2023 ushered in a wave of excitement about Large Language Models (LLMs) and became the year of the Generative AI proof-of-concept. Enterprises experimented with Generative AI and explored how it may impact their business. According to BCG, Generative AI solutions can deliver up to 50% efficiency and effectiveness gains. However, only 10% of enterprises actually have Generative AI models in production.

Throughout the experimentation process, many enterprises learned that out-of-the-box, generative models are not accurate enough at domain-specific tasks, nor do the models have access to proprietary company data. These companies are turning to model customization via fine-tuning and retrieval augmented generation (RAG) to solve this. This model customization enables them to improve performance on domain-specific tasks, maximize ROI through more capable solutions and reduced token usage, and increase confidence in model reliability, safety, and accuracy.

However, many enterprises lack the expertise, tools, and framework needed to build customized Generative AI models and applications at scale. Capturing value from those Generative AI solutions and accelerating that customization capability across an organization is even more challenging. This is why we built Scale GenAI Platform to help enterprises make more cost-effective investments and more easily build, test, and deploy customized Generative AI applications. By leveraging Scale GP, companies can make 2024 the year of deploying GenAI apps to production and creating real business value.

We wanted to not just stand up a demo or POC, but deploy production-ready infrastructure for an initial use case as a foundation for expansion. With Scale GenAI Platform, we were able to quickly start our first use case: a GenAI solution that makes it easy for users across Global Atlantic to get information from our Enterprise Data Hub using natural language. This will help enable data-driven decision making, shortening the time to insights from days or weeks down to seconds.

Padma Elmgart, CTO, Global Atlantic Financial Group

Scale GenAI Platform

With Scale GenAI Platform, customers use their proprietary data to customize GenAI applications. Our customers are building use cases like:

Content-generation systems that enable sales teams to be more effective and efficient.
Highly customized wealth management copilots that make advisors more effective by helping them tap into their knowledge bases quickly and accurately.
Text2SQL business intelligence applications to make analysts more efficient and embed a culture of data-driven decision-making.

These sophisticated organizations regularly operate at the cutting edge of technology. Yet, even these companies found that they did not have the infrastructure and tools to build enterprise-ready Generative AI applications.

To succeed in their Generative AI journey, we learned that companies need the following:

Custom models built with proprietary data & experts
Focus on expanding to more use cases, not building infrastructure
Flexibility in foundation model selection and cloud service provider
Test and Evaluation to maximize performance and ensure safe and responsible AI

Scale GP enables companies to accelerate their Generative AI journeys and create real business value from their investments.

Custom models built with proprietary data & experts

Enterprise data is often spread across the organization in many different data stores, formats, and with varying degrees of accessibility. This data is often poorly formatted, contains inaccuracies, or is incomplete. Fine-tuned foundation models are extremely sensitive to low-quality data and even one example of poor data can make the difference between a model that is capable for a specific use case or is completely useless.

With Scale GenAI Platform, customers tap into Scale’s industry-leading data expertise by leveraging the Scale Data Engine to transform their proprietary data and generate the highest quality training data for their use cases. Scale then uses this training data to deliver fine-tuned models tailor-made for their unique use cases. Combined with our advanced Retrieval Augmented Generation (RAG) tools, customers can build applications that reference and cite their knowledge base for more accurate responses.

Focus on expanding to more use cases, not building infrastructure

Enterprise customers want to get ROI from their Generative AI investments quickly, so they need to accelerate their ability to customize, build, and deploy Generative AI applications. To do this consistently across their organizations is difficult without centralized infrastructure, which is time and resource-intensive to build.

GenAI Platform does all the heavy lifting by providing streamlined and centrally managed infrastructure to accelerate use cases into production and effortlessly scale up the number of Generative AI applications across the enterprise.

Flexibility in foundation model selection and cloud service provider

Enterprises need the flexibility to keep up with the rapidly developing trends in Generative AI and want to avoid lock-in with a solution or provider that cannot consistently keep pace.

Some enterprises use closed-source models like OpenAI’s GPT-4 or Cohere’s Command model, while others opt for open-source models like Meta’s Llama 2. GenAI Platform supports all major open and closed-source foundation, embedding, and reranking models, including GPT-4 and Llama 2. We are also excited to announce that we have now added Cohere’s Command model and rerank technology to GenAI Platform for fine-tuning, inference, and use in RAG workflows.

Similarly, some customers are on AWS, while others are on Azure, Google Cloud Platform, or have a multi-cloud strategy. We built GenAI Platform so our customers can securely customize and deploy enterprise-grade Generative AI Applications in their own VPC, including AWS and Azure. And we are excited to announce that we will soon be coming to the Azure Marketplace.

Test and Evaluation to maximize performance and ensure safe and responsible AI

Like traditional software applications, organizations must test Generative AI applications to ensure they work as intended. Our customers need test and evaluation (T&E) to be confident that their models perform well and are safe and responsible. However, the tooling, processes, and human expertise for testing Generative AI applications are not widely available today.

The T&E features of GenAI Platform enable our customers to be confident in their customized models with human-in-the-loop testing, evaluation, and monitoring.

Our partnership with Scale helped us build robust GenAI custom solutions for our clients, cutting time-to-market in half. Combining BCG's deep sector and functional experience and focus on value with Scale's proven platform and engineering depth in GenAI, we are uniquely differentiated to help companies realize value quickly with GenAI. This includes customized, multi-model, and production-grade solutions on a scalable multi-cloud infrastructure. We're excited to continue to bring these capabilities to market.

Vladimir Lukic, Managing Director & Senior Partner; Global Leader, Tech and Digital Advantage, BCG

Conclusion

2023 was the year of the Generative AI POC, and we believe 2024 is the year of deploying Generative AI applications to production – delivering real business value. With Scale GenAI Platform, it is now possible to accelerate your Generative AI journey and equip your entire organization to customize, build, test, and deploy enterprise-ready Generative AI models and production applications. Learn more about GenAI Platform here or book a demo below to start today.