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LLAMA 3.3 70B, LLAMA 3.2 11B, LLAMA 3.2 3B AND LLAMA 3.1 405B

AddAI

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500,000

end-customer interactions per month

85%+

AI answer accuracy rate

50%

reduction in unanswered queries

6,000

hours of human labor saved every month
*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.

CASE STUDY

Bringing drag-and-drop simplicity to AI development with Llama

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At a glance

Industry

Technology

Use case

Simplifying AI development with AI-assisted training, fine-tuning, deployment and management

Goal

Make creating custom AI agents accessible to non-technical business users

Llama versions

Llama 3.3 70B, Llama 3.2 11B, Llama 3.2 3B and Llama 3.1 405B

Deployment

IBM watsonx

THEIR STORY

Using AI to transform AI development

Founded in 2019, AddAI is dedicated to simplifying and accelerating the implementation of AI assistants with user-friendly tools that make AI development accessible — even for companies that don’t have their own AI teams. Customers across sectors are using the AddAI platform to create personalized AI assistants for customer experience, communication and support.

THEIR GOAL

Put the keys to AI development into business users’ hands

Building custom AI agents that understand a business’s unique brand, knowledge bases and customer needs is a massive challenge for skilled AI developers. Getting to a functional, expert AI agent can require model training and fine-tuning, custom retrieval augmented generation (RAG) workflows and complex pipelines filled with tool calls, API requests and exacting data management. The AddAI team wanted to bake all of that AI development complexity into a drag-and-drop platform where business users could create their own personalized, expert AI assistants without the help of developers.

THEIR SOLUTION

Use Llama to do the technical work of AI development

AddAI created an intuitive, no-code platform for developing and deploying expert AI assistants. They used Llama models throughout the platform to perform technical development tasks beyond the average user’s abilities.Llama models work in the background to supervise model training, connect pipeline elements and orchestrate tasks. The platform includes a Llama-powered assistant that coaches customers through prompt engineering, fine-tuning, deployment and performance analytics. Llama models also support end-user experiences by powering AI assistant conversations, coordinating intuitive multi-turn logic and generating responses in RAG-based discovery workflows.

THEIR APPROACH

Build fast with Llama and IBM watsonx

The AddAI team built its platform using IBM watsonx. With watsonx, the team was able to call different Llama models via API, fine-tune them for their specific use cases within the platform and architect complex, multi-model pipelines.Llama’s open-source licensing and open weights allowed the AddAI team to create the custom IP their platform needed to make developing AI a simple, code-free experience. Key elements AddAI built with Llama include: an AI autopilot that helps customers construct and manage assistants; conversational agents with decision-making capabilities; and a RAG workflow for intelligent, contextual discovery.
their approach graphic
The AddAI platform transforms building and managing AI assistants into a friendly, code-free experience..

THEIR SUCCESS

Millions of expert AI interactions, thousands of hours saved each month

AddAI customers across sectors — banking, e-commerce, telecommunications and automotive — have used the platform to build and deploy custom, personalized AI assistants for customer experience (CX), support and communications. Since the platform makes it easy to train models on unique knowledge bases and includes RAG workflows, customers have achieved remarkable accuracy with unique industry vocabularies, arcane subject matter and even brand tone and assistant personality.
  • 500,000 end-customer interactions per month
  • 85%+ AI answer accuracy rate
  • 50% reduction in unanswered queries
  • 6,000 hours of human labor saved every month
*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.
“We are preparing on-premise Llama deployments that will give us complete control over operations, data and the model customizations our clients need. The ability to adapt, optimize and deploy models in our local environment was decisive in selecting Llama.”

Tomáš Jindra, COO and Co-Founder, AddAI

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