Centralized Aviation Knowledge Base Saves 8 FTE (20%) and Speeds Support

Context

The client is a global transport and aviation company operating 24/7 technical support hubs that cover a broad aviation product portfolio. The project objective was to improve the speed, consistency, and quality of technical support responses provided by these worldwide technical support teams, while simultaneously strengthening and scaling the organization’s internal expert knowledge so subject-matter experts (SMEs) could resolve cases more reliably and more quickly.

Challenges

Customers submitted technical requests through calls, web platforms, and mobile devices, but rising request volumes, expanding product diversity, and increasing case complexity made consistent, rapid response more difficult. New technical content was added daily, creating risk that response quality would degrade over time, that irrelevant or outdated solutions would surface, and that there would be limited auditability of the source information that led to case resolutions. Information available to SMEs was dispersed across multiple data sources, systems, and formats, written in several languages, inconsistent in structure and depth, and often missing details for outlier or rare cases. In a safety-critical aviation context, SMEs were understandably reluctant to rely fully on model-assisted outputs: trust hinged on accuracy, clarity of sources, and the ease with which recommendations could be validated.

Implementation

The implementation produced a decision-support solution that helps SMEs quickly identify similar historical issues and potential resolutions by returning a priority-ranked list of relevant historical claims rather than a single opaque answer. The architecture integrated a data collection module, a retrieval component, a structured knowledge base, and a scoring algorithm that evaluates both similarity and contextual relevance to rank candidate cases.

A centralized technical-support knowledge base was built to consolidate dispersed content. A Python module automated claims ingestion and translation so multilingual records could be normalized into a common working language. Topic modelling extracted structured metadata (product type, time in service, environment, issue category, root cause, and resolution) to improve search precision and reuse. For records with incomplete metadata, NLP-based completion provided similarity-based suggestions for missing fields; these suggestions were clearly labelled to maintain transparency and avoid confusion with verified sources.

Ranking relied on vector similarity metrics and multiple tested distance measures to improve accuracy for different case types. To maintain traceability and support audits, automated logs captured key details about each search and each case resolution. Quality controls were embedded in the workflow: SMEs rated the usefulness and relevance of returned results, and that feedback was fed back into the ranking model alongside similarity metrics and contextual factors. Aggregated feedback dashboards monitored quality trends over time so the support organization could detect degradation, bias, or the surfacing of outdated solutions.

To build SME confidence, the project team ensured that original source material was always accessible alongside similarity scores and contextual indicators. The system emphasized human-in-the-loop decision-making: SMEs retained full control of final resolutions while AI acted as a research accelerator rather than a replacement.

Results

Operational efficiency improved measurably. The solution supported 40 SMEs and delivered the equivalent of 8 full-time employees (20%) in time savings through reduced search time, faster issue resolution, and improved knowledge reuse. Customer surveys indicated a measurable increase in satisfaction driven by faster response times and higher quality, more consistent technical answers — improvements that translate directly into safety and operational reliability in the aviation environment.

Beyond immediate efficiency gains, the centralized knowledge base and feedback-driven ranking process provided stronger auditability and a scalable foundation for long-term knowledge management. By surfacing ranked, source-linked historical cases and preserving SME oversight, the program balanced acceleration with the traceability and verification demanded in a safety-critical industry, enabling more consistent, faster, and better-documented technical support worldwide.