Choosing the Right Enterprise Generative AI Platform: A Practitioner's Guide

Introduction:

As generative AI continues to transform various aspects of business operations, enterprises are increasingly looking to adopt Large Language Models (LLMs) and Generative AI technologies.

However, with a ton of choices out there to implement generative AI solutions, choosing the right platform can be a confusing and daunting task. There are traditional enterprise conversational AI vendors, Gen AI tools like LangChain and LlamaIndex, model companies like OpenAI, Anthropic, and Cohere, and then there are hyperscalers like Microsoft, Google, and AWS.

In this article, I tried to describe the features that matter most and an evaluation checklist to help enterprises to make an informed decision when choosing a platform. Fair warning: it's a long one, but I couldn't help it - all these features are important for an enterprise.

Now, lets dive into some of the Key Considerations for Choosing an Enterprise Generative AI Platform:

1. Breadth of Use Cases:

An enterprise-ready generative AI platform needs to handle a wide range of use cases across business functions and industries. Platforms offering pre-built solutions, industry-specific models, and customizable workflows can help tackle all sorts of challenges, such as:

- Customer Support Automation: Generative AI can power intelligent virtual assistants and chatbots that understand customer queries, provide accurate responses, and resolve issues efficiently, reducing response times and improving customer satisfaction.

- Employee Experience Automation: A universal virtual assistant that can solve for a wide range of self-service automation use cases, including IT service requests, HR and recruitment automation, and knowledge management capabilities, answering from enterprise-wide knowledge bases to find the right information at the right time.

- Enhancing Agent Experience: Enhance agent productivity with real-time coaching, playbook adherence, agent performance monitoring, and helping agents to fulfill customer requests by automatically integrating and interacting with enterprise APIs.

- App Co-pilot Experience: Provide the ability to be a co-pilot for enterprise applications.

- Business Process Automation: Generative AI can automate repetitive and time-consuming tasks, such as data analysis, data entry, document generation, and workflow management, freeing up human resources for higher-value activities.

- Content Generation: From product descriptions and marketing copy to campaigns and community posts, generative AI can streamline content creation processes, ensuring consistency, quality, and scalability.

- Insights and Intelligence Decision Systems: By analyzing vast amounts of structured and unstructured data, generative AI can extract key information that is of great use, and generate insights to support data-driven decision-making.

2. Ease of Use and No-Code Capabilities:

To democratize AI adoption, look for platforms with user-friendly interfaces and no-code tools like drag-and-drop model builders and visual workflow designers. This allows non-technical users to create and deploy generative AI applications, accelerating time-to-value and fostering collaboration between IT and business teams.

3. Advanced Language Model Orchestration:

Advanced Language Model Orchestration is a critical capability for enterprise conversational AI platforms to maximize performance, cost, and scalability across diverse use cases and requirements. A well-designed conversational AI + generative AI platform should employ orchestration techniques to intelligently route requests to the most suitable language model based on factors such as task complexity, language, domain, and resource availability.

By leveraging a combination of intent-based Natural Language Understanding (NLU) and Large Language Models (LLMs), a robust platform ensures that each user query is handled by the most appropriate model, maximizing accuracy and efficiency. This approach enables enterprises to leverage the strengths of different models, such as using intent-based NLU for structured and deterministic interactions while harnessing the power of LLMs for more open-ended and contextual conversations.

Moreover, advanced model orchestration capabilities allow for fine-tuning models to specific industry domains and use cases, further enhancing performance and relevance. This is particularly valuable for enterprises with specific compliance, security, and business logic requirements, as it ensures that the generative AI solution is tailored to their unique needs.

In contrast, platforms that offer powerful language understanding and generation capabilities through state-of-the-art models but lack granular control and orchestration may lead to challenges in adhering to business compliance needs, suboptimal resource utilization and higher costs.

By prioritizing advanced language model orchestration, generative AI platforms enable enterprises to strike the right balance between performance, cost, and scalability.

4. Knowledge Integration and Retrieval Augmented Generation (RAG):

Knowledge Integration and Retrieval Augmented Generation (RAG) capabilities can enhance the accuracy and relevance of generative AI outputs by integrating enterprise knowledge sources. Look for a platform that offers advanced RAG features, including:

- Vector Search: Ability to understand the intent behind user queries and retrieve the most relevant information from enterprise knowledge bases, with the help of semantic embeddings and vector search.

