Announcement: Checkout out our new PocketLLM tool – Instant neural search for documents
Meet NeuralDB Enterprise by ThirdAI: bringing RAG (Retrieval Augmented Generation) on Rails. A one-stop, full-stack platform designed for comprehensive generative search and retrieval across vast collections of documents and text. Built to easily auto-scale on existing on-premises data centers or cloud CPUs, transforming your commodity compute infrastructure into a modern Generative Search Platform. No need to wait for specialized hardware; the future GenAI stack is here.
Get Started For Free
Runs on your hardware with air-gapped privacy. Absolutely no data movement or transfer needed.
Orders of magnitude cheaper, and a generous free tier. Get started for less than $20 a month.
Improve your Search Relevance Perpetually via Pre-training, Fine-tuning and Reinforcement Learning with Human Feedback (RLHF).
NeuralDB is not just an ordinary RAG ecosystem; it is the most advanced and comprehensive RAG ecosystem available, allowing you to pre-train, fine-tune, and personalize RAG LLM models at will. Watch this video on how you can modify rankings and retrieval in real-time using live RLHF and/or model editing with just a few clicks. Read our API docs to learn how to unlock pre-training, fine-tuning, and RLHF for your RAG ecosystem. Please read this report on why pre-training could be crucial for making LLMs aware of your domain-specific needs. We provide real production case studies here to illustrate why constant behavioral fine-tuning is critical for the success of LLM-driven search in live production.
Since NeuralDB runs on your hardware, our pricing is straightforward software subscription: pay per core per month with no limits on tokens, model training, hosting, retrieval, or embedding generation. The first 8 cores are free, and after that, it’s less than 2 cents per hour per core. Use our easy calculator to determine your needs. The freemium version with 8 core CPUs is more than sufficient for searching and building a knowledge base with millions of text chunks. NeuralDB is easily over 10 times more cost-effective than popular RAG alternatives, and as you scale up, it saves you even more. Notably, with NeuralDB, there’s no need for an embedding model or any vector database. Additionally, NeuralDB allows fine-tuning of LLMs for retrieval and pretrains the retrieval LLM model on inserted text.
