Acting on the wrong source
With the same customer living in five systems, an agent can read a stale or conflicting copy and act on it. The output is only ever as trustworthy as the messiest record it touched.
The foundation for AI
Your AI will only be as good as the data beneath it.
Agentic AI: systems that don't just answer questions but take actions across your business to get work done, is arriving fast. Whether it works for you comes down to a question most organisations haven't asked yet: can your AI trust the data it's standing on?
One data model, one security layer. Truity runs your whole business on a single Salesforce foundation: the thing agentic AI needs most and federated architectures struggle to provide.
- Every part of your business: sales, finance, operations, delivery
- The AI agents you'll put to work on top of it
- The overlap: one trustworthy foundation the agents reason and act from.
First, what "agentic" means
An agent doesn't just answer. It acts.
Most AI you've used answers a question and stops. An agent goes further: it plans steps, uses tools, and works through a task toward a goal with limited human oversight - pulling a record, deciding, updating a system, moving to the next step. That's powerful. It also means every action an agent takes rests on the data it read a moment before.
The problem with federated data
When your data lives in a dozen systems, an agent has no single truth to act on.
"Federated" data means the truth is spread across many systems, each with its own copy of the customer, its own permissions, its own version of events. For a human that's a nuisance. For an agent taking action, it's a fault line, and it shows up in specific, practical ways.
Errors that compound across steps
Because an agent chains many steps, a wrong read early carries forward and contaminates everything after it. Federated data multiplies the chance of that first wrong read.
Governance sprawl
Every system is its own permission boundary. Controlling what an agent may see and do across a dozen of them: for privacy and IP security: is far harder than governing one model with one security layer.
No single place to trust, or to audit
When there's no one source of truth, there's also no one place to verify what an agent did or why. Trust and auditability both fragment along with the data.
Why Truity is different
One data model. One security layer. One place for an agent to trust.
Truity runs your business: sales, finance, operations, delivery, logistics - on a single Salesforce data model. That isn't just tidier. It's the specific thing agentic AI needs to work reliably.
One source to reason from
An agent reads one record of the customer, the order, the project: not five copies it has to reconcile. Better grounding, fewer wrong first steps.
One security layer to govern
Salesforce profiles, permission sets and sharing rules already decide who, and what, can see and do what. Your agents inherit that, so privacy and IP boundaries are governed in one place, not a dozen.
One place to audit
When action and data share a platform, what an agent did and what it read are visible together, so you can trust it, and prove it.
The head start
Consolidating your data isn't just good hygiene. It's your AI head start.
Organisations spread across federated systems will spend the next few years untangling their data before AI can act on it safely. Businesses already running on one model start from where the others are trying to get to, with better data integrity, lower onboarding costs, and far less non-revenue overhead from integrations and stacked subscriptions along the way.
Talk to us
Get the foundation right before the agents arrive.
Tell us how your business data is spread today. We'll show you what it looks like to run on one model, and why that's the difference between AI that acts reliably and AI that acts on the wrong copy.
The Truity library: CPQ, Finance, ERP, Projects, Logistics and more: is how businesses consolidate onto that one model, one app at a time.