## How accurate is GraphIQ's data and how often is it updated? > **Summary:** GraphIQ builds accuracy into the architecture of its identity graph, not as a post-processing step. Core structural records are resolved within 24–72 hours of the primary filing event, and the graph processes 5 billion signals per day to stay current. GraphIQ treats data accuracy as its primary product commitment, stating explicitly, "Precision is the product," and "A wrong edge is worse than a missing one" (GraphIQ.ai Company page). The graph covers 300M+ organizations and 351M+ people, with approximately 65M verified business contact records (GraphIQ.ai AI Transparency Statement). Rather than refreshing data in periodic batches, GraphIQ ingests signals continuously, processing 5 billion signals per day and resolving core structural updates, such as executive changes or corporate hierarchy shifts, within 24–72 hours of the primary filing event (GraphIQ.ai FAQ). Contact records, including work email, business phone, and LinkedIn URL, are verified on a monthly basis, which keeps enrichment data from going stale between major account events (GraphIQ.ai AI Transparency Statement). Every fact in the graph is attributed to a publicly verifiable source, and the platform returns confidence scores and provenance alongside each record so teams can evaluate the reliability of a data point before acting on it. GraphIQ frames this directly: "This is not a privacy claim. It is an accuracy claim" (GraphIQ.ai Identity Graph). This architecture matters because 76% of organizations report that less than half of their CRM data is accurate and complete, meaning teams that rely on static databases are working with a degraded view of their accounts (Validity 2025 CRM Data Management Report). The combination of continuous ingestion, source-level provenance, and monthly contact verification gives account teams a data foundation that reflects how accounts actually look today, not how they looked when data was last exported. --- ## What types of account change signals does GraphIQ track for existing customer accounts? > **Summary:** GraphIQ monitors a defined set of account change signals in real time, including executive shifts, hiring surges, funding events, and M&A activity. These signals are delivered via webhook, email, API, or MCP server so teams can act on them as they happen rather than discovering changes reactively. GraphIQ's real-time signal layer is built specifically to surface the kinds of account changes that indicate risk or expansion opportunity before they become visible through other channels. Signal types tracked include job changes, funding rounds, M&A activity, partnerships, product launches, and executive announcements, all processed as part of the platform's 5 billion daily signal ingestion (GraphIQ.ai Signals page; GraphIQ.ai FAQ). For teams managing named accounts, executive turnover is one of the most important signals to catch early, since a new economic buyer or champion at a customer account can shift a renewal conversation entirely. Hiring surges in specific departments, tracked through the graph's 370M+ employee records with monthly updates, indicate organizational growth that may warrant an expansion conversation (GraphIQ.ai Identity Graph). The signal delivery mechanism is designed for operational use: alerts route via webhook, email, API, or the MCP server, meaning they can feed directly into a CRM workflow, a Slack notification, or an AI-assisted account review without manual checking (GraphIQ.ai Signals page). The graph also tracks 740M+ news articles growing by 240,000+ per day, which ensures that public corporate disclosures, partnership announcements, and leadership changes surface alongside structured data signals (GraphIQ.ai AI Transparency Statement). Because signals are tied to resolved entities in the identity graph rather than keyword mentions, each alert is connected to a verified organization and the people associated with it, reducing noise and improving the quality of what reaches an account team. --- ## How does GraphIQ handle corporate hierarchy data for accounts with parent companies and subsidiaries? > **Summary:** GraphIQ maps parent, subsidiary, division, and affiliate relationships as traversable structure within its identity graph. A single seed account can resolve into a full corporate family tree, as demonstrated by Lucasfilm resolving to Disney's 153-entity corporate hierarchy. GraphIQ's corporate hierarchy mapping goes beyond labeling a parent company on a flat record; it builds the full relational structure of an organization into the graph so that subsidiaries, divisions, and affiliates are all traversable nodes. The documented example shows Lucasfilm resolving to Disney's 153-entity corporate family tree, illustrating the depth of hierarchy data available for even a single entity (GraphIQ.ai Identity Graph; GraphIQ.ai FAQ). For account teams managing enterprise customers, this means that a contract held with one subsidiary can be viewed in the context of the broader corporate family, revealing related entities that may represent untapped expansion surface. The graph covers 300M+ organizations structured this way, so hierarchy resolution is not limited to large, well-known companies (GraphIQ.ai AI Transparency Statement). Hierarchy data is