- Data Ingestion Pipelines: Automated processes for extracting, transforming, and loading data from various sources into a unified knowledge repository of text vector index.

- Context and Metadata Enrichment: Enhancing retrieved information with additional context, such as generative topics, categories, and relationships, to improve understanding and retrieval quality. The success of a RAG solution depends on the quality of context available in the documents indexed. As enterprise documents are not necessarily created keeping the context in mind, it becomes very important to enrich the context of the documents extracted.

- Customizable Retrieval Algorithms: Flexibility to fine-tune retrieval algorithms based on enterprise-specific requirements, such as domain-specific terminology, metadata filters, business rules, and access controls. Options to choose various different strategies that are suitable for a collection of a type of documents - and also support combining these retrieval strategies (fusion of strategies).

- Tools for Explainable AI: The platform should provide explainability into how the chunks are extracted, how they are indexed, how they are retrieved and ranked at every stage of the pipeline to understand and improve the quality of answer generation.

- Ability to Monitor and Continuously Evaluate: The platform should offer capabilities to monitor and continuously evaluate the performance of retrieval and gen AI answer generation for quality, fairness, and completeness, alert on incidents, and allow administrators to swiftly take actions.

- Enterprise Readiness: The platform should provide a wide range of connector capabilities, support for role-based access controls, inclusion/exclusion rules, sensitive information redaction, and the ability to monitor using insights.

5. Hybrid Approach: Combining Traditional Conversational AI and Generative AI

Enterprises with existing investments in traditional conversational AI can benefit from platforms that seamlessly combine the strengths of both approaches, enabling more natural and contextual conversations.

Supporting conversational AI seamlessly on multiple channels, including voice and text channels, could be very complex. The platform should be able to handle conversational AI nuances such as complex contextual conversations and digressions. Similarly, it should support complex Voice AI nuances like integrations into advanced speech-to-text and text-to-speech engines, ability to customize speech adaptation, and ability to support human-like synthetic voice generation and interactive experiences nuances like repeat holds, etc.

6. Explainable AI and Model Governance:

Transparency and accountability are crucial factors when implementing generative AI in enterprise settings. Opt for a platform that prioritizes explainable AI, providing insights into how models arrive at their outputs. This helps build trust among stakeholders, ensures compliance with regulatory requirements, and enables informed decision-making. Additionally, look for robust model governance features, such as version control, performance monitoring, and auditing, to maintain control over AI deployments and mitigate risks associated with model drift and biases.

The platform should provide advanced model evaluation tools for subjective evaluation of the model and prompt performance across applications, ability to see the evaluation against parameters such as toxicity, bias, completeness, cohesiveness, and factual correctness.

7. Vertical and Domain-Specific Prebuilt Solutions:

To accelerate enterprise adoption of generative AI, it is crucial for platforms to offer vertical and domain-specific prebuilt solutions. These solutions should include pre-trained models, workflows, and integrations tailored to specific industries and business functions, such as healthcare, finance, retail, and customer service. By providing out-of-the-box functionality and industry-specific knowledge, prebuilt solutions can significantly reduce the time and effort required for enterprises to implement and derive value from generative AI. Moreover, these solutions should be built on top of a flexible and extensible platform, allowing organizations to customize and enhance them to meet their unique requirements. An enterprise generative AI platform that supports a wide range of prebuilt solutions across various verticals and domains, along with the ability to easily adapt and extend them, can greatly accelerate the adoption and success of generative AI in enterprise settings.

8. Scalability and Performance:

Enterprise generative AI platforms must handle large-scale deployments, processing vast amounts of data and serving multiple concurrent users. Look for a platform with a scalable architecture that leverages technologies like event-based architectures and stateless horizontal scaling to dynamically adjust resources based on demand. It should also provide advanced performance optimization techniques, such as model compression, quantization, and caching, to reduce latency and improve responsiveness.

The platform should offer flexibility in terms of deployment options, supporting both cloud and on-premises environments to accommodate enterprise security and compliance requirements. This way, enterprises can choose the deployment model that best suits their needs and ensures they remain in control of their data and infrastructure costs.

9. Security and Compliance:

Generative AI often involves processing sensitive enterprise data, so security and compliance are absolutely critical. Choose a platform that adheres to industry-standard security practices, such as sensitive data redaction, data encryption, access controls, and data segmentation. It should also comply with relevant regulations, such as PCI, GDPR, HIPAA, and SOC 2. Look for features such as data lineage, audit logs, and secure data controls to ensure the confidentiality and integrity of your enterprise information.

