Context Layer
Turn Trusted Data into AI Context
When achnored in the D‑U‑N‑S® Number, the context layer connects, organizes, and continuously updates business data. It is a governed foundation for AI decision workflows and reliability.
The New Compute Stack
The Context Layer: Where Value Is Created
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.
Most Enterprise Data Isn't Ready for AI
Hallucination & Validation Problems
Stale Records
Inability to Scale & Embed
The Solution
Get the Right Context with the D&B Commercial Graph
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.
How the D&B Commercial Graph Is Built
Foundation
Context Creation
Governance
640M+
Verified Business Records
250+
Global Markets Covered
100B+
Monthly Data Quality Calculations
1.4B
Match Points
Originated, Not Aggregated
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.
Human + Machine Validation
Entity matching is based on decades of AI development by our global team of data scientists and domain experts.
Continuous Monitoring
Business data degrades quickly. Updates flow continuously as structural changes, legal actions, or financial events occur.
Lineage & Explainability
Attributes carry metadata about their origin, timestamp, and reliability insights, giving your AI models the context needed to produce more consistent, trustworthy outputs.
Aggregated Data Vs. D&B Commercial Graph
| 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 |
Clean data is the essence that AI needs to be valuable, and we’ve accelerated our ability to bring AI into our company by probably years [with Dun & Bradstreet].
Explore AI-Powered Use Cases for Every Team
Across Compliance, Procurement, Finance, Sales, and Data teams, Dun & Bradstreet the single foundation of verified data your teams need to power AI workflows.
Built for Responsible AI
Enterprise AI requires more than just accuracy; it demands governance. Our data products are designed to support rigorous compliance and explainability standards.
Build AI You Can Trust
See how Dun & Bradstreet applies responsible AI principles — combining governance, transparency, and ethical design to help organizations innovate with confidence while preserving data trust and compliance.
Frequently Asked Questions
What Teams Ask Before Adopting the D&B Commercial Graph
We originate data at the source — government registries, financial filings, trade exchanges, and direct inquiries — and anchor every record to the D‑U‑N‑S Number rather than fuzzy-matching across resold feeds. Your AI gets persistent business identity through the D‑U‑N‑S Number, along with provenance and transparency where available.
The D&B Commercial Graph is updated daily, with continuous monitoring of structural, legal, and financial events worldwide. Your models act on today's reality, not last year's snapshot.
Each business is anchored to a verified D-U-N-S Number, with native corporate-family linkage instead of relying on assumptions by using names, addresses, or locations as identifiers. This is designed to keep distinct legal entities distinct and to help ultimate parents remain traceable.
Yes. The D&B Commercial Graph is AI-ready with structured, governed, and formatted for retrieval-augmented generation and autonomous agents and per-attribute lineage so your team can explain every output.
The D&B Commercial Graph starts with our trusted data foundation, bringing together hundreds of millions of global business identities through the D-U-N-S Number, connected and intertwined with corporate hierarchies and linkages, and rooted in structured contextual metadata that helps agentic systems understand the where, when, why, and how our data, scores, and analytics can be leveraged in powerful workflows.