Many organizations are looking for a reliable way to experiment with and understand the applications of generative AI such as ChatGPT and Copilot. These tools, built using complex ML components like LLMs and neural networks, offer a lot of promise but are expensive to train and should be approached with care. We understand that it can be difficult to know how or where to start when it comes to safely using open LLMs within your organization. With EK’s Enterprise LLM Accelerator, our experts can help you identify the right LLM framework for your bespoke business domain and security requirements through a customized pilot and private LLM that sits behind your organization’s firewalls – with the ability to be contextual with your data using vector embeddings. Learn how to adapt generative AI trained on your secure data to solve domain-specific problems in your organization.

Over 10-12 weeks, EK will help your organization develop a first implementable version (FIV) of an LLM from end to end. EK will start by prioritizing and piloting your key use cases to quickly validate the application of an LLM, demonstrating its benefits for your organization. We will then define, develop, and pilot your FIV LLM, validating it with your stakeholders, and build an implementation roadmap to support the scalability of LLM usage across your organization. 

Throughout the entire engagement, our experts will be conducting continuous knowledge transfer and upskilling your team in LLM best practices and standards. This will not only empower your organization to operate independently in the future, but also socialize the value that LLMs bring to a wider audience. 

Once the initial model has been developed, EK will evaluate its performance and make improvements employing the technique(s) most appropriate for your organization.

By the end of the Enterprise LLM Accelerator engagement, your organization will have:

• A provable, scalable foundation, roadmap, and repeatable approach for adapting LLMs to enterprise use cases.
• Clear demonstration of how a semantic LLM can provide value before making a long-term investment.
• Alignment on LLM strategy for scale, supported by findings from initial use cases and in line with organizational objectives.
• Tangible solutions to data challenges, encouraging adoption and garnering interest and ownership among users.