Practical use of AI for “real-world” problems is hard, requiring experts to align domain knowledge to design the right data/model combinations. Large language models like GPT-4 are great for general purpose tasks but fall short on domain-specific use cases, especially when proprietary data is involved. Engineers are being thrown into the AI race and need guardrails.
Jaxon’s an AI platform that guides data science teams through the research-design-build process. It combines formal reasoning with an LLM-driven agent to ensure data science teams adhere to best practices. Jaxon explores tradeoffs using simulations to quickly figure out what will work best for each use case.
With Jaxon, users actively participate in the development process and iteratively validate performance but don’t need to be PhDs to be effective. Jaxon’s proprietary framework manifests as a trusted technical advisor that helps data science teams quickly design state-of-the-art AI systems, steering clear of potential pitfalls and misinformation. Jaxon continuously improves its performance by self-analyzing user projects and incorporating new knowledge of what works and when.
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