Rabbitt.AI develops reliable generative AI solutions by leveraging enterprise data to create custom large language models and high-quality training datasets. The platform addresses the challenge of inconsistent AI performance by providing precise data annotation and AI-assisted quality checks, ensuring accurate and effective model outputs.
Funding
$2.1M 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.
TFounders
Product
Problem
Many enterprises struggle to deploy generative AI solutions due to the challenges of inconsistent AI performance and the need for high-quality, properly annotated training data. Existing pre-built LLMs may not be suitable for specific business needs, and creating custom LLMs requires significant expertise and resources.
Solution
Rabbitt.AI offers a platform that leverages enterprise data to develop reliable generative AI solutions, including custom large language models (LLMs) and high-quality training datasets. The platform provides interactive data annotation tools and AI-assisted quality checks to ensure accurate and effective model outputs. Rabbitt.AI assists at every level of the LLM adoption journey, from defining the problem and designing the data to developing the model and application, and evaluating the data and model. By focusing on data quality and providing expert consultation, Rabbitt.AI enables businesses to tap into the full potential of their data and create AI solutions tailored to their specific needs.
Target Audience
Rabbitt.AI primarily targets enterprises looking to build custom LLMs or improve the performance of existing AI models through high-quality data annotation and curation.
Features
- No-code solution for creating custom labeling projects
- AI-assisted technologies for efficient and precise data annotation
- Industry-specific consultants to analyze business and data requirements
- Crowdsourcing pool of labelers
- Consultation services for defining problems, designing data, developing models, and evaluating data and models
- Support for creating high-quality training datasets for fine-tuning
- Formatting of internal documents for LLM-ready use in RAG systems