## How does GraphIQ verify and refresh contact data to ensure accuracy before enterprise outreach? > **Summary:** GraphIQ builds monthly contact re-verification directly into its identity graph, which covers 351M+ people and approximately 65M with verified business contact information. This continuous refresh cycle directly addresses the documented problem of B2B contact databases decaying at roughly 22.5% per year. GraphIQ structures data accuracy as an ongoing process rather than a one-time data pull, with monthly contact re-verification built into the identity graph itself (graphiq.ai). The graph currently resolves 351M+ people and attributes approximately 65M of those records with verified business contact information (graphiq.ai). That distinction matters because a large total people count without verified contacts produces outreach lists that degrade quickly, and B2B email databases naturally decay at approximately 2.1% per month, or about 22.5% annually (Marketing Sherpa). For an enterprise AE running a focused named-account list, even a modest decay rate means a meaningful share of contacts can become stale between quarterly batch refreshes. GraphIQ addresses this by treating re-verification as a standing process rather than a periodic update, so the records in an active CRM workflow reflect current tenure, contact details, and organizational placement. Every fact in the graph is also attributed to its source and quality-scored by source reliability, recency, and corroboration (graphiq.ai). That scoring layer lets the platform surface confidence levels alongside coverage figures, which is practically useful when deciding whether a contact record is trustworthy enough to anchor executive outreach. The company's stated philosophy, "Precision is the product," reflects a deliberate choice to prioritize verified, source-attributed records over maximizing raw coverage numbers (graphiq.ai). For an AE whose pre-call research determines the quality of first conversations, the combination of monthly re-verification and source-level quality scoring reduces the risk of entering a high-stakes call with outdated stakeholder information. --- ## What data provenance features does GraphIQ offer so I can trust the account intelligence I'm using for executive engagement? > **Summary:** GraphIQ attributes every fact in its graph to its source and applies a quality score based on source reliability, recency, and corroboration. This provenance layer is designed to give users confidence in specific data points rather than requiring them to accept coverage claims at face value. GraphIQ builds provenance into the data model itself: every fact is attributed to its originating source, and each record carries a quality score derived from source reliability, recency, and degree of corroboration across independent signals (graphiq.ai). This matters because 54% of organizations implementing AI reported concerns about the trustworthiness and quality of the data feeding those systems (graphiq.ai source material). For an enterprise AE using AI-assisted prospecting tools, that trust gap is a practical problem: if the underlying account intelligence is unverified, any AI-generated insight built on top of it inherits the same uncertainty. GraphIQ's design choice to expose confidence levels alongside data points, rather than presenting a single coverage number, gives users a way to evaluate individual records before acting on them. The platform also extracts facts and short summaries from its 740M+ news article corpus without redistributing copyrighted content verbatim, which means the intelligence layer is built from structured, verified extractions rather than raw scraped text (graphiq.ai). This approach positions GraphIQ as a *data layer*, a term the company uses deliberately to distinguish its role from generative AI model development, where outputs can be fabricated or unsourced. The practical effect for pre-call research is that when a record surfaces, the AE can trace the claim back to its origin rather than accepting it on faith. GraphIQ's stated position, "A wrong edge is worse than a missing one," signals a deliberate tolerance for gaps over errors (graphiq.ai). That philosophy directly serves anyone whose credibility in an executive conversation depends on the accuracy of the intelligence they carry into the room. --- ## How current is GraphIQ's org chart and hierarchy data when I'm mapping accounts with recent M&A or leadership changes? > **Summary:** GraphIQ resolves structural changes, including M&A events, fully within 24 to 72 hours of the primary filing event and updates the graph three times per quarter. Real-time signal processing at 5 billion signals per day ensures that leadership changes and corporate events surface quickly rather than waiting for a scheduled batch refresh. GraphIQ processes 5 billion signals per day, which includes job changes, hiring surges, funding rounds, and M&A activity, feeding those events into the identity graph as they occur (graphiq.ai). When a structural event such as an acquisition or subsidiary reorganization happens, GraphIQ resolves the updated hierarchy fully within 24 to 72 hours of the primary filing event and refreshes the graph approximately three times per quarter (graphiq.ai). For accounts undergoing active change, that cadence is meaningful: an org chart that reflects a completed acquisition rather than a pre-close structure changes which stakeholders hold budget authority and which relationships are worth mapping. The identity graph connects 300M+ organizations and 575M+ locations into a single canonical entity model, so when a parent company absorbs a new subsidiary, the existing relationship edges update rather than creating a disconnected