Aviation & Logistics · Flight Operations Analytics
Adaptive Retrieval for Flight Operations: Analytics Questions, Answered Conversationally
A conversational assistant that answers complex operational questions in seconds
The Client · A major North American airline and a member of one of the leading international airline alliances
Overview
The airline engaged Taller to build a conversational AI assistant for its operations organization, whose analysts could not quickly cross-reference operational and analytics data.
The Problem
The airline's engineers and analysts faced limitations in deeply cross-referencing analytics and operational data, meaning that every question required slow, manual work across multiple sources, driving up operational costs and introducing errors. The frustration was strategic: an operations organization that cannot interrogate its own data quickly cannot reduce costs or improve productivity at the pace the business demands.
The Solution
Taller built a conversational analytics assistant for flight operations. Because users asked an unpredictable variety of questions, the team designed a custom adaptive retrieval process tailored to the question set: for each question, the system pulled the relevant documents from vector databases (which index information by meaning, enabling semantic search), took the conversation history into account so users could ask follow-ups, and collected user feedback on every response. The assistant was orchestrated with LangGraph (a framework for building multi-step AI workflows) running over large language models, and deployed on Kubernetes through automated CI/CD pipelines. A tight team — three engineers, a product manager, and a technical leader — worked in daily contact with the business, a cadence that proved decisive in shaping what the assistant needed to answer.
The Impact
Getting an analysis now takes markedly less effort. Questions that once required manual cross-referencing across operational and analytics data are answered in a single conversational session, with follow-ups. The client is satisfied with the solution and is already planning the next step: integrating the assistant with another existing application via API. That planned integration turns a successful assistant into operations infrastructure — a query layer other applications can build on.