10. Generative AI Tooling:

Fine-tuned models are crucial for widespread AI adoption in enterprise applications. They enable models to adapt to enterprise data and business rules. A robust enterprise generative AI platform must provide capabilities to fine-tune a wide range of models, including commercial and community models of various sizes, and continuously support new industry models. Fine-tuning tools should support techniques like LoRA, QLoRA, etc. They should be user-friendly enough for subject matter experts to easily bring in enterprise data, clean it, and use it for fine-tuning.

Model evaluation tools should enable collaborative team-level fine-tuning, provide necessary tooling for reinforcement learning with human feedback, and offer easy-to-use features for various collaborators and tasks.

Deploying these models should be seamless in cloud or on-premises environments. The platform should provide sophisticated operational capabilities to automatically provision the right size GPU/TPU machine for each model, ensure redundancy, enable automatic API endpoint deployments with endpoint protection, and scale horizontally with load while provisioning only the required hardware. After all, hosting large language models can be a significant infrastructure cost for enterprises. The platform should handle all these model operations, which require extensive engineering expertise.

11. Empowering Enterprises to Build and Customize AI Models:

To build enterprise-grade generative AI applications, you need a comprehensive set of tools and frameworks for data preparation, model training, testing, and deployment. An enterprise generative AI platform must provide intuitive and powerful tooling that enables developers and data experts to efficiently build, customize, and deploy AI models, without requiring them to be experts in machine learning or natural language processing.

Prompt engineering is a critical aspect of this tooling. It allows teams to collaboratively design, test, and refine prompts to ensure high-quality, relevant, and accurate responses. The platform should also provide tools for fine-tuning pre-trained Large Language Models to create highly specialized and effective generative AI solutions.

Equally important are tools for evaluating model outputs, both objectively and subjectively, at scale. Objective evaluation tools should provide metrics and insights on factors such as accuracy, fluency, diversity, and coherence, while subjective evaluation tools should enable human reviewers to assess the quality, relevance, and appropriateness of model outputs across entire applications.

To facilitate continuous model and prompt optimization, the platform should offer tools that allow teams to collaboratively analyze evaluation results, identify areas for improvement, and efficiently iterate on model architectures, training data, prompts, and fine-tuning strategies. Advanced analytics and reporting capabilities should surface actionable insights from model performance data, user interactions, and business metrics. This way, enterprises can continuously adapt and improve their generative AI models to meet their evolving needs.

12. Enterprise-Grade Support and Expertise:

Choose a platform provider with a proven track record of successful enterprise deployments, domain expertise across industries, and comprehensive support services to ensure a smooth implementation and ongoing success.

Enterprise Generative AI Platform Evaluation Checklist

To help evaluate and compare different generative AI platforms, I have compiled an evaluation checklist based on the key considerations discussed in this article.

For a detailed breakdown of each category and its associated requirements, refer to the Evaluation Checklist.

Conclusion:

Choosing the right platform for generative AI applications is critical for enterprises. It is important to not just invest in today's use cases but also focus on a more comprehensive set of use cases for the next five years. You should talk to industry practitioners, understand the applicable use cases, challenges, and tools required, and carefully evaluate options available in the market to make the right decision that fits your enterprise-specific needs and goals.

While several leading platforms are available, Kore.ai offers a comprehensive solution with a wide range of capabilities for building and deploying conversational AI and generative AI applications. These include no-code development, advanced language model orchestration, advanced knowledge retrieval and answer generation capabilities, model fine-tuning and deployment capabilities, model and prompt evaluation tools with continuous monitoring, and guardrails along with enterprise-grade security and scalability. Moreover, Kore.ai provides a suite of prebuilt vertical solutions tailored to specific industries, such as retail banking, healthcare, retail commerce, and travel, as well as domain-specific solutions for functions like human resources (HR) and IT service management (ITSM). These solutions come with pre-trained models, workflows, and integrations that address common use cases and challenges within each industry and domain, helping enterprises quickly deploy and derive value from generative AI. However, the ultimate decision should be based on a thorough evaluation of your organization's specific requirements and priorities.

By focusing on the critical features highlighted in this article and using the evaluation checklist, enterprises can navigate the generative AI landscape effectively and tap into the full potential of applying generative AI technologies to boost innovation, improve customer experiences, and optimize operations.