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Our mission is to inject Deep Learning and LLM technology into core business enterprise workflows

Argonaut AI was formed in 2021, in Palo Alto California, by technology industry veterans, ex-consultants, and top academics from (UC Berkeley and Stanford) who came together to bring the latest advances in machine learning (ML) to solve high-value business problems.

We saw an unmet need for a new type of technology solutions provider that could bring together implementation services with SaaS products to enable large global enterprises to inject Deep Learning and Intelligent Automation into their core workflows and business processes. We specialize in developing domain-specific LLMs (Large Language Models) for use in vertical markets like healthcare and financial services.

Despite all of the recent hype about LLMs (e.g. ChatGPT, Bing, Bard), LLMs are not yet ready for the enterprise market, especially when it comes to applying them to the automation of core business processes. We use LLMs to transform business processes into frictionless ML pipelines or "Autonomous Workflows". Put another way, we are solving "the last-mile problem" for LLMs in the enterprise.

We work with some of the largest corporations in the world to develop industry-specific LLMaaS applications

Argonaut AI recruits top data science, software development, and engineering talent from all over the world and pairs them with experienced industry leaders to deliver optimal results in the form of scalable deep learning products and platforms.

We are in the process of releasing new industrial LLMs that are capable of engaging with subject matter experts and performing complex domain-specific tasks and decisioning.

Demonstrated ability to operationalize LLMs with full-stack enterprise solutions LLMaaS (LLM as a Service) to support real-world use cases with massive data sets.

Our delivery model combines subject matter experts (e.g. healthcare and financial services) with data scientists to validate LLM outputs, drive factuality and verifiability. We can deploy teams at very large scale in the US and India.

Use of fine-tuning and embeddings to improve model performance (and precision) with active learning involving humans – RLHF (Reinforcement Learning with Human Feedback).