Agents cannot reason about what you have not defined
An AI assistant can summarise what it sees. An agent needs to understand what it means. In an enterprise, the same customer, product, account, transaction or risk concept can exist differently across applications, business units and data stores. Without a common business definition, agents can produce answers that sound right while interpreting the underlying business context differently.
Reference Data Model gives those concepts a governed meaning, connecting entities, relationships, events, history and business rules into a common enterprise knowledge foundation. That gives every downstream assistant and agent the same starting point for reasoning.
From AI Answers to Autonomous Action
The Reference Data Model provides the common enterprise knowledge layer that enables this progression.
AI Assistant
Answers questions
Needs:
- Trusted data
- Consistent definitions
- Context
AI Agent
Reasons and executes tasks
Needs:
- Relationships
- Business rules
- Events
- History
- Governed access
Autonomous Enterprise
Coordinates decisions and actions
Needs:
- Shared enterprise knowledge
- Persistent context
- Governed execution
- Traceability
Your canonical blueprint for decision intelligence
BigTapp's Reference Data Models unify core concepts (Party/Customer, Product, Account/Policy, Transaction/Event, Risk, Pricing, GL linkage) with governance and time-variance built in. The models are AI-ready (semantically consistent, retrieval-friendly) and ship with mapping templates and validation packs so you can move from source discovery to conformed model to governed analytics fast, and make retrieval-augmented workflows and agent skills more accurate.
Make Enterprise Knowledge Executable
Knowledge alone does not act. Execution alone does not understand. The two together are what let an agent work inside your business.
Reference Data Model
Provides the enterprise knowledge foundation
Beeonix AI
Provides the agentic execution layer
Domain Agents
Reason, orchestrate and act with governed enterprise context
Banking: one enterprise view of the customer, product and financial relationship
Reference Data Model connects the business concepts that banking assistants and agents need to understand, from customer and relationship structures through products, facilities, transactions, risk and regulatory obligations.
So an RM assistant can understand a customer's relationship, not just retrieve records; a credit agent can reason across exposure and facilities, not just query a table; and a service agent can act with the right customer and product context.
Insurance: connect the customer, policy, claim and risk
The Reference Data Model creates a consistent business view across policy, coverage, billing, claims, underwriting, risk and reinsurance, giving AI agents the context needed to support decisions and execute workflows.
A claims agent can understand policy coverage and claim history together. An underwriting agent can reason across customer, risk and policy context. A servicing assistant can act with a consistent view of the policy lifecycle.
One model, from source discovery to decisions agents can act on
Modelling patterns that keep data consistent, auditable and AI-ready across every subject area, in banking and insurance alike.
A faster, safer path to a foundation agents can act on
Faster to build
Reusable patterns and DDL starters cut platform design and build time significantly, so teams start delivering sooner.
Lower risk at audit
Regulatory-relevant fields, lineage and audit trails sit in the model from day one, reducing rework during audits and exams with governance already in place.
Agents in production sooner
Semantic and KPI starters mean agents have something to reason over from the first sprint, so value arrives in weeks rather than after the modelling programme finishes.
Numbers over narratives.
Every engagement is measured. Here's what that has looked like in production, across regulated industries — not in a sandbox.
