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Turn Trusted Data into AI Context
The context layer is the governed substrate beneath enterprise AI. With Dun & Bradstreet, it standardizes how every business entity is identified, sourced, and connected, turning fragmented records into a continuously updated, explainable view of the commercial world.
The context layer gives models and decision systems the one thing they cannot generate themselves: trusted, traceable ground truth.
Models are only as reliable as the foundation they run on. Fragmented records, inconsistent identifiers, and unclear lineage lead to hallucinations and flawed operational decisions and no amount of model tuning can fix it downstream.
Without high-quality, governed business context behind every output, teams can’t trust what AI says about companies, suppliers, customers, or counterparties.
Business context is anchored to the D U N S® Number, with provenance and governance that help AI systems rely on structured, trusted business identity data.
Models trained on aggregated, third-party data inherit the staleness their upstream sources had missing critical details like recent bankruptcies, address changes, or executive churn.
We make continuous updates, helping your agents act on today's reality.
AI pilots stall on the way to production because the underlying data is fragmented, ungoverned, or trapped in silos.
Governed, AI-ready company data delivered through the D&B Commercial Graph plugs straight into existing pipelines.
Sitting between the analytics, decisions, and results that drive your business and the gen AI models that act on them, the D&B Commercial Graph™ provides the verified company context enterprises depend on. It brings structure, meaning, and trust to AI by resolving who an entity is, how it relates to others, and whether the information can be relied upon.
The D&B Commercial Graph is powered by core capabilities including the D U N S Number, entity resolution, global commercial data, data provenance, the Data Quality Framework, and decision signals. Together, they help ensure AI outputs are grounded in real world context, not assumptions.
Most business data on the market is aggregated — collected from third parties, repackaged, and resold. The D&B Commercial Graph is different: we originate the record at the source, anchor it in an authenticated D‑U‑N‑S Number, and maintain it as the authoritative version of truth. That's what makes the D&B Commercial Graph a foundation, not a feed.
Entity matching is based on decades of AI development by our global team of data scientists and domain experts.
Business data degrades quickly. Updates flow continuously as structural changes, legal actions, or financial events occur.
Attributes carry metadata about their origin, timestamp, and reliability insights, giving your AI models the context needed to produce more consistent, trustworthy outputs.
Varied approaches to business data produce very different results for enterprise AI. Here's how the D&B Commercial Graph compares across the dimensions that determine whether models can be trusted in production.
| Dimension | Aggregated Data | D&B Commercial Graph |
|---|---|---|
| Origin of record | Resold or acquired from third parties or public sources | Originated at the source by Dun & Bradstreet (registries, financial filings, direct inquiries, trade exchange) |
| Identity model | Name, address, or location match, relying on assuptions | Anchored to the D‑U‑N‑S Number — one persistent ID, globally |
| Coverage | Limited to what publishers and crawlers expose | 640M+ entities, public and private, 250+ global markets covered |
| Freshness | As stale as the upstream provider | Updated daily; continuous monitoring |
| Quality controls | Inherited; uneven across sources | Data Quality Framework, including 100B data quality checks per month across the D&B Commercial Graph |
| Lineage | Lost the moment data is repackaged | Source-of-record lineage |
| Linkage & hierarchy | Flat or vendor-specific | Native corporate family tree built from originated relationships |
| AI reliability | Confident hallucinations on entities | Auditable data provenance supports transparency and explainability your agents can be held to |
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