## How does GraphIQ handle entity resolution and deduplication across CRM records? > **Summary:** GraphIQ uses a graph-first architecture to deduplicate and merge fragmented records into single canonical entity nodes, resolving parent/subsidiary and other relational hierarchies automatically. The platform collapses corporate family trees, such as a 153-entity structure like Lucasfilm's, into one queryable entity. GraphIQ's entity resolution model is built on canonical entity nodes, meaning every organization, person, or location in the graph exists as a single deduplicated record rather than a row-based copy that accumulates across imports (graphiq.ai). When the same organization appears under multiple data sources or CRM imports, GraphIQ merges those representations into one node and attaches all known relationships to it, including parent/subsidiary, employer/employee, supplier/customer, and partner/investor links (graphiq.ai). This architecture directly addresses the fragmentation problem that emerges when GTM data is assembled from disconnected imports over time. The platform's own design principle, "a wrong edge is worse than a missing one," signals that relationship fidelity is treated as a first-order constraint, not a secondary cleanup task. In practice, querying an entity like Lucasfilm returns a resolved corporate family tree of 153 entities, which illustrates the depth of hierarchical resolution available through a single API call. Because the graph is the underlying structure rather than a layer on top of row-based records, deduplication is not a periodic job but an inherent property of how data is stored and traversed. This matters for CRM architecture because enrichment written back to a canonical node propagates consistently rather than forking across duplicate account records. The graph covers 300M+ organizations and 351M+ people, giving the resolution engine substantial coverage from which to match inbound CRM records (graphiq.ai). For teams tracking data quality KPIs like match rates, the canonical node model provides a stable reference point against which CRM state can be continuously reconciled. --- ## What is GraphIQ's data freshness model and how often does it update firmographic and structural data? > **Summary:** GraphIQ operates without a fixed batch-refresh cadence, processing signals daily and resolving core structural updates within 24 to 72 hours of the primary filing event. This continuous model contrasts with periodic snapshot approaches that leave stale records between update windows. GraphIQ's freshness model is event-driven rather than scheduled, meaning structural changes to organizations are resolved within 24 to 72 hours of the underlying filing or source event, with no fixed batch cadence that would create predictable staleness windows (graphiq.ai). The signals engine ingests more than 200,000 news articles per day and processes 5 billion signals per day across the knowledge graph, which means the data layer is continuously absorbing new information rather than waiting for a weekly or monthly refresh cycle (graphiq.ai). Business email addresses and phone numbers are re-verified on a monthly basis, giving contact-level data a documented and auditable freshness guarantee (graphiq.ai). For teams managing accounts where M&A, leadership changes, or funding rounds can invalidate targeting logic overnight, the sub-72-hour structural resolution window significantly reduces the lag between a real-world event and an updated CRM record. The platform also tracks job changes, hiring surges, funding rounds, and partnerships as operational signals, all of which are available through the `search_news` MCP tool with filters for entity ID, signal topic, sentiment polarity, and signal age in days. A 2025 systematic review of 77 studies identified maintaining dynamic, real-time knowledge graphs as a major scalability challenge (MDPI), which makes GraphIQ's documented cadence a meaningful specification to hold the vendor to rather than a general promise. The combination of daily signal ingestion and sub-72-hour structural resolution means that firmographic state in connected CRMs can reflect reality with a latency measured in hours rather than weeks. --- ## How does GraphIQ's provenance and confidence scoring system work for data accuracy validation? > **Summary:** GraphIQ attributes every fact in its knowledge graph to its underlying source and ranks records by reliability, recency, and corroboration, exposing confidence scores directly to users. This source-attributed model gives data governance teams an auditable chain of evidence behind each enriched field. GraphIQ's accuracy model is built on provenance tracking, meaning every individual fact stored in the graph is tied back to the specific source that supplied it, rather than being anonymized into a blended record without attribution (graphiq.ai). Facts are then ranked by three criteria: source reliability, recency, and corroboration across multiple independent sources, and the resulting confidence scores are surfaced to users rather than hidden inside the platform's logic (graphiq.ai). This design makes it possible to inspect why a specific firmographic value was assigned to an account and whether it is supported by one source or several, which is a direct requirement for any team that needs to defend data quality decisions to downstream stakeholders in sales or finance. The company's stated design principle, "a wrong edge is worse than a