AI POWERED BUSINESS INTELLIGENCE
Enterprise AI Context Layer Built on Trusted Business Data
The data layer that makes enterprise AI trustworthy
Large language models are trained on vast amounts of public text, but they lack reliable, verified knowledge about the specific businesses your organization works with. Without accurate context, AI systems hallucinate company names, confuse subsidiaries with parent entities, and produce outputs that cannot be trusted for financial or operational decisions.
D&B’s Context Layer solves this by supplying AI systems with structured, continuously refreshed business entity data — verified against 30,000+ sources worldwide. Every business record is anchored to a D-U-N-S® Number, the global standard for company identification, enabling your AI to resolve entities consistently and act on facts.
600M+ verified business records
The world’s largest commercial database, refreshed daily with firmographic, financial, and ownership data across 250global markets.
D-U-N-S® Number
A persistent, globally recognized identifier that resolves business entities across your systems, eliminating duplication and ensuring AI models always refer to the correct company.
Traceable data
Every record is traceable to its source and updated through D&B’s patented data quality process — giving compliance, legal, and audit teams a foundation they can stand behind.
Realtime enrichment
Signals like payment behavior changes, ownership shifts, and financial stress indicators are fed into your AI context continuously — not as a monthly batch.
600M+ verified business records
The world's largest commercial database, refreshed daily with firmographic, financial, and ownership data across 250global markets.
D-U-N-S® Number
A persistent, globally recognized identifier that resolves business entities across your systems, eliminating duplication and ensuring AI models always refer to the correct company.
Traceable data
Every record is traceable to its source and updated through D&B's patented data quality process — giving compliance, legal, and audit teams a foundation they can stand behind.
Realtime enrichment
Signals like payment behavior changes, ownership shifts, and financial stress indicators are fed into your AI context continuously — not as a monthly batch.
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.
- Proactive ethical reviews and auditing across data attributes
- Explainability and immutable audit trails for regulatory compliance
- AI Systems Cards and Agent Cards for AI transparency
Build AI you can Trust
We apply responsible AI principles — combining governance, transparency, and ethical design to help organizations innovate with confidence while preserving data trust and compliance.
Where the Context Layer delivers value
Retrieval-Augmented Generation (RAG)
Ground RAG pipelines with verified D&B entity data so your LLM retrieves and reasons from accurate, structured business facts rather than unverified public text.
- Entity disambiguation at retrieval time
- Structured data injection into prompts
- Reduced hallucination on company-specific queries
Agentic AI workflows
Give autonomous AI agents access to real-time business context — so they can make sound judgments about counterparties, suppliers, and prospects without human intervention.
- Live entity lookup during task execution
- Ownership and hierarchy resolution
- Risk signal integration into agent decision trees
Risk Decisioning
Feed credit models with fresh financial indicators, payment behavior, and firmographic signals from D&B — replacing stale batch data with continuously updated context.
- Financial stress early-warning signals
- Payment behavior data
- Failure Scores, D&B Ratings
Aggregated Data vs. D&B Commercial Graph
Different approaches to business data lead to very different outcomes for enterprise AI. These are the dimensions that determine whether a model 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 assumptions | 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, across 250+ global markets |
Freshness | As stale as the upstream provider | Refreshed daily with continuous monitoring |
Quality controls | Inherited and uneven across sources | Data Quality Framework with 100B+ checks per month across the Commercial Graph |
Lineage | Lost the moment data is repackaged | Source-of-record lineage maintained throughout |
Linkage & hierarchy | Flat or vendor-specific | Native corporate family tree built from originated relationships |
AI reliability | Confident hallucinations on entities | Auditable data provenance that supports transparency and explainability |
Ready to build AI on a foundation of trusted data?
Talk to a D&B AI specialist and discover how to embed world-class business intelligence into your AI strategy. From proof of concept to production.
FAQ over D&B.AI
How does D&B data improve AI model accuracy?
D&B’s verified entity data — including the D-U-N-S® Number, financial indicators, and ownership hierarchies — provides the ground-truth context that AI models need to make reliable predictions. By resolving entities consistently and enriching records with fresh signals, your models avoid the garbage-in, garbage-out problem that limits generic training data.
How does the D&B Commercial Graph fit alongside our existing data stack?
The D&B Commercial Graph complements your existing data stack by adding a trusted global business intelligence layer. Built on hundreds of millions of verified business identities linked through the D-U-N-S® Number, it connects corporate hierarchies, ownership relationships, and rich contextual metadata. This gives AI agents the context they need to understand when, where, and how business data, analytics, and risk insights can be applied within intelligent workflows.
What makes Altares Dun & Bradstreet different from other business data providers?
Our data is collected directly from authoritative sources, including government registries, financial statements, trade exchanges, and verified business inquiries. Every company profile is built around the globally recognized D-U-N-S® Number, providing a consistent and reliable business identity instead of relying on uncertain record matching. This enables AI applications to work with trusted, persistent company identifiers, enriched with available source attribution and data lineage for maximum transparency.
How up-to-date is the data my AI will see?
The D&B Commercial Graph is continuously refreshed with new business intelligence from around the globe. Daily updates capture changes to corporate structures, legal status, financial performance, and other critical business events, ensuring your AI applications operate on accurate, up-to-date company data rather than outdated snapshots.
How do you prevent hallucinated entities and merge subsidiaries?
Every organization is linked to a verified D-U-N-S® Number, providing a unique and persistent business identity. Native corporate hierarchy mapping enables AI systems to accurately distinguish legal entities while maintaining clear visibility into parent companies and complex ownership structures—without relying on potentially ambiguous company names or addresses.
Is the D&B Commercial Graph ready for RAG and agentic workflows?
Absolutely. The D&B Commercial Graph is purpose-built for modern AI applications, including Retrieval-Augmented Generation (RAG) and agentic workflows. Its structured, governed, and AI-optimized data model enables reliable retrieval, while attribute-level lineage provides complete transparency, making every AI-generated response explainable and auditable.
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