Employee Productivity AI: Content Generation and Intelligent Retrieval at 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 (employee productivity AI on LLM + RAG, the aircraft manual comparison RAG system for compliance, the loyalty program customer-care self-service analytics, and the enterprise generative-AI framework supporting multi-agent and graph-based RAG deployment) plus two .NET / Angular modernization workstreams that Volume II does not yet capture (Crew Scheduling and the Customer Care Services business-rules-engine rewrite). 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 workforce productivity bottleneck was content generation and information retrieval. Knowledge that lived in manual workflows took hours of skilled time to extract, package, and put in front of a decision-maker, and the operational cost of that latency compounded across the airline's broader employee base. The strategic problem is converting the airline's knowledge corpus from a passive document store into an active assistant that responds at the cadence operations actually requires.

Taller's Senior LLM engineers built the AI-powered productivity solution combining large language models with retrieval-augmented generation. The engineering pattern that earns the outcome at production scale is the integration of three distinct retrieval strategies into one orchestrated response path: hybrid search combining lexical and dense-vector retrieval for domain terminology, multi-query retrieval rewriting questions into several phrasings to compensate for vocabulary mismatch, and re-ranking methods that prioritize the most contextually relevant passages before they reach the model. LangChain orchestrates the inference; LangGraph manages multi-step agent state for workflows needing more than a single retrieval hop; an automated RAGAS evaluation pipeline gates every model output before delivery; React with WebSocket streaming surfaces responses progressively.

The solution delivered content generation and data retrieval time collapsed from hours to seconds, with measurable lift in employee engagement on the affected workflows and improved operational efficiency across the airline's content-and-retrieval surfaces.

Production AI at the employee-workflow layer is the operational substrate behind the airline's broader generative-AI ambitions. The productivity AI deployment is what proves the AI investment will translate into employee adoption rather than only into pilots — the precondition for the airline's broader framework investments to compound into industry-leading capability.