AI data Foundation
AI data Foundation
Many Chatbots
One
truth
AI chatbots are changing expectations. They promise oracle-like answers. But without the correct data foundation, they give confidently wrong answers.
Individual applications can answer local questions, but when every application ships its own chatbot, definitions drift, joins differ, and there is no single source of truth.
Keep using application chatbots for day-to-day operations. But for strategic decisions, use Zap's decision agent, grounded in a centralized, reconciled data foundation. Zap Data Hub centralizes and reconciles your data in a data warehouse, while a semantic layer defines metrics, relationships, and access.
The 3 essentials for AI success
1
Context
A centralized warehouse reconciles data across sources, giving AI complete business context. The semantic layer then constrains AI to your defined metrics, relationships, and access rules.
2
Structure
A well-structured data model and architecture keep analytics and AI fast and reliable at scale. Data quality is enforced through structure (keys, integrity checks), enabling trusted sub-second queries for humans and AI alike.
3
Context
Centralized metrics, enforced access controls (including row-level security) and auditable queries (not one-off prompt responses) ensure trust. Once meaning and access are governed in the semantic model, you can use "Talk Data to Me" in Zap Analytics or Copilot in Power BI to ask questions of your data.
The AI stack
Zap's AI Foundation undergirds our manifesto. Built on our automated data warehouse and semantic layer, the following sit on top:
Thinking Engines run pattern discovery, anomaly detection, and explainer engines against your governed semantic layer.
Agents act on those outcomes in tools like Teams or downstream agents, assessing conditions to trigger alerts and actions.
Reconcile early
-
In the ERP, Sales can mean invoiced revenue in base currency.
-
In CRM, Sales can mean won deals (or pipeline).
-
In spreadsheets, Sales can mean bookings or cash received.
If each chatbot answers from its own context, you'll get multiple plausible answers. And if you try to reconcile on demand, you add ambiguity at query time when definitions and joins matter most!
When data and definitions are spread across systems, the same question can mean different things.
For example:
Zap's AI Data Foundation provides
Complete business context
Centralised, reconciled data starting with your ERP, based on our pre-built data models.
Governed definitions
A semantic layer that pre-defines relationships, measures, and relative time logic.
Fast
exploration
Interactive exploration so humans and agents can query and discover fast.
Enforced access controls
Defined access controls (including row-level security) configured through the data model.
See it on your data
Zap AI is in active rollout from mid-Q1, with continuous capability releases following. Get immediate value from your data foundation through fast, governed analytics.
Book a demo to start building your AI data foundation today.