AI-Powered Search and Enrichment Platform for Digital Marketplace Optimisation

The challenge

The organisation relied on manual and batch-based processes to ingest and enrich marketplace listings, resulting in delays, inconsistent metadata quality, and limited search relevance. Existing search capabilities lacked real-time updates, scalable ranking, and the ability to support advanced filtering and faceting, making it harder for users to find relevant results efficiently. There was also a need for a governed, production-ready architecture that could integrate securely with the website, automate enrichment workflows, and provide operational visibility across performance, cost, and system health.

The approach

Designed and implemented a cloud-native AI search solution using Microsoft Azure and modern AI services to combine hybrid retrieval, automated enrichment, and event-driven indexing. The platform processes listing and image updates through triggers and queues, applies AI models to enrich and validate metadata, and uses confidence scoring, fallback logic, and conflict resolution to improve consistency and control. The solution was integrated with the website through search APIs and included support for filtering, faceting, pagination, and analytics. CI/CD pipelines, Infrastructure as Code, monitoring, alerting, and governance controls were also established to enable secure, repeatable, and scalable production operations.

The outcome

Delivered a production-ready AI-powered search platform that improved search relevance, data freshness, and operational efficiency across a digital marketplace environment. Automated ingestion and enrichment reduced manual effort while increasing consistency and responsiveness as listings changed. The solution enabled better product discoverability, stronger operational governance, and a secure foundation for future AI-driven capabilities such as intelligent recommendations, advanced analytics, and further search optimisation.