Case Study: AI Metadata Extraction Agent for SG Library
Automated metadata enrichment pipeline for document collections—improving quality, search and throughput.
The Problem
Manual tagging slows scale and yields uneven metadata quality.
Manual Tagging Overhead
Staff spend excessive hours labeling documents.
Inconsistent Metadata Quality
Human variation results in uneven field coverage.
Low Discoverability
Poor metadata reduces search relevance and click‑through.
Slow Ingestion Throughput
Pipeline cannot scale to growing document volumes.
Our Solution
Automated extraction, normalization and search relevance scoring pipeline.
Automated Entity Extraction
LLMs identify key phrases, entities and relationships.
Schema Normalization
Validation layer enforces required fields & formats.
Search Relevance Scoring
Boosts ranking using enriched metadata signals.
Parallel Ingestion Pipeline
Processes batches concurrently to scale throughput.
Results
80% Fewer Tagging Hours
Bulk automation replaces manual labeling effort.
62% → 94% Completeness
Metadata field coverage raised dramatically.
45% Higher Search CTR
Improved metadata boosts discovery and engagement.
3× Ingestion Throughput
Processing speed scaled from 1k to 3k docs/hour.