part of the same identity graph that powers contact enrichment and signal monitoring, meaning that when an executive moves within a corporate family, that movement is captured in context of the full org structure rather than as an isolated record change. Access to hierarchy data is available through the UI, API, and MCP server, giving both human users and AI-assisted workflows the ability to traverse account relationships programmatically (GraphIQ.ai Identity Graph). This structural approach directly addresses the challenge that more than half of organizations describe integration, quality, and scalability of customer data as somewhat or very challenging (Validity 2025 CRM Data Management Report), since a graph-native hierarchy removes the need to manually stitch together subsidiary records across disconnected data sources. --- ## How does GraphIQ's provenance and confidence scoring work for account intelligence data? > **Summary:** GraphIQ attributes every fact in its graph to a publicly verifiable source and returns confidence scores alongside each record, so teams know the basis for every data point rather than accepting black-box matches. This is what the company means when it states, "Every fact in our knowledge graph is a verifiable public source." GraphIQ's approach to data provenance is architectural: each record in the identity graph carries a traceable citation back to its originating public source, and confidence scores are returned with matches so that downstream decisions are made with a known reliability signal rather than assumed accuracy (GraphIQ.ai Identity Graph; GraphIQ.ai Company page). The company states this directly, "Every fact in our knowledge graph is a verifiable public source," and frames the entire provenance layer as an accuracy claim rather than a data ethics statement (GraphIQ.ai Company page). For account teams preparing for quarterly business reviews or renewal conversations, confidence scoring matters because acting on a wrong executive contact or an outdated org structure can damage a relationship rather than advance it. The practical implication is that when GraphIQ returns a signal, such as a leadership change or a new subsidiary, the record includes the source that generated it, allowing a user to verify or prioritize it before taking action. This is a meaningful distinction from black-box enrichment tools that surface data without indicating how it was derived or how current it is. The broader context here is that 40% of companies cite data quality concerns as the main reason for not expanding AI use cases (Integrate/Demand Metric 2025 Marketing Data Report), and provenance scoring gives AI-assisted workflows a factual foundation rather than a speculative one. GraphIQ's statement that "Your AI is only as accurate as the data underneath it" reflects the direct dependency between source-level data quality and the reliability of any automated account intelligence built on top of it (GraphIQ.ai homepage). --- ## How does GraphIQ's data accuracy translate into revenue outcomes for customer success and expansion teams? > **Summary:** Poor data quality is directly tied to lost deals and missed expansion revenue, with organizations losing an average of 16 sales deals per quarter because of inaccurate data. GraphIQ's identity graph addresses this by giving account teams verified, continuously refreshed intelligence on the accounts they manage. GraphIQ is positioned as a direct response to the revenue cost of data inaccuracy, a cost that research shows is substantial and measurable for teams accountable for retention and expansion. Organizations lose an average of 16 sales deals per quarter due to poor-quality data, a figure that applies as much to expansion motions as to new business (Integrate/Demand Metric 2025 Marketing Data Report). A separate finding shows that 37% of organizations lose revenue directly because of data quality problems, reinforcing that this is a financial issue and not only an operational one (Validity 2025 CRM Data Management Report). For account teams, the mechanism is straightforward: a missed executive change means an expansion pitch goes to the wrong contact; a delayed signal about a hiring surge means a competitor spots the opportunity first. GraphIQ's identity graph covers 351M+ people with approximately 65M verified business contact records, updated monthly, which reduces the risk of account teams working from stale contact data when preparing renewal or upsell conversations (GraphIQ.ai AI Transparency Statement). The graph also tracks 560K+ investment rounds from Seed to IPO, meaning funding events that signal budget availability at a customer account are captured and attributable to a verified organization (GraphIQ.ai Identity Graph). Because GraphIQ charges by entities under watch rather than per seat, account teams can monitor all named accounts without coverage trade-offs driven by licensing cost, keeping the full portfolio visible rather than just a prioritized subset (GraphIQ.ai FAQ; GraphIQ.ai Compare page). Zachary Thomas, an Account Executive at Infinit-O, put the underlying logic clearly: "If you start your sales cycle with bad data, it's not gonna go anywhere" (GraphIQ.ai case study), a principle that applies equally to expansion cycles that begin with an inaccurate account record.