Put AI to work on the data your team governs.
Use Sage to assess performance, compare options, and draft workflows across InsightsOutward. Sovereign AI defines the deployment boundary, access controls, and audit requirements around that work.
- Sage
- The intelligence layer
- Sovereign AI
- The deployment and governance model
An answer you can investigate.
Illustrative workspace · Fictional data
Why did Northeast redemption decline while earn activity stayed steady?
- Scope
- Northeast · October
- Sources
- Program activity + reward catalog
- Access
- Authorized regional records
Two signals to investigate
Select a finding to locate its supporting evidence.
Working hypothesis Reduced reward availability may have contributed. These records show a relationship, not a confirmed cause.
Supporting evidence
Source record- A
Catalog change log
Rewards NE–014 and NE–028 changed from available to unavailable in October.
Catalog history / Northeast - B
Prior-period redemptions
Both rewards appear in the region’s September redemption history.
Redemption ledger / September - C
Monthly activity comparison
Earning volume is similar across the two periods; redemption volume is lower in October.
Program activity / September–October
Finding 01 is linked to records A and B.
Concept illustration, not a live product screen. Available data, configured logging, and approved permissions determine what a deployment can return.
Working with Sage
One question.
Several ways forward.
Choose the kind of help the task needs. These are complementary capabilities, not a required sequence.
- Assess
Establish what changed.
Compare regional performance with the prior period and surface the records behind the difference.
- Guide
Compare the options.
Explore restoring reward availability or adjusting the offer, with assumptions and tradeoffs available for review.
- Assist
Prepare the next action.
Draft an audience, incentive rule, or monitoring plan for an authorized operator to review before activation.
- Explain
Trace the supporting evidence.
Connect a finding to the relevant activity and configuration records, keeping observations separate from hypotheses.
Sovereign AI / Deployment
Make the data path visible.
A useful answer is only part of the design. Your team also needs to establish where processing happens, what the user may retrieve, and what evidence remains.
- 01 / Access
Ask
Identify the user and the scope of their request.
- 02 / Retrieval
Ground
Retrieve permitted context from approved sources.
- 03 / Processing
Generate
Process the question and context through the agreed model endpoint.
- 04 / Review
Return
Present the response and supporting context for operator review.
Identity & permissions
Processing & residency
Logging & retention
Sovereign AI is designed for a governed Azure environment. Model services, external dependencies, support access, and retained records must be mapped to the agreed deployment boundary.
Explore the deployment questionsStart with the question your team cannot answer easily today.
The strongest fit is an operating question that needs valuable data from several systems, defined access rules, and a team prepared to review the full AI data flow.
That might mean investigating a loyalty performance change or assessing a supplier exception. Start with the decision, identify the data it needs, and then establish the controls around its use.
If the task needs only public information or a simple report, a private deployment may add more complexity than the use case warrants.
Need to define the governance model first?Define the controls.
Agree on the evidence.
A technical review turns the proposed use case into explicit deployment decisions and open questions. These are review areas, not claims of completed assurance.
| Review area | Decision to resolve | Evidence to inspect |
|---|---|---|
| Model & data use | Which model and provider terms apply? How are grounding, tuning, and client data use constrained? | Model configuration, data-use terms, and the exclusion of client data from cross-client training. |
| Processing boundary | Where do retrieval, inference, filtering, and support access occur? | Deployment diagram, service inventory, external dependencies, and control ownership. |
| Residency & retention | Where are source data, indexes, responses, logs, and backups stored, and for how long? | Region configuration, retention policies, and deletion procedures. |
| User permissions | Does retrieval respect the records and fields each user is allowed to access? | Role mapping and acceptance tests covering permitted and denied access. |
| Audit & assurance | What can the team investigate, and who owns the response when something goes wrong? | Sample audit records, approval history, testing scope, incident procedures, and applicable assurance reports. |
Frequently asked
Before a technical review
Does a private deployment make our organization compliant?
Can Sage access data a user cannot otherwise see?
Does Sage activate changes on its own?
What should we bring, and what do we leave with?
Start with one operating question
Bring the use case. We’ll map the boundary.
Include the people who own the data, the decision, and the controls. Together, we can establish what a useful, reviewable deployment would need.
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Discuss Your AI Use Case
Map the proposed deployment, identify control ownership, and document the questions that require deeper diligence.
Discuss Your AI Use Case -
See Sage in Action
Explore Assess, Guide, Assist, and Explain through representative operating questions before a technical review.
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