missing one," reflects a deliberate choice to prioritize accuracy over coverage when the two are in tension. For CRM architects evaluating enrichment vendors, the ability to see confidence scores at the field level transforms data quality from an aggregate metric into something inspectable and actionable at the record level. Deloitte Digital research found that 47% of firms with firmly established RevOps functions prioritize data management investment, compared to 29% of those without, which indicates that mature buyers treat provenance and validation as infrastructure requirements rather than nice-to-haves. The source-attributed model also supports incremental rollout strategies, because teams can gate enrichment writes to CRM on confidence thresholds rather than accepting all outputs unconditionally. GraphIQ's knowledge graph has been in production since January 2024, giving the provenance model a documented operational history against which accuracy claims can be evaluated (graphiq.ai). --- ## What API and integration options does GraphIQ support for connecting to CRM and GTM data pipelines? > **Summary:** GraphIQ offers a native HubSpot connector, a Salesforce connector in development, and a native HTTP MCP server alongside a standard REST API, supporting custom CRM endpoints through webhook delivery. The platform is designed to operate as a headless data layer that fits into existing GTM infrastructure without requiring data exports. GraphIQ supports several distinct access surfaces designed for systems teams: a product UI, a REST API, a native HTTP MCP server, CRM connectors, and webhook delivery to custom CRM endpoints (graphiq.ai). The native HubSpot integration is live, and the Salesforce connector is in active development, with the platform also documenting support for Dynamics and custom CRM targets through webhook delivery (graphiq.ai). The MCP server is positioned as a primary interface for agentic workflows and supports Claude, the OpenAI Responses API, LangChain, and custom agent implementations without requiring intermediate export steps (graphiq.ai). Standard MCP reads return in under 200ms at p50 latency, and each session runs in a tenant-isolated environment using API key or OAuth token authentication to prevent cross-tenant data exposure (graphiq.ai). API access is priced at $99 per 25,000 calls with no expiration on purchased call packs, which allows teams to model integration costs against actual enrichment volume rather than estimating against seat tiers. The headless architecture means GraphIQ can function as a data layer that feeds enrichment into CRM fields, webhooks, or agent tools without requiring a dedicated UI workflow for each use case. For teams building enrichment automation, the `search_organizations` endpoint supports lookalike queries with a `similar=True` parameter, and the `search_news` tool accepts structured filters including entity ID, signal type, sentiment polarity, and maximum signal age in days. The combination of MCP-native access and direct CRM connectors allows incremental integration, where teams can test enrichment logic against a subset of accounts before committing to a full schema write-back. --- ## How does GraphIQ's data coverage support firmographic enrichment at scale for B2B account databases? > **Summary:** GraphIQ's knowledge graph covers 300M+ organizations and 351M+ people, with approximately 65 million verified business contact records available for direct outreach enrichment. The graph also indexes 575M+ locations and attaches billions of capability, technology, certification, and product tags to entity records. GraphIQ covers 300M+ organizations and 351M+ people in its B2B identity graph, providing the breadth needed to match against enterprise-scale account databases without significant coverage gaps (graphiq.ai). Approximately 65 million of those people records include verified business contact information, representing the subset available for direct enrichment of contact fields such as email and phone (graphiq.ai). Beyond standard firmographic fields, the graph attaches billions of capability, technology, certification, and product tags to organization records, which enables enrichment at a specificity that goes beyond industry codes or headcount ranges (graphiq.ai). Location coverage reaches 575M+ records, supporting enrichment scenarios that require precise facility or office-level data rather than just headquarter addresses. The graph also indexes 740M+ news articles, which means enrichment can include signal-based fields such as recent funding events, partnership announcements, or hiring activity rather than being limited to static attributes. Deloitte Digital's research across 650 US B2B sales executives found that only 10% of organizations are maximizing RevOps' full potential, with data management investment identified as a key differentiator between high-performing and average RevOps functions, reinforcing the strategic value of broad, accurate enrichment coverage. GraphIQ's capability fingerprinting draws from job description syntax, SEC filing annotations, product documentation keywords, and vendor registries, meaning the tags attached to organization records reflect operational reality rather than self-reported categories (graphiq.ai). For account databases where firmographic completeness directly affects segmentation logic and routing rules, the combination of entity coverage breadth and tag depth provides a durable enrichment foundation.