Point-in-Time Restore
Snapshot and delta backup enable restoration to any historical state. Recovery objectives can be aligned to documented service targets. Accidental deletions can be undone by restoring affected records to their prior state.
Execution Platform
Dataservices DRR
Unified data, services, workflows and agents in one operating substrate.
Dataservices DRR is part of the Advanze platform layer: the execution foundation that enables applications, services and AI agents to work as one operating system for the business. It is designed for extensibility, governance and scale from the start.
Execution Platform / Dataservices DRR
Built as an execution foundation
Advanze is positioned around the idea that enterprise software must evolve from passive systems of record into active systems of execution. In that model, this page is not just a feature description. It explains how Dataservices DRR contributes to an operating environment where people define intent, agents execute governed work, and leadership can see progress through unified data.
The value is strongest when the capability is connected to adjacent processes. Records, workflows, controls, communications and analytics should not live in separate tools. They should participate in a shared execution fabric that can coordinate work across departments while preserving human accountability.
Core capabilities
What Dataservices DRR enables
Each capability is designed to work as part of the broader execution platform rather than as a disconnected module.
Soft Delete Protection
Deleted records move to an archive table instead of being purged immediately. Retention policies control how long archived data persists. Accidental deletions are reversible within the retention window.
Automated Snapshots
Scheduled snapshots capture full database state at regular intervals. Incremental snapshots minimize storage overhead. Snapshot integrity checks verify data consistency before marking a snapshot as valid.
Geographic Replication
Multi-region replication protects against regional outages. Asynchronous replication balances performance with durability. Failover procedures switch to backup regions with minimal disruption.
Recovery Testing
Automated recovery drills validate that backups can be restored successfully. Recovery time metrics inform disaster planning. Compliance requirements for data protection are met through documented tests.
Cloud-Native Storage
DRR leverages cloud storage accounts for limitless capacity and durability. Geo-redundant storage options protect against hardware failures. Storage lifecycle policies move old backups to archive tiers automatically.
Agentic operating model
AI agents execute work inside the control model
Advanze treats AI agents as participants in the operating model. Agents can read context, call services, update records, trigger workflows, prepare decisions and escalate exceptions. Human teams remain responsible for judgement, governance and business accountability.
That distinction matters. The goal is not to add another chatbot to existing systems. The goal is to create an execution platform where work can move across functions with consistent permissions, policies, audit trails and data visibility.
Business outcomes
What becomes possible
Agentic use case
Where Dataservices DRR becomes governed execution.
A bad import or system change has corrupted business records.
What makes it harder in the real world: Data correction requires evidence, impact analysis, ownership, approval, rollback choice, downstream notification and audit trail. A fast fix without traceability can make the incident worse.
What Advanze changes: Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.
Data correction requires evidence, impact analysis, ownership, approval, rollback choice, downstream notification and audit trail. A fast fix without traceability can make the incident worse.
Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.
Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.
Why AI execution needs architecture
The work needs context, controls and clear permissions before automation can safely act.
That is why the Advanze control model matters: identity, permissions, policies, workflow, audit evidence and human judgement are embedded into execution before agents act.
- Data correction requires evidence, impact analysis, ownership, approval, rollback choice, downstream notification and audit trail. A fast fix without traceability can make the incident worse.
- The right agent must receive the right context, tools, permissions and approval path before work moves forward.
- Audit evidence, exception handling and human judgement need to be part of the workflow, not notes added after the fact.
Implementation path
How to move from concept to production
Advanze can be introduced progressively. The recommended path is to start with a visible workflow, prove the operating model, then expand into adjacent capabilities as the platform foundation matures.
- 1Assess the current operating model
Map the workflows, systems, data sources and manual coordination points around this capability.
- 2Design the target execution flow
Define the data model, human approvals, agent tasks, service calls and governance controls.
- 3Launch a focused implementation wave
Start with a bounded use case that proves the operating pattern and creates reusable platform assets.
- 4Scale across the business
Extend the pattern to adjacent workflows, more agents, more users and deeper integrations.
Next step
Build this into your execution platform roadmap
Explore how Dataservices DRR can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.