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

Enterprise entity resolution - ML, graph canonicalization and human-in-loop targets a $12.0B = 120,000 mid-large enterprises x $100K ACV. Rationale: global firms that must maintain canonical customer/supplier/entity records invest in MDM and data quality, typical enterprise ACV for robust ER solutions is in five figures to low six figures. total addressable market with medium saturation and a year-over-year growth rate of 15% estimated MDM and data ops growth driven by automation and AI investment.

Key trends driving demand: embeddings-and-vector-search -- enables fuzzy, semantic candidate retrieval across noisy enterprise records that traditional blocking misses; enterprise-automation -- as organizations automate workflows, reliable canonical entities become a gating factor for deployable automation; LLM-contextual-disambiguation -- large models can use broader context to resolve ambiguous short records, improving candidate scoring; data-regulation-and-kyc -- compliance and audit requirements increase demand for auditable canonical entity graphs.