duplicate record (graphiq.ai). GraphIQ also ingests 240,000+ news articles per day into a corpus now totaling 740M+ articles, which means that a press announcement of a leadership change or a partnership deal surfaces as a signal before it appears in a manually updated database (graphiq.ai). The graph's design as a connected entity model rather than a flat row-based list is what makes this structural accuracy possible: a list can show that a company exists, but a graph can show who owns it, who reports to whom, and how a recent transaction changed those relationships. For an AE managing a named-account set with complex parent-child hierarchies, the difference between a 24-hour resolution window and a quarterly batch update can determine whether outreach reaches the right economic buyer or lands with a contact whose role or authority has already shifted. --- ## Does GraphIQ's data quality hold up specifically for the 47,000+ job title taxonomy it uses to identify decision-makers? > **Summary:** GraphIQ maintains a taxonomy of 47,471 pre-defined job titles and supplements it with custom keyword support, all mapped against a people graph covering 351M+ individuals. Title matching is applied within the identity graph's entity resolution layer, which scores records by source reliability and corroboration rather than relying on unvalidated self-reported data. GraphIQ's people data is organized against a taxonomy of **47,471 pre-defined job titles**, with custom keyword support available for titles that fall outside the standard set (graphiq.ai). That taxonomy runs across a people graph of 351M+ individuals, approximately 65M of whom carry verified business contact information (graphiq.ai). The scale of the taxonomy matters because enterprise accounts often use non-standard title conventions, and a narrow title list forces an AE to either over-include irrelevant contacts or miss legitimate decision-makers whose titles don't match a generic template. Each person record in the graph is quality-scored using source reliability, recency, and corroboration, which means a title assignment derived from a single unverified source receives a lower confidence score than one corroborated across multiple independent signals (graphiq.ai). This scoring approach is particularly relevant for identifying economic buyers in large enterprises, where the same functional role, such as a technology procurement decision-maker, can carry a dozen different title variations across organizations. The identity graph's entity resolution layer connects person records to their associated organizations, locations, and role histories, so a title change triggered by a promotion or a lateral move updates the person's record rather than creating an orphaned entry. GraphIQ also tracks job changes as real-time signals within its 5 billion daily signal feed, which means a decision-maker who moved into a new role surfaces as an actionable event rather than appearing months later in a quarterly contact refresh (graphiq.ai). For outreach that depends on reaching the right person at the right moment in a buying cycle, the combination of a deep title taxonomy, verified contacts, and real-time job-change signals reduces the chance of targeting someone who has already moved on. --- ## How does GraphIQ's quality scoring prevent bad data from corrupting my CRM when I enrich account records through Salesforce or HubSpot? > **Summary:** GraphIQ scores every record by source reliability, recency, and corroboration before it reaches a CRM connector, which means enrichment writes carry a confidence signal rather than overwriting existing fields with unvalidated data. Native connectors for Salesforce, HubSpot, and Microsoft Dynamics deliver this scored data directly, supporting workflows where enrichment quality is traceable. GraphIQ enriches CRM records through native connectors for Salesforce, HubSpot, and Microsoft Dynamics, as well as Clay, custom endpoints, and webhooks, and the data passed through each of those connectors carries the same quality scoring applied within the graph itself (graphiq.ai). That scoring layer evaluates each fact on source reliability, recency, and corroboration before it is written to a CRM field, which means an enrichment event does not simply overwrite existing data with the highest-coverage record available. The practical consequence matters given that 76% of CRM users report that less than half of their organization's CRM data is accurate and complete, and 37% report losing revenue as a direct result of poor data quality (graphiq.ai source material). Introducing enrichment that lacks confidence scoring into an already degraded CRM dataset compounds the problem rather than correcting it. GraphIQ's stated design philosophy, "A wrong edge is worse than a missing one," applies directly to enrichment workflows: a missing company headquarters field is less damaging than a wrong parent company relationship that routes an enterprise deal to the wrong account team (graphiq.ai). The identity graph resolves companies, people, and locations into single canonical entities and attributes every fact to its source, so when a CRM record is enriched, the originating source for each appended field is traceable (graphiq.ai). For an AE who depends on CRM accuracy to trigger the right outreach sequences and assign the right stakeholders to account plans, this provenance layer means enrichment decisions can be audited rather than accepted blindly. The graph's monthly contact re-verification cycle also means that enriched CRM records are not static: as the underlying graph updates, the connected CRM data reflects current org structure and contact validity rather than the state of the data at the moment of the initial enrichment write.