Enterprise Generative AI Framework: Establishing AI Excellence at Industry Scale

The major US airline is the world's third-largest airline, operating one of aviation's most asset-intensive, safety-sensitive, real-time networks. The a large application estate is anchored by a large microservice substrate, with thousands of apps involved in dispatching a single aircraft and hundreds of unique services triggered by one boarding pass. Loyalty (their loyalty program with tens of millions of active members), traveler commerce (their advertising platform, hundreds of millions of unique travelers, millions of daily website visitors), and a multi-year applied-AI program sit on top of the core airfare business. Taller's engagement at this airline has been continuous since March 2023, sourced through a staffing partner. The dominant footprint spans four AI / data workstreams that Volume II captures plus two .NET / Angular modernization workstreams. Multi-year SOW extensions have been confirmed across cohorts. Taller engineers placed at this airline have earned strong client-side recognition, with client stakeholders reporting strong satisfaction with team performance.

The airline's strategic ambition was becoming an applied-AI leader in transportation. That ambition requires a unified framework that any internal team can build against, with the operational discipline (governance, observability, evaluation) that converts experimentation into deployable capability.

Taller helped design and develop the reusable generative-AI framework, which is a foundation for agent development, multi-agent workflow orchestration, and graph-based RAG that supports deployment across diverse internal projects. The architectural commitment that makes a framework load-bearing rather than aspirational is treating agents as first-class deployment units: each agent has its own versioned manifest, knowledge-base scope, action groups, and evaluation gates. The framework provides the orchestration spine (LangGraph), the retrieval substrate (graph-based RAG with re-ranking), the LLM provider abstraction (AWS Bedrock plus other model routes when latency or cost requires), and the knowledge-graph layer that lets agents share structured context without leaking domain language across boundaries.

The framework supported generative AI deployment across diverse internal projects within twelve to eighteen months, establishing the foundation that supports industry leadership in applied AI across transportation.

Operating the substrate that converts isolated pilots into firm-wide capability is the strategic property that distinguishes an AI-aware company from an AI-led company. The airline's positioning as a generative-AI reference in transportation is a downstream effect of the framework decision, and the framework itself is the reusable asset that lets every subsequent AI investment compound rather than start from scratch.