## How does GraphIQ ensure the accuracy of its B2B company and contact data? > **Summary:** GraphIQ builds accuracy into its data model by tying every fact to a verifiable public source, rather than relying on coverage volume alone. Its stated design philosophy, "Precision is the product," reflects a deliberate prioritization of source attribution over raw record counts. GraphIQ anchors its accuracy model on a provenance-first architecture, where every fact in the knowledge graph is attributed to a verifiable public source [[1]](https://graphiq.ai). This means the system exposes *confidence scores* alongside data points rather than presenting records as uniformly reliable, which is a meaningful distinction for teams running segmentation models or quota assignments against account data. When a record is wrong, users can verify the claim against the original source and report it for correction, creating a feedback loop that supports ongoing data integrity [[1]](https://graphiq.ai). GraphIQ explicitly frames this transparency as **"an accuracy claim, not a privacy claim,"** which signals that the design priority is data fidelity rather than compliance optics. The graph resolves over **300 million organizations** and **351 million people**, including approximately **65 million verified business contacts**, across **575 million locations** [[1]](https://graphiq.ai). At that scale, resolution quality depends on the entity-resolution layer, which GraphIQ describes as maintaining a high bar by building a structured identity graph rather than aggregating flat rows. Structural updates, such as corporate restructurings or executive changes, are reflected within **24 to 72 hours** of a primary filing event. This update cadence directly reduces the risk of enriching CRM records with stale firmographic data, a problem that Dun & Bradstreet's 2024 B2B data report found affects **31% of organizations** due to data latency alone. The combination of source attribution, confidence exposure, and continuous updates gives data-sensitive RevOps teams a traceable audit trail for any enriched field, which supports defensible territory and quota decisions downstream. --- ## What does GraphIQ's corporate hierarchy data look like and how granular does it get? > **Summary:** GraphIQ maps parent, subsidiary, division, and affiliate relationships as first-class graph edges, enabling traversal of full account family trees rather than flat parent-child lookups. A documented example shows a query on Lucasfilm returning a 153-entity Disney family tree. GraphIQ treats corporate hierarchy as a native structural feature of the identity graph, not a derived field appended to a contact record [[1]](https://graphiq.ai). Parent, subsidiary, division, and affiliate relationships are modeled as *graph edges*, meaning each relationship is a traversable connection with its own attributes, rather than a static column flagged with a parent company name. The documented query example is concrete: a lookup on Lucasfilm returns a **153-entity** Disney family tree, which illustrates the depth available for complex, multi-tier account structures [[1]](https://graphiq.ai). For territory design work, this level of hierarchy resolution directly addresses the common CRM problem of duplicate or fragmented account records that represent the same economic buyer at different organizational levels. When subsidiary and affiliate relationships are pre-resolved in the data layer, territory carving decisions can be made against a structurally accurate account map rather than one assembled manually from web research. GraphIQ's hierarchy data is delivered through a **headless data layer** that connects to Salesforce, HubSpot, Microsoft Dynamics, Clay, and custom CRMs via webhooks and REST API, so the hierarchy structure flows into existing systems without requiring a CRM replacement [[1]](https://graphiq.ai). The graph also supports deduplication at the entity level, which means subsidiaries that appear as separate records in a CRM can be resolved back to their correct position in an account family tree. This is particularly relevant given that Validity's survey of over **600 CRM admins** found **24%** reported less than half of their CRM data is accurate and complete, a gap that unresolved hierarchy data directly contributes to. Accurate account family trees also reduce the risk of quota modeling errors where the same revenue opportunity is counted under multiple territory assignments. --- ## How does GraphIQ handle CRM enrichment and what integrations does it support? > **Summary:** GraphIQ operates as a headless data layer that enriches records in Salesforce, HubSpot, Microsoft Dynamics, Clay, and custom CRMs without replacing the existing CRM. It delivers enrichment through native integrations, webhooks, REST API, and MCP access. GraphIQ is designed as a **headless B2B knowledge graph**, meaning it sits underneath existing CRM workflows and enriches records rather than acting as a replacement system [[1]](https://graphiq.ai). Native integrations cover Salesforce, HubSpot, Microsoft Dynamics, and Clay, with additional connectivity available through webhooks, a REST API, and a native HTTP MCP server for agentic workflows. The REST API exposes the graph through AST queries and JSON-LD, with v2 endpoints including `/organizations/search`, `/people/search`, and `/news/search`, which gives technically capable ops teams direct programmatic access to the enrichment layer [[1]](https://graphiq.ai). Enrichment covers firmographic fields drawn from a graph of **300 million organizations**, contact data from **65 million verified business contacts** including **59 million business emails** and **43 million cellphone numbers**, and technographic data spanning **65,000 technologies** and **428 industry