Executive Summary

Many enterprises—sales, finance, HR and operations teams at mid-market and larger companies—struggle with fragmented, duplicate and inconsistent entity records across dozens of SaaS systems, ERPs and data lakes; estimate addressable demand at roughly 200,000 enterprises with an average $100K ACV implies a $20B market for master data and matching solutions. The pain shows up as lost revenue, billing errors, poor reporting, and failed compliance efforts, and it is typically owned by data governance, revenue operations, and IT leaders who face long, manual reconciliation cycles and brittle rule-based tools. The product to build is an AI-assisted entity-resolution and schema-mapping platform that combines semantic embeddings and LLM-guided mapping with deterministic rules, human-in-the-loop validation, pre-built connectors, and an auditable merge workflow so businesses can consolidate identities and canonicalize records in weeks rather than months. This moment is favorable: continued SaaS proliferation increases fragmentation, embeddings and LLMs reduce expensive feature engineering, and rising regulatory and internal governance mandates make unified records a board-level priority—together supporting the revenue potential and the 92/100 market score. To stand out you should prioritize explainability, compliance-grade audit trails, verticalized pre-trained models, fast connectors to common systems, and pricing that aligns incentives (e.g., outcome-based or phased pilots), while acknowledging real challenges: integration complexity, the need for reliable ground truth and labeled data, model drift, and typically long enterprise sales cycles. If you can demonstrate measurable ROI—revenue recovery, reduced churn or compliance risk reduction—within a 3–6 month pilot, the economics suggest a viable path, but expect nontrivial investment in deployment automation, security, and change management.

Market Opportunity

Entity-resolution for messy business data — AI-assisted matching & mapping targets a $20.0B = 200k enterprises x $100K ACV (global MDM / enterprise data-matching demand) total addressable market with medium saturation and a year-over-year growth rate of 12% CAGR (MDM & data integration markets).

Key trends driving demand: SaaS proliferation -- more apps create fragmented records that need matching across systems, increasing demand for reconciliation tools.; Advances in embeddings/LLMs -- semantic similarity models reduce feature engineering for fuzzy matching and enable quicker deployment.; Compliance & data governance -- regulations and internal governance initiatives push companies to unify identities and maintain clean records.; Cloud data stacks standardization -- adoption of Snowflake, BigQuery, and unified catalogs makes connector-driven solutions easier to integrate..

Key competitors include Tamr, Informatica (MDM & Data Integration), Dedupe (open-source library) / dedupe.io (hosted), Spreadsheets + SQL + Alteryx / OpenRefine (workarounds).

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Entity-resolution for messy business data — AI-assisted matching & mapping

Executive Summary

Market Validation

Market Opportunity

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