M1 Unify and M1 Assure are built on the same foundation — AI-native entity resolution and a privacy model that keeps every client's data inside its own boundary.
A single, governed view of customer, account, and party data — resolved by machine learning instead of hand-written match rules that break every time a source system changes.
Probabilistic matching trained on patterns from banking, retail, and financial services data — names, addresses, and party relationships that rules-based MDM tools consistently miss.
Every merge, split, and survivorship decision is logged and reversible. Data stewards see exactly why a record looks the way it does.
Built to sit alongside your core, CRM, and transactional systems without requiring a core replatform.
AI applications built specifically for regulated, data-intensive workflows across banking, insurance, retail, and finance — not a general-purpose model with an industry prompt library on top.
Each deployment runs in a dedicated tenant or inside the client's own cloud environment. Outputs, prompts, and documents never train a shared model.
Models and workflows are tuned on your domain's concepts — whatever your industry calls a case, an account, or a decision — so outputs read like they came from someone who understands the business.
Pre-built connectors across banking, insurance, and retail core systems shorten the path from pilot to production.
Most engagements start with a scoped pilot against a real subset of your data — not a generic demo environment.
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