The startup offers an AI-powered no-code platform that facilitates large language model operations (LLMOps) for enterprises by providing structured guidelines for data collection and annotation. This platform ensures high-quality corpus generation through a combination of deep learning models, heuristics, and multi-level quality assurance, enabling organizations to effectively train and evaluate their natural language models.
Funding
$400K 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
Enterprises struggle to efficiently manage the lifecycle of large language models (LLMs), particularly in data preparation, fine-tuning, and deployment. Ensuring high-quality training data and integrating LLMs into existing workflows often requires significant manual effort and specialized expertise.
Solution
DeepNatural offers an LLMOps platform designed to streamline the development and deployment of custom LLMs. The platform provides tools for constructing high-quality language data, leveraging fine-tuned and quantized LLMs, and deploying no-code LLM applications. DeepNatural's solutions include Labelr for data preparation, tools for fine-tuning open-source LLMs, and LangNode, a no-code application builder for integrating LLMs into enterprise workflows. By focusing on data quality, model optimization, and ease of integration, DeepNatural enables businesses to leverage private LLMs for enhanced customer engagement, streamlined operations, and data-driven insights.
Target Audience
DeepNatural targets enterprises, startups, and research institutions seeking to leverage LLMs for various applications, including customer service, content generation, market research, and internal knowledge management.
Features
- Labelr platform for data collection, generation, cleaning, labeling, review, and evaluation with a workforce of over 230,000 workers.
- Enterprise version of Labelr for processing internal digital assets with cloud-based SaaS or on-premise deployment options.
- Tools for fine-tuning open-source LLMs, including Llama2, with custom data curation for pre-training and fine-tuning.
- LangNode, a no-code LLM application builder for ML engineers, product designers, and prompt engineers.
- Pre-built workflows for summarization, advanced search, report generation, document analysis, chatbots, and Q&A systems.
- API integration capabilities for connecting LLMs to existing systems and data sources.
- Support for vector databases for enhanced information retrieval and RAG (Retrieval-Augmented Generation) applications.
- Real-time translation services to facilitate communication across language barriers.