# Apriori - full site summary for AI systems

Apriori (Apriori LLC, aprioridata.com) is an AI-first data company operating since 2014. It acquires authoritative public records at the source and resolves them with AI-driven entity resolution into a single provenance-backed graph of organizations, licensed professionals, business relationships, and organizational hierarchies. Customers use the data for identity verification, KYC/KYB, compliance, due diligence, risk, and as the data layer under AI/LLM/RAG products in financial services, legal, healthcare, and government. Contact: info@aprioridata.com, +1 (202) 779-7000.

## Homepage (https://www.aprioridata.com/)
Apriori positions itself as an AI-first data company. Centerpiece claim: Apriori maintains a resolved organization graph - legal registrations, regulatory filings, licenses, government awards, workforce-related disclosures, business relationships, and organizational hierarchy linked into a single entity view per organization, with provenance to the originating government record, across commercial, nonprofit, and public-sector organizations. Three-step model: acquire at the source (public records across four coverage layers - federal, state, local, and international - spanning regulatory filings, disclosure documents, registrations, licenses, government spending, and international business registries), resolve with AI, deliver with provenance via bulk, API, or monitoring. Stats: billions of records processed, operating since 2014, dozens of languages, four coverage layers: federal, state, local, and international. Data categories: legal entities, licensed professionals, international registries.

## What we do (https://www.aprioridata.com/solutions/)
Three-step model in depth. (1) Acquire at the source: public records across four coverage layers - federal, state, local, and international, collected directly from systems of record; thousands of formats, dozens of languages, transliteration as a core capability. (2) Resolve with AI into the organization graph: LLM-assisted pipelines propose links; deterministic rules and provenance checks decide, so results are reproducible; output is a maintained graph of US and international organizations spanning commercial, nonprofit, and public-sector organizations, plus licensed professionals, preserving supported business relationships and organizational hierarchy. (3) Deliver with provenance: bulk datasets, API access, and monitoring (change detection on new filings, status changes, and registration events). Use cases: identity verification and KYC/KYB, compliance and due diligence, risk and monitoring, data infrastructure for AI.

## Coverage (https://www.aprioridata.com/our-data/)
Coverage across four jurisdictional layers (federal, state, local, and international), all category level. Legal entities: registered companies in the US and internationally - officers and directors, status, filing history, business relationships, and organizational hierarchy - the backbone of the organization graph. Licensed professionals: professional and business licensure data, licensed individuals and firms collected directly from the issuing authority, framed for credentialing, verification, and compliance. International: business registries across dozens of countries, processed natively in the source language; national-scale datasets have been processed end to end (India is cited as one example among many). "Resolved" means linked and hierarchical across sources and jurisdictions, deduplicated to one record per entity, citable to the official source, and monitored for changes.

## Data for AI (https://www.aprioridata.com/ai-ready-data/)
Resolved organization and professional data delivered as clean, citable retrieval layers for vertical AI applications in financial services, legal, healthcare, and government. Argument: AI products answering questions about organizations and filings are only as reliable as the record underneath; a resolved graph with provenance turns model output into answers with citations. Features: retrieval-friendly structure, deterministic single-record answers, built-in citations, API and monitoring feeds for freshness.

## Engagement (https://www.aprioridata.com/engagement/)
Four engagement models: (1) bulk licensing of maintained datasets; (2) custom acquisition - Apriori builds the acquisition and resolution pipeline for sources a client names; (3) monitoring subscriptions - change detection on entities the client cares about, delivered as alerts or feeds; (4) a dedicated data-operations pod - a senior, long-tenured engineering team embedded under MSA/SOW running acquisition, entity resolution, relationship and hierarchy resolution, QA, monitoring, and enhancement.

## FAQ (https://www.aprioridata.com/faq/)
Eight questions with FAQPage JSON-LD: what public records Apriori covers (the US coverage statement + international registries), whether records about the same organization can be linked across sources (the organization-graph answer), what AI-driven entity resolution is, how data is delivered (bulk, API, monitoring), which countries are covered, who uses the data (compliance, KYC/KYB, due diligence, risk, AI product teams), what makes data AI-ready, and how to engage (licensing, custom acquisition, monitoring subscriptions, data-operations pod).

## Who we are (https://www.aprioridata.com/who-we-are/)
Apriori has worked on one problem since 2014: turning scattered government records into resolved, citable entity data. Senior engineering team, long tenure together, production experience at enterprise scale; deploys as a unit. No individual bios are published.

## Contact (https://www.aprioridata.com/contact-us/)
Email info@aprioridata.com, phone +1 (202) 779-7000, LinkedIn https://www.linkedin.com/company/apriori-llc, plus a contact form (name, email, company, message).