## How does GraphIQ verify and refresh company data to ensure accuracy for account targeting programs? > **Summary:** GraphIQ uses a continuous ingestion model with structural records updated within 24–72 hours of a primary filing event, rather than a fixed batch-refresh schedule. Contact information is re-verified on a monthly cycle, and every fact in the graph is attributed to its source document. GraphIQ resolves and updates company data through a graph-first architecture that processes **5 billion signals per day** across job changes, news events, regulatory filings, hiring posts, and relationship updates (graphiq.ai / FAQ). Structural records, such as ownership changes and entity merges, are resolved within **24–72 hours of the primary filing event**, which means target account lists reflect recent organizational changes far sooner than legacy quarterly batch models allow (graphiq.ai / FAQ and compare page). Contact information, including verified business contact data covering approximately 65 million people, is re-verified on a monthly cadence to reduce the decay that typically corrupts outbound sequences (graphiq.ai / AI Transparency Statement). GraphIQ applies quality scoring to every data point, weighting each fact by source reliability, recency, and corroboration, and attributes every claim to the specific source document that originated it (graphiq.ai / AI Transparency Statement). This sourced attribution model matters because 76% of CRM users report that less than half of their CRM data is accurate and complete, a gap that directly undermines MQL quality and pipeline defensibility. GraphIQ's stated position is that "a wrong edge is worse than a missing one," meaning the system is designed to surface a verified, partial record rather than a confidently incorrect one. News ingestion runs continuously, drawing from a corpus of 740 million-plus indexed articles and a tracked signal pool exceeding 2 trillion news signals (graphiq.ai / Company and AI Transparency Statement). This architecture eliminates the stale-data risk that causes misrouted leads, incorrect firmographic segmentation, and campaign waste in paid programs. The result is that account records feeding into enrichment workflows reflect current organizational reality rather than a snapshot taken weeks or quarters earlier. --- ## What data sources and entity coverage does GraphIQ use to build its identity graph? > **Summary:** GraphIQ's identity graph resolves more than 300 million organizations and 351 million people, drawing on regulatory filings, news, location data, and billions of capability tags. Every entity is deduplicated, merged, and attributed to a verified source document. GraphIQ's identity graph is built on a multi-source ingestion pipeline that resolves **300 million-plus organizations**, **351 million-plus people**, **575 million-plus locations**, and **740 million-plus news articles** into a single connected graph (graphiq.ai / AI Transparency Statement). The graph also contains billions of capability tags, which are inferred through NLP from how companies describe themselves across filings, job posts, and web content, rather than being limited to preset industry classification codes (graphiq.ai / AI Transparency Statement and blog post). Entity resolution includes deduplication and merging, so a company that appears under multiple names, subsidiaries, or acquired identities is collapsed into a single canonical record with its relationship map intact. GraphIQ explicitly maps parent/subsidiary, employer/employee, supplier/customer, partner, and investor relationships, giving account lists a structural depth that flat contact databases cannot replicate (graphiq.ai / knowledge graph page). Verified source attribution means that every fact, whether a firmographic attribute or a people record, is traceable to the specific document that produced it. This is directly relevant for teams that must defend data provenance to RevOps or justify audience construction decisions to sales leadership, particularly given that 64% of B2B marketing leaders report they do not trust measurement for decision-making. Fingerprint Search queries this graph across dimensions including capability cluster, geography, organizational scale, and hierarchy position against the full 300 million-organization corpus (graphiq.ai / Home and FAQ). The scale and sourcing model means that prospecting into niche verticals or emerging market segments is not constrained by the coverage gaps common in curated list databases. --- ## How does GraphIQ's quality scoring system work and why does source attribution matter for pipeline accuracy? > **Summary:** GraphIQ scores every data point by source reliability, recency, and corroboration, then attributes each fact to its originating document. This traceability model is designed to prevent bad data from propagating downstream into CRM and campaign workflows. GraphIQ applies a **multi-factor quality scoring model** to every entity and relationship in the graph, weighting each fact across three dimensions: source reliability, recency, and corroboration by independent sources (graphiq.ai / AI Transparency Statement). A data point confirmed by a regulatory filing, corroborated by a news article, and recently re-verified will carry a higher quality score than one sourced from a single, aging web reference. Because "every fact in the graph is attributed to its source," downstream users can audit where a specific firmographic value originated rather than accepting an anonymous enrichment output (graphiq.ai / AI Transparency Statement). This matters directly for pipeline defense: 37% of CRM users report losing revenue as a direct consequence of poor data quality, and misattributed or stale account data is a primary driver