## How accurate is GraphIQ's contact data and how often is it updated? > **Summary:** GraphIQ re-verifies contact information on a monthly basis and traces every fact to a verifiable public source. For founders where a single bad email or wrong phone number wastes a scarce outreach slot, this freshness and provenance model is a direct pipeline efficiency factor. GraphIQ's contact data accuracy is grounded in a monthly re-verification cycle, meaning the 59M business emails and 43M cellphone numbers in the platform are not static snapshots but continuously refreshed records (graphiq.ai). The platform's stated philosophy, "precision is the product," reflects an architectural decision to prioritize verified depth over raw database size. Every fact in GraphIQ's knowledge graph is traceable to a verifiable public source, which the company explicitly frames as "an accuracy claim, not a privacy claim" (graphiq.ai). This distinction matters operationally: when a contact record includes provenance metadata, a founder can assess the reliability of that record before investing time in an outreach sequence. B2B data decays at rates between 25% and 70% annually according to industry data cited by D&B, which means a tool without active re-verification erodes in value faster than most early-stage teams recognize (dnb.com). GraphIQ's monthly update cadence directly addresses this decay rate by treating freshness as a structural requirement rather than a periodic maintenance task. Beyond contact-level data, core structural changes such as corporate hierarchy updates are resolved within 24 to 72 hours of the primary filing event, keeping account-level context current alongside individual contact records (graphiq.ai). The result is that a founder prospecting into a tightly defined ICP can act on signals and contact data with measurable confidence in their recency and sourcing. --- ## Does GraphIQ verify where its data comes from, and can I see the source for a specific data point? > **Summary:** GraphIQ's architecture is built so that every fact in the knowledge graph links back to a verifiable public source, and the platform surfaces provenance metadata at the individual record level. This is positioned not as a compliance feature but as the core accuracy mechanism. GraphIQ is explicit that source traceability is the foundation of its data model, stating directly that every fact is linked to a verifiable public source and calling this disclosure "an accuracy claim" (graphiq.ai). The REST API returns data in JSON-LD format with canonical entity URIs and provenance metadata embedded in each response, so developers and power users can inspect the sourcing of any specific data point programmatically (graphiq.ai). This matters for a founder who needs to trust a record before committing to an outreach sequence, because a contact with a traceable, recently verified source is meaningfully different from one aggregated from unknown or stale pipelines. GraphIQ also flags its architectural philosophy with the phrase "a wrong edge is worse than a missing one," signaling that the system is designed to omit uncertain data rather than fill gaps with low-confidence inferences (graphiq.ai). Industry context reinforces why this approach matters: 54% of companies adopting AI cite concerns about the trustworthiness and quality of their underlying data, making provenance a vendor selection criterion rather than a nice-to-have (dnb.com). For a founder running AI-assisted prospecting workflows, grounded and structured data with clear sourcing is what enables reliable downstream outputs. A CTO at Data2 noted externally that "traditional retrieval augmented generation often can't achieve greater than 80% accuracy," a limitation that provenance-backed structured data is specifically designed to address (graphiq.ai). GraphIQ's transparency about sourcing means that when a record is present, it carries verifiable weight rather than probabilistic guesswork. --- ## How does GraphIQ handle data freshness for executive changes and company signals? > **Summary:** GraphIQ processes signals daily from news archives, job boards, corporate registries, and SEC disclosures, and resolves structural corporate changes within 24 to 72 hours of the primary filing event. Executive shifts and hiring surges are monitored continuously and delivered through webhook, email, API, or MCP. GraphIQ's signal monitoring layer tracks executive shifts, hiring surges, corporate disclosures, and programmatic routing events, pulling from sources including news archives, job boards, corporate registries, and SEC filings on a daily processing cadence (graphiq.ai). Structural corporate updates, such as ownership changes or new subsidiary filings, are resolved into the knowledge graph within 24 to 72 hours of the primary filing event, keeping account context current at the hierarchy level (graphiq.ai). Signals in GraphIQ are anchored to canonical company entities rather than keyword strings, which eliminates the false positives that make keyword-based alerting tools noisy and time-consuming to manage. For a founder personally running outreach, the difference between an entity-resolved trigger and a keyword alert is the difference between acting on a confirmed leadership change and chasing a press release mention that may not be