Enterprise entity resolution - ML, graph canonicalization and human-in-loop

Executive Summary

Entity resolution breaks enterprise automation because it requires cross-document linking and canonical IDs, not just token classification. Offer a SaaS that combines embeddings, graph-based linking, connectors and human-in-loop review to cut false matches and manual work. The article is part of an enterprise AI automation series emphasizing that ER is a blocker to reliable automation. Recent shifts make this tractable: dense embeddings and vector DBs let you generate robust fuzzy candidates, LLMs enable context-aware disambiguation of short records, and cheaper cloud compute makes continuous graph linking practical. Upstream validation also shows recurring monthly workflows and identifiable budget owners, so a subscription model with connectors and SLAs fits current buying patterns. The source notes that entity resolution is harder than NER because it requires cross-document linking, canonical IDs and handling fuzzy, evolving matches rather than token classification. Use modern embeddings and vector search to surface candidate matches, build and persist canonical entity graphs as proprietary customer assets, and layer a lightweight human-in-loop labeling and active learning workflow so accuracy improves over time. Combine prebuilt connectors to CRMs, ERPs and data lakes to capture recurring monthly reconciliation workflows and make the product a subscriptioned infrastructure component rather than a one-off project.