AI & Data Foundations
LLMs, agents, knowledge graphs, semantic layers and dashboards are only as good as the entities underneath them. When the same customer lives in four systems under three names, each of them sees a different set of customers, and none of them can tell.
For agents. Every agent shares one identity layer, so two agents never disagree about who a customer is.
Three companies to the dashboard, three nodes to the graph, three answers to the agent. It is one company.
AI Inherits Every Duplicate In Your Data
The same customer lives in your CRM, billing system, support desk and product database, spelled differently in each. A person reading those rows sees one customer. A dashboard, a retrieval pipeline, a knowledge graph or an agent sees four.
Most teams patch this downstream, one system at a time: a fuzzy join in a transformation model, a dedup step in the RAG pipeline, a merge rule in the graph loader. Each fix is local, each disagrees with the others, and none of them gives the entity a stable identifier the next system can use.
The result is AI that is confidently inconsistent. The agent and the dashboard disagree about how many customers you have, and nobody can say which one is right. The fix is a shared identity layer underneath all of them.
Split Answers
Retrieval returns part of a customer’s history and misses the rest, because it was written differently.
Inflated Metrics
Counts, segments and lifetime value are computed on duplicate rows, not real entities.
Disconnected Graphs
“Acme Inc” and “Acme Incorporated” become two nodes, and their relationships are split between them.
One Identity Layer Under Everything
Resolve entities once, in the data platform where the data already lives, and give each one a persistent ID. Then point every downstream system at that layer: retrieval, knowledge graphs, semantic models, dashboards and agents all join on the same ID instead of doing their own matching.
Fixes stop being local. When a match improves, every consumer gets it at once, and when two systems disagree, they are disagreeing about data, not about who a customer is.
Retrieval, graphs, metrics and dashboards all read this one entity.
Resolve Once, Upstream
Matching happens in one place, below every tool that reads the data.
One Persistent ID
Each entity keeps the same ID as data changes, so links and history hold.
Everyone Reads The Same Entity
Dashboards, graphs, retrieval and agents share one answer to “who is this?”.
Everything AI Reads, Resolved First
Identity RAG
Retrieve everything known about the right entity, not similar-sounding strangers.
Learn moreKnowledge Graph
Resolved nodes in, trustworthy graph out, with relationships across people and companies.
Learn moreSemantic Layer
Define metrics on one resolved entity so every count means the same thing.
Learn moreData Quality For BI
Accurate counts, segments and dashboards without double-counted customers.
Learn moreThe Identity Layer Under AI & Data
Zingg runs as a workload inside your data platform and gives every entity one persistent ZINGG_ID. Everything above it reads the same resolved entities.
The same layer powersCustomer Analytics & MarketingFinancial Crime & RiskPrivacy & ComplianceSupply Chain & ProcurementProduct 360Master Data Management
What Zingg Brings
Persistent ZINGG_ID
Every resolved entity keeps one global ID across systems and runs. ZINGG_ID
AI Plus Rules
Matching learned from your labels, combined with deterministic rules where you have them. How Zingg matches
Incremental Flow
Only new and changed records are matched, and IDs persist. Incremental flow
In Your Data Platform
Runs on Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS. Data never leaves your account.
AI & Data Foundations Questions
What is an identity layer?
A shared set of resolved entities, each with one persistent ID, that every downstream system reads. Zingg builds it inside your data platform by matching records across your sources.
Why does AI need entity resolution?
Models and agents reason over the records they are given. If one customer is four records, the model sees four customers, and no prompt can fix data it never received correctly.
Does Zingg replace my vector database, graph database or semantic layer?
No. Zingg resolves entities and adds a ZINGG_ID. The tools you already use keep doing retrieval, graph queries and metrics, on resolved entities.
Where does Zingg run?
Inside your own data platform: Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS.
Give AI One Version Of Every Entity
Build the identity layer in your own data platform, then point every model, metric and agent at it.