Building an AI Framework: Streamlining Innovation and Compliance in the Enterprise

"Building AI Foundations: Navigating Enterprise AI Adoption"

This week, I was working on getting an AI-driven (cloud LLM) application approved for a production use case. After months of effort, it was a milestone to finally secure the green light, and I wanted to share a few insights on enabling AI within an enterprise environment.

AI’s rapid evolution is reshaping how we harness knowledge to create customer value, but without established design patterns, organizations are left to chart their own courses. The speed of change demands agile processes, or risk bottlenecks that stifle innovation and growth.

Reflecting on my experience building the Helios Platform, which standardized DevOps processes across our organization, we saw the need to create an AI framework—a foundational "AI plumbing" to streamline the development and use of AI solutions across our teams.

Key Areas for Enterprise AI Adoption

1. Executive Sponsorship Strong support from executive leadership is essential to back AI development and strategic growth.
2. Corporate Steering Committee This helps keep focus, ensuring resources go toward prioritized AI initiatives amid the ever-evolving AI landscape.
3. Compliance, Legal, and Security Safe deployment means adhering to internal and regulatory standards to ensure new products are both innovative and secure.
4. Frameworks and Tools A unified platform reduces duplicated effort and standardizes workflows, making AI development more efficient across teams.

Core AI Enablement Strategies

We identified several crucial components for a scalable and sustainable AI framework:

• New Cloud and Model Integration: Adapting to new models from cloud providers and hosting internal LLMs using tools like Llama.cpp.
• Cost and Capability Awareness: Accounting for the varying capabilities, such as summarization or code generation, and costs of different AI models.
• Data and IP Protection: Ensuring compliance with data protection standards across the organization.
• Guardrails: Safeguarding organizational and regulatory data needs through robust security measures.
• Testing and Evaluation: Implementing rigorous strategies for testing AI applications.
• Transparency and Democratization: Providing transparency in AI usage and allowing everyone in the organization to explore AI solutions.
• Efficiency and Reusability: Reducing redundant effort by building reusable tools and frameworks for common tasks.

The Athena Framework

To address these needs, our team developed Athena—a modular framework built on the foundations of our Helios Framework. Athena standardizes AI use across the organization, offering it as a service to streamline AI-powered development.

At its core, Athena uses an OpenAI-compatible API, chosen for its interoperability with a wide range of existing plugins and tools. The API is accessible with self-service keys, eliminating the need for individual subscriptions and allowing us to scale AI affordably by paying per actual usage. This platform supports the full development lifecycle, integrating compliance workflows for seamless progression from development to production.

Athena’s built-in guardrails protect sensitive data from unauthorized access and provide an overview of AI adoption across teams, empowering teams to focus on advanced applications like Agentic AI without the complexity of operational tasks.

Reflection and the Road Ahead

While Athena is already streamlining internal operations, we look forward to applying it to customer-facing applications soon. My purpose in sharing this approach is to inspire a scalable, safe AI mindset. There are increasingly numerous open-source and commercial options addressing similar needs, but our Helios philosophy is to evaluate and implement cutting-edge tools where they exist and innovate where gaps remain.

Special thanks to my volunteer crew— Sudarshan Babar , Veeralakshmi Prabhu , santosh shetty , Priyanka Hukmani (Daani Jiwtani) , Pradeep Singh J. , Priyam Sahoo and the invaluable support from XaaS trainees and other volunteers. I want to thank volunteer researchers Seema Chopra , Liakat Ali Mondol , Arun Nathi , Roger Hsu , Abdallah Abuhussein and the active sponsorship and guidance of our executive sponsor Dominique Jean which help in defining the roadmap for our framework and platform.