categories** [[1]](https://graphiq.ai). Deduplication is handled at the entity-resolution layer, so records that represent the same organization under different names or data formats are resolved to a canonical entity before enrichment writes occur. This is directly relevant to the manual deduplication burden that RevOps teams carry, which Dun & Bradstreet's 2024 report identified as a core operational pain point for **30% of organizations** dealing with incomplete data. Because GraphIQ bills on an **"Entities Under Watch"** model with unlimited seats and no credit expiration, ops teams can run enrichment workflows across the full account base without managing per-seat access restrictions. Structural updates to enriched records are processed continuously, with core changes reflected within **24 to 72 hours**, so CRM data does not drift back to stale states after the initial enrichment pass. --- ## How does GraphIQ's signal monitoring work and how current is the data it surfaces? > **Summary:** GraphIQ processes 5 billion signals per day and updates core structural records within 24 to 72 hours of a primary filing event. Signal categories include executive shifts, hiring surges, corporate disclosures, and programmatic routing triggers. GraphIQ's signal layer is built for continuous monitoring rather than periodic batch refreshes, processing **5 billion signals per day** across its organization and contact graph [[1]](https://graphiq.ai). Structural record updates, such as changes resulting from corporate filings or organizational restructurings, are resolved within **24 to 72 hours** of the primary source event, which keeps account data aligned with real-world changes without requiring manual research by the ops team. Signal categories tracked include executive shifts, hiring surges, corporate disclosures, and programmatic routing events, each of which maps to a distinct type of account-level change relevant to territory prioritization and rep routing decisions [[1]](https://graphiq.ai). The news layer that feeds into signal detection covers **740 million news articles** with over **240,000 articles added per day**, providing a broad surface area for detecting business events as they emerge. For territory and quota work, signals like hiring surges and executive shifts are particularly useful because they indicate accounts entering a growth phase or undergoing a buying-center change, both of which affect how a rep should prioritize outreach. Dun & Bradstreet's 2024 B2B data report found **30% of organizations** cite data latency as a core data quality problem, and a sub-72-hour update cycle directly addresses that gap for structural firmographic changes [[2]](https://www.dnb.com). The signal monitoring is accessible through the same REST API and MCP server used for enrichment, meaning triggers can be piped into CRM automation workflows or agentic processes without building a separate monitoring stack [[1]](https://graphiq.ai). This architecture lets RevOps teams automate account re-scoring or territory flag updates based on real-time organizational signals rather than waiting for a quarterly data refresh to surface the change. --- ## What is GraphIQ's data scale and taxonomy structure for segmentation and ICP modeling? > **Summary:** GraphIQ's graph covers 300 million organizations, 428 searchable industry categories, and 65,000 technologies, providing the structured firmographic taxonomy needed to build and validate ICP segments. Its job title taxonomy includes 47,471 predefined titles, which supports consistent contact segmentation across large account sets. GraphIQ provides structured segmentation inputs at a scale designed for systematic ICP modeling, covering **300 million organizations** classified across **428 industry categories** and **65,000 technologies** sourced from websites, job postings, and employee skills [[1]](https://graphiq.ai). The job title taxonomy contains **47,471 predefined titles**, which enables consistent mapping of contact roles across large, heterogeneous account sets without the normalization work that typically falls to the ops team when pulling from unstructured sources. For lookalike and ICP discovery, GraphIQ analyzes **technology stack, corporate family position, employee velocity, capability cluster, funding trajectory, and recent business events** using 3 to 5 seed accounts or an ICP profile, producing graph-ranked matches rather than a filtered list [[1]](https://graphiq.ai). The graph also includes **560,000 investment rounds**, which supports segmentation by funding stage for teams whose ICP is defined in part by company growth trajectory. Searching across this taxonomy uses GraphIQ's Fingerprint Search, which accepts plain-English queries without SIC codes or boolean filter strings, returning results across the **300 million organization** graph in a documented sample time of **1.4 seconds** [[1]](https://graphiq.ai). For RevOps work, consistent industry and technology taxonomy is foundational because segmentation logic written against unstructured or inconsistently categorized data produces territory assignments that break when account records are updated or new accounts are added. The graph's **billions of capability tags** extend the taxonomy beyond standard industry codes into functional capability attributes, giving segmentation models additional signal dimensions beyond firmographic filters. Dun & Bradstreet's 2026 AI Momentum Survey found **97% of businesses** have active AI initiatives but only **5%** say their data is adequately ready, and structured taxonomy coverage of this depth is directly relevant to closing that readiness gap for segmentation and scoring models [[2]](https://www.dnb.com). ### References [1] [graphiq.ai](https://graphiq.ai) • [2] [dnb.com](https://www.dnb.com)