-and-Play Integration: Integrate into existing applications within minutes with OpenAI/Anthropic compatibility.
- LLM Data Management: Collect, label, and manage LLM conversation history to build custom datasets.
- Fine-tune SLMs: Create smaller, specialized language models (SLMs) fine-tuned with user data.
- Smart Model Routing: Define rules to route AI requests to different models based on content, topic, task, size, or user criteria.
- Model Evaluation: Compare different AI models on cost, performance, and accuracy using manual and automated benchmarking.
- AI Analytics: Understand LLM consumption patterns, model quality, token usage, inference costs, and user feedback.
- Deploy SLMs: Deploy specialized models to cloud environments or user devices.
- LLM Guardrails: Implement smart policies to secure LLM traffic against abuse, hallucinations, and prompt injections.
Use Cases
- Optimizing AI application costs by routing requests to the most efficient models.
- Improving the accuracy and relevance of AI-generated responses.
- Building and deploying custom-trained Specialized Language Models (SLMs).
- Managing and analyzing LLM interaction data for insights and improvements.
- Securing AI applications against misuse and prompt injection attacks.
- Evaluating and comparing the performance of various LLMs and SLMs.
- Implementing different AI model strategies for different user segments or tiers.
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