Give AI the business behind the data.
Ragioneer Data Intelligence connects your systems, records and business rules into a maintained foundation for analytics, AI and automation.
Give people and approved applications access to the information they need, with the context to interpret it and the permissions to use it.
Records. Meaning. Rules. Permissions.
How it worksA database can store a fact. The business has to explain it.
In 1970, E. F. Codd published the relational model, proposing a way to work with data without depending on its physical arrangement inside a computer.
That distinction between how information is stored and how it is used remains relevant. Businesses can accumulate records across dozens of applications while still struggling to agree on what those records mean.
Consider one word
Sales may use “closed” for a won contract. A project team may use it for completed work. Finance may wait until the final account is settled.
An AI assistant asked which projects are closed needs to know which meaning applies. It also needs the supporting records, the relevant dates and permission to read them.
That is the work of data intelligence.
Bring the business into the data.
Each layer adds something a model cannot infer on its own.
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01
Connect the records.
Bring together the relevant operational systems, databases, spreadsheets and documents. Reconcile identifiers so the same customer, asset or project can be followed across its history.
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02
Define what the records mean.
Make terms, calculations and relationships explicit. Preserve distinctions such as booked revenue, collected cash and project margin after closeout.
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03
Carry the rules alongside the facts.
Connect policies, contract terms and operating procedures to the situations where they apply, including effective dates and approved exceptions.
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04
Keep the foundation current.
Refresh information at the cadence the work requires. Track source changes, missing fields and conflicts that need an owner’s attention.
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05
Make it usable within its permissions.
Support reporting, search, AI applications and approved workflows with access controls, source references and a record of relevant activity.
Customer support needs more than the help centre.
A customer asks for a refund on an annual subscription.
An assistant working from the help centre alone has only part of the case.
Ragioneer connects the authorized information needed to handle the request: the customer and purchase, the terms in force, the support history and the actions available under the policy.
Evidence from customer support
15%
more issues resolved per hour, on average, for 5,172 customer-support agents given a generative AI assistant.
For each implementation, we measure the work itself
- 01Resolution quality
- 02Repeat contact
- 03Escalation
- 04Time to complete the request
The conversation can vary. Financial authority is checked in code.
For a refund workflow, the execution service checks identity, purchase status, eligibility, prior refunds and transaction limits before any money moves.
The model can propose the action. The service enforces the approved conditions and routes exceptions for review. Payment acceptance and subscription changes need their own explicit permissions and validation.
This creates a controlled path from a request to an action. Clear records make it possible to investigate what was requested, what was allowed and what actually happened.
A foundation for expansion.
Data intelligence makes those connections available. Decision Intelligence adds the analysis to test which opportunities the evidence supports.
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Used by support
The same product information
can help a buyer understand compatibility.
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Used by an account team
The same service history
can help reveal unmet demand.
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Used in operations
The same delivery data
can help assess whether a new region can be served profitably.
Public discovery
For public discovery, we help make approved offering information consistent and usable across websites, feeds and AI interfaces. Search fundamentals still matter: Google says existing SEO practices remain relevant to its AI search features.
Explore Decision IntelligenceStart with a defined piece of the business.
Each additional use case can build on definitions, relationships and controls already established.
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Step 01
Choose the outcome
Identify a decision or workflow that matters, its owner and the evidence needed to evaluate success.
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Step 02
Build the relevant foundation
Connect the necessary sources, agree definitions and establish who can see information or authorize an action.
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Step 03
Test it against real work
Check whether answers reflect current records, exceptions are handled correctly and authorized workflows complete as intended. Establish maintenance responsibilities before expanding.
Before you start.
What teams usually ask before connecting their first systems.
Do we have to replace our existing systems?
The starting point is the systems you already use. Integration and access are designed around the use case, your infrastructure and the responsibilities of each source system.
Can we use Data Intelligence without the second product?
Yes. The foundation supports reporting, business intelligence, search and AI workflows on its own. Decision Intelligence adds systematic analysis of patterns and outcomes over time.
What happens when records disagree?
We define which source governs each field, retain relevant history and surface unresolved conflicts. Important uncertainty should remain visible to the people or applications using the information.
Does this make us compliant?
Ragioneer helps implement the data access and operating controls you specify. Your legal and compliance teams remain responsible for determining the requirements that apply.
Ragioneer Data Intelligence
Make the next AI use case easier to build.
Start with a foundation whose sources, meaning and permissions are understood. Keep it maintained as your business changes.