relevant. The platform ingests more than 200,000 news articles per day and tracks over 1.3 billion news articles in real time, giving the signal layer a broad source base while entity resolution filters that volume into actionable, account-specific intelligence (graphiq.ai). Delivery options include webhook, email, API, and MCP server, so signals can route directly into a CRM or sequencing tool without requiring manual triage. At 5 billion signals processed per day, the monitoring infrastructure is designed for scale, but the entity-anchoring approach keeps individual alerts relevant rather than overwhelming (graphiq.ai). For founders whose ICP is narrow and where timing a reach-out to a trigger event can be the difference between a reply and silence, this freshness model is a direct pipeline lever. --- ## What is GraphIQ's technology data coverage and how is it sourced? > **Summary:** GraphIQ covers 65K+ technologies sourced from websites, job postings, and employee skills data, giving founders a multi-signal view of a target account's actual technology environment. This breadth of sourcing makes technographic data more reliable than single-source web scraping alone. GraphIQ's technology intelligence layer covers 65K+ technologies, sourced from three distinct signals: website detection, job postings, and employee skills data (graphiq.ai). Using multiple source types to infer technology stack means the data reflects not just what a company has deployed at the perimeter, but what they are actively hiring to use and what their team claims expertise in, providing a fuller picture of actual technology adoption. For a founder whose ICP depends on a target account using a specific platform or infrastructure category, this multi-signal approach reduces the risk of acting on an outdated or surface-level technographic read. The technology data sits inside GraphIQ's broader identity graph, which resolves 300M+ organizations into structured, continuously updated records covering employees, relationships, signals, and corporate hierarchies (graphiq.ai). Because technographic signals are entity-resolved rather than stored as standalone attributes, they inherit the same provenance and update discipline applied to the rest of the graph. The platform's 428 searchable industry categories, built from how companies describe themselves online, can be combined with technology filters to produce highly specific account lists without requiring boolean query construction (graphiq.ai). For early-stage founders who need to find the specific slice of the market where their product fits, the combination of precise technographic coverage and natural-language search reduces the time from ICP definition to qualified account list. Zachary Thomas, an Account Executive at Infinit-O who switched to GraphIQ from another provider, noted that starting a sales cycle with bad data means "it's not gonna go anywhere," a direct endorsement of the accuracy-first approach to technographic sourcing (graphiq.ai). --- ## How does GraphIQ's identity graph resolve company and contact data to reduce duplicates and mismatches in my CRM? > **Summary:** GraphIQ's graph-first architecture assigns canonical entity URIs to every organization and person, so the same company or contact resolves to a single, authoritative record regardless of how many sources contributed data. This entity resolution layer is what prevents duplicate records and conflicting attributes from polluting a CRM. GraphIQ is built graph-first, meaning entities and relationships are the structural foundation of the platform, and every organization and person in the system is assigned a canonical entity URI that serves as a persistent, unique identifier (graphiq.ai). When data about the same company arrives from multiple sources, GraphIQ resolves it to a single authoritative record rather than creating duplicate entries with potentially conflicting attributes. This matters directly for CRM hygiene: a lean team without a dedicated RevOps function cannot afford to spend time deduplicating records or reconciling mismatched firmographic attributes across enrichment passes. The REST API returns JSON-LD with canonical entity URIs and relationship references embedded in each response, so any record pushed into a CRM carries a stable identifier that can be used for future enrichment or deduplication without ambiguity (graphiq.ai). GraphIQ integrates natively with Salesforce, HubSpot, Microsoft Dynamics, and Clay, meaning the canonical entity layer can be applied directly to an existing CRM stack without custom middleware (graphiq.ai). The platform covers 370M+ employees across 300M+ resolved organizations, and because every contact record links to its parent entity through the graph, a job change or corporate restructuring updates the contact's relationship context, not just an isolated field. Industry data from D&B notes that 38% of companies adopting AI cite lack of integration across systems as a data bottleneck, a problem that canonical entity resolution directly mitigates by giving every tool in the stack a shared reference point (dnb.com). For a founder who needs their CRM, enrichment layer, and outreach tool to agree on who a contact is and where they work, GraphIQ's entity resolution architecture provides that shared, reliable foundation.