Beyond labels. Namsor v3 transforms any proper name into a high-dimensional cultural vector: the missing layer for your AI models in fraud, compliance, marketing, and risk.
"Categorical labels strip away the deep cultural nuances that drive real predictive power. Embeddings preserve them."
Each Namsor embedding is the synthesis of three specialized models running in parallel. Together they capture the full cultural identity carried by a name.
Trained on 14+ billion names. Computes the probability distribution of demographic attributes (gender, origin, diaspora) for any name.
Decomposes each name into roots, prefixes, and suffixes. Recognizes the structural fingerprint across 22+ alphabets, even on unseen spellings.
Leverages LLMs to capture cultural context: associations and meanings beyond pure pattern. Surfaces signals that statistics would miss.
From a self-serve API to fully custom on-premise deployments, pick the integration depth that matches your stack.
Tailor-made AI solutions trained on your data: fraud detection, fake-name flagging, segmentation. Built on Namsor embeddings, ideal for teams without dedicated ML resources.
Plug Namsor embeddings into your churn, LTV, fraud, or forecasting models. A drop-in cultural feature that lifts accuracy and surfaces hidden patterns.
Deploy instant deduplication, lookalike audiences, and identity matching directly in your data warehouse. Mathematical distance, no training required.
Global watchlists like OFAC and Interpol generate massive false positives due to simple spelling similarities, slowing down compliance teams.
Namsor differentiates a sanctioned individual from an innocent homonym by understanding the linguistic identity of each name.
Regulators mandate accurate names. Aliases, emojis, and random strings make verification impossible and trigger costly audits.
Namsor flags non-human signatures and fictional characters at the point of entry, before they pollute the database.
Victims willingly authorize payments (Authorized Push Payment fraud), bypassing traditional transaction filters and exposing platforms to mandatory reimbursement.
Namsor surfaces high-risk transfers in real time, complementing transaction filters with a behavioral risk signal before funds leave the platform.
"Distinguish "Abramovich" (sanctioned Russian) from "Abramowicz" (innocent Polish citizen) automatically."
"A user attempts a transfer or registration using "Mickey Mouse" or "I Love You :)"."
"A first-time international transfer of €2,000 to an unknown beneficiary triggers a soft-block for human review."
Every business is unique. Measure Namsor's impact on yours, in three steps.
Provide historical data: false alerts, churners, customer profiles. No IT integration; a secure file export is enough.
Namsor processes your data in a siloed environment, appending embeddings to surface the cultural patterns your stack misses.
Performance lift measured across accuracy gained, churn prevented, and review time saved, translated into bottom-line impact.
A name embedding is a high-dimensional mathematical representation of a proper name. Beyond categorical labels (gender, origin), it captures the deep cultural identity of a name in a vector space your AI models can directly consume.