Enhance Data Security and Boost Productivity with Custom AI Models

How Companies Utilize Custom AI for Data Privacy and Boost Workplace Productivity

Generative AI tools like ChatGPT, Gemini, and Claude are transforming the landscape of artificial intelligence applications in business.

These sophisticated large language models (LLMs), with hundreds of billions or even trillions of parameters, act like expansive public libraries. They provide a wealth of information on a wide range of topics, enabling users to solve complex problems and improve performance across various tasks.

However, similar to a library, commercial LLMs often lack highly specialized information and ideally should not contain any proprietary data specific to your organization.

While LLM-powered applications can efficiently handle a broad spectrum of queries, they pose challenges in training and deployment. They rely on datasets that can quickly become outdated and lack access to proprietary policies, practices, or unique knowledge.

This is particularly crucial in enterprise environments, where specialized and proprietary data are key to maintaining a competitive edge. Company secrets, including information on personnel, processes, formulas, and business practices, gain substantial value when combined with AI's automation and analytics capabilities.

To leverage AI's potential while protecting their data, companies are developing smaller, customized AI models. These models can access local proprietary datasets and on-site computing resources, with training enhanced by synthetically generated data.

Leveraging Intellectual Property

Companies are customizing AI applications to their business data using retrieval-augmented generation (RAG). This AI framework integrates foundational or general-purpose models with proprietary knowledge sources such as product data, inventory management systems, and customer service protocols.

By linking AI models to carefully selected data sources, RAG facilitates rapid, effective, and context-specific AI deployment, exposing only a targeted portion of enterprise data to AI models.

Even in highly specialized fields, internal data can be utilized to train and deploy effective generative AI models. For instance, NVIDIA researchers used RAG to create an AI copilot that assists engineers in chip design.

NVIDIA GPUs are extremely complex systems, with tens of billions of transistors connected by wires thinner than a human hair. By fine-tuning an existing foundational model with NVIDIA's design and schematics data, developers created a copilot capable of accurately answering questions about GPU architecture and design, and helping engineers quickly find technical documents. The copilot accesses live databases on local servers, ensuring all computations remain secure and in-house.

This approach allows companies in various industries to use their own data to develop AI agents supporting numerous business functions. Examples include customer support agents trained on product catalog and customer interaction data, supply chain optimization copilots trained on inventory and demand forecasting data, or product quality control agents trained on labeled image data and inspection criteria.

However, many organizations face the significant challenge of collecting and preparing the appropriate data to train effective models.

Achieving Results with Synthetic Data

Gathering and labeling data for model training can be time-consuming and expensive. In highly regulated sectors such as healthcare, finance, and government, data transfer into AI environments may be restricted.

As a result, AI-generated synthetic data is becoming essential for AI success. Organizations can use generative AI techniques to create synthetic data by training models on real data, which then generates new data samples.

Delta Electronics, a global leader in power and thermal management technologies, previously spent days manually collecting and labeling images for training automated optical inspection algorithms on its assembly lines. To expedite the process and reduce costs, the company began using AI-generated synthetic data for deep neural network training in perception tasks. This change allowed Delta to produce the necessary training data in just 10 minutes and complete model training in a fraction of the previous time.

The Future of AI in Business

Smaller RAG-equipped models offer a solution to balancing privacy and problem-solving in AI. They can query local data and operate on on-site infrastructure, reducing data center costs and enhancing security by avoiding the need to send workloads to third-party servers. Additionally, synthetic data provides a fast and cost-effective way for organizations to generate the data needed for accurate, customized AI solutions.

To lower barriers to custom AI, businesses can leverage partnerships to access foundational models, AI and RAG workflows, synthetic data generation pipelines, and other AI development tools.

By tailoring their own models, companies can benefit from reduced computational requirements, faster AI deployment, and minimized exposure of sensitive information, all while maintaining data security and regulatory compliance.