LLAMA 2 7B and 3.1 8B

Oxide AI

40%

reduction in production costs for LLM-generated content and extractions

37%

faster average sequential response times than OpenAI GPT-4 while maintaining comparable quality

95%

accurate results after a month of low-rank adaptation (LoRA) tuning

^*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.

CASE STUDY

Finding trustworthy signals in a sea of financial data

At a glance

Industry

Technology

Use case

Enhancing financial research with fine-tuned, domain-specific LLMs

Goal

Deliver precise, transparent insights at scale with optimized LLM performance

Llama versions

Llama 2 7B and Llama 3.1 8B

Deployment

AWS Cloud, Amazon Bedrock, IBM Cloud, IBM watsonx

Download full case study

THEIR STORY

Amplifying humans with trusted AI

Oxide AI’s mission is to amplify individuals by giving them access to enterprise-grade AI technology that solves the critical problem of information overload. Oxide’s solutions deliver precise, context-aware insights at the right time, empowering people to make smarter, faster decisions supported by trusted and transparent AI.

THEIR GOAL

Extract insight from the flood of financial data

The volume, velocity and complexity of financial information far exceed human comprehension, making smart decision-making nearly impossible for the average investor. Oxide wanted to use large language models (LLMs) to overcome this information overload by researching markets and generating valuable insights for investors.

THEIR SOLUTION

Llama-powered finance app cuts through the noise

Oxide developed Oxogen AI, an intelligent app that scours the web for quantitative knowledge like trading data, SEO filings and quarterly reports, plus qualitative information like patent applications, news and social media feeds. The app uses multiple lightweight Llama models to extract critical information and generate valuable financial content.

Llama-powered Oxogen AI delivers quantitative and qualitative financial data to investors.

THEIR APPROACH

Multi-agent engine fuses quantitative and qualitative insight

Qualitative and quantitative information come together in EvoQ™, Oxide’s proprietary AI engine. EvoQ uses multiple reasoning AI agents operating at high speed to research entire markets quickly. To ensure accuracy, Oxide used LoRA to fine-tune lightweight Llama models for data extraction, classification and content generation.

Llama powers custom research agents in the Oxogen AI app.With LoRA, smaller models can produce highly accurate, domain-specific results that are as good as or better than the results produced by general-purpose models hundreds of times larger. Using smaller models conserves computing power, which allows Oxogen AI to scale up without incurring massive computing costs.

Lightweight Llama models with LoRA power multiple research agents and content generation.

THEIR SUCCESS

Open-source Llama put Oxide AI in control of their data and performance

Shifting to open-source Llama helped address key challenges around privacy, domain-specific fine-tuning and production efficiency, all while achieving major cost savings. Small Llama models matched the accuracy of GPT but used just a fraction of the computing resources.With Llama powering its generative AI components, Oxogen AI is positioned to take personal financial services far beyond traditional investing solutions and deliver on a global scale.