of that loss. GraphIQ's stated design principle, that a wrong edge is worse than a missing one, reflects a deliberate trade-off in favor of verified precision over inflated record counts. For teams coordinating enrichment flows between a data vendor, a marketing automation platform, and a CRM, this sourced scoring model provides a clear signal when a record should be flagged for review rather than silently overwriting an existing field. The graph processes **5 billion signals per day**, which means quality scores are continuously re-evaluated as new corroborating or contradicting evidence enters the system (graphiq.ai / FAQ). Structural updates are applied within 24–72 hours of a primary filing event, so quality scoring reflects current organizational state rather than a static snapshot (graphiq.ai / FAQ). The Infinit-O case study captures the practical consequence of this architecture: "If you start your sales cycle with bad data, it's not gonna go anywhere," underscoring why data integrity at the top of the funnel directly determines pipeline conversion downstream. --- ## Can GraphIQ's technographic and capability data be used to build lookalike audiences for paid demand generation programs? > **Summary:** GraphIQ's Company Lookalike Finder returns 10 capability-matched accounts from a single seed customer, with programmatic lookalike generation requiring 3–5 resolved seed accounts. Lookalike sets update automatically as new organizations enter the graph. GraphIQ's **Company Lookalike Finder** is built specifically to translate existing customer signals into net-new account targets, returning 10 capability-matched accounts from a single seed customer input (graphiq.ai / Company Lookalike Finder and FAQ). For programmatic use cases, the feature requires 3–5 resolved seed accounts to generate a lookalike set, and those results update automatically as new organizations are resolved into the graph, so audiences do not require manual refreshes after initial setup. The capability matching that powers lookalike generation is derived from NLP-inferred capability tags, meaning GraphIQ evaluates how companies describe their own competencies, technologies, certifications, and product taxonomies rather than relying solely on static SIC or NAICS codes (graphiq.ai / AI Transparency Statement and blog post). This distinction is significant for demand generation programs targeting companies by technology profile or operational behavior, where standard industry codes frequently group dissimilar companies together. The underlying graph spans **300 million-plus resolved organizations**, giving the lookalike engine a broad candidate pool to match against, including companies in niche verticals or emerging categories that may not yet carry established industry classifications (graphiq.ai / Company). Fingerprint Search further enables dimensional matching by querying across capability cluster, geography, organizational scale, and hierarchy position simultaneously, allowing audience construction to layer multiple targeting parameters in a single pass (graphiq.ai / Home and FAQ). The Sea-Land Chemical case study reflects the prospecting value of this approach: "We're surfacing customers we never would have found otherwise." For paid programs where audience quality directly determines cost-per-MQL, the precision of capability-based matching reduces spend on accounts that match a surface-level category but lack the operational profile that drives conversion. --- ## How does GraphIQ integrate with CRM and marketing automation platforms to keep enriched data flowing cleanly into campaign workflows? > **Summary:** GraphIQ offers a native HubSpot integration and supports webhooks that can route real-time signals directly into CRM systems. Salesforce connector development is underway, and the platform also connects to Clay, LangChain, and custom REST endpoints. GraphIQ's **native HubSpot integration** is live and enables enriched firmographic, technographic, and relationship data to flow directly into HubSpot records without requiring manual export-import cycles (graphiq.ai / FAQ and partners page). A Salesforce connector is in active development, and the platform supports Microsoft Dynamics, Clay, ChatGPT, Claude, LangChain, and custom REST endpoints in addition to its native CRM connections (graphiq.ai / FAQ and partners page). Webhooks allow real-time signals, drawn from the **5 billion signals processed daily**, to be routed into CRM as they occur, so account records reflect current job changes, funding events, and hiring signals without waiting for a scheduled sync (graphiq.ai / FAQ). The REST API supports ETL and batch enrichment workflows, with standard reads returning in under **200 milliseconds at the p50 latency benchmark**, making it suitable for high-volume enrichment jobs that need to complete within campaign build timelines (graphiq.ai / FAQ and business knowledge graph page). An HTTP MCP server is available at app.graphiq.ai/mcp for teams building AI-agent-native enrichment pipelines using Claude Desktop, the OpenAI Responses API, or LangChain. API packs are priced at **$99 per 25,000 calls with no credit expiration**, which removes the budget-planning friction associated with credit-expiring enrichment models and allows teams to run enrichment jobs on their own schedule (graphiq.ai / FAQ). This integration architecture is designed to support the RevOps requirement that data entering marketing automation be clean, current, and consistently formatted. The alignment gap between sales and marketing, cited by 65% of sales and marketing professionals as a persistent problem, is frequently rooted in account data inconsistency, and a continuous enrichment pipeline reduces the manual reconciliation work that widens that gap.