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Execution Platform

Platform Dataservices

Unified data, services, workflows and agents in one operating substrate.

Platform Dataservices 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.

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Data and business teams reviewing analytics, resilience and recovery evidence.
Platform Dataservices starts with people trying to make the right call. A bad import or system change has corrupted business records.

Execution Platform / Platform Dataservices

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 Platform Dataservices 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.

Data and business teams reviewing analytics, resilience and recovery evidence.
Where Platform Dataservices becomes real work people can trust. Data, analytics and resilience work connecting business decisions to governed evidence.

Core capabilities

What Platform Dataservices enables

Each capability is designed to work as part of the broader execution platform rather than as a disconnected module.

Hyperscale Document Storage

Distributed NoSQL database engineered for billions of records. Built-in sharding, replication, and auto-scaling handle explosive growth without manual intervention or downtime.

Point-in-Time Recovery

Continuous backup with second-level granularity. Restore any database state from the last 30 days, protecting against data corruption, accidental deletions, or security incidents.

Zero-Config Indexing

Automatic index creation based on query patterns. The platform observes actual usage and optimizes indexes dynamically, eliminating the need for manual database tuning.

Multi-Model Data Support

Single platform for transactional records, indexed entities and API-backed data. Agents access the right data model for each use case without managing separate databases.

Built-In Encryption

Encryption at rest and in transit by default. Field-level encryption for sensitive data with automatic key rotation and comprehensive access controls integrated with identity services.

Global Distribution

Multi-region replication with configurable consistency levels. Deploy data close to users and agents worldwide while maintaining strong consistency or eventual consistency as needed.

The Advanze Control Model - AI Agents Execute Work Inside the Control Model

Agentic operating model

AI agents execute work inside the control model

Advanze treats AI agents as active participants in the execution model rather than passive assistants. Agents can autonomously read context from unified data stores, call platform services to perform operations, update business records in real-time, trigger multi-step workflows, prepare decision packages for human approval, and escalate exceptions when policies require oversight. Human teams define the guardrails and maintain accountability for business outcomes.

This architecture matters because it transforms work execution across the enterprise. Instead of adding chatbots on top of disconnected systems, Advanze provides an execution substrate where agents operate with consistent permissions, follow the same governance policies as human users, generate complete audit trails for every action, and share unified data visibility with human colleagues. Work moves faster while control strengthens.

Business outcomes

What becomes possible

Scale by designArchitecture supports growing workloads, data and agent activity.
Governed automationSecurity, identity and controls are embedded in execution paths.
Composable servicesTeams can build faster using reusable platform capabilities.
Operational confidenceTelemetry and auditability make execution observable.
Data and business teams reviewing analytics, resilience and recovery evidence.
The outcome is not just automation. It is confidence in what happens next. When Platform Dataservices runs inside a governed execution model, teams can move faster without losing judgement, accountability or trust.

Agentic use case

Where Platform Dataservices 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 Quality AgentDetects anomaly, schema drift, duplicates or invalid values.
Impact AgentIdentifies affected tables, records, processes, customers and downstream systems.
Recovery AgentProposes record-level, table-level or full restore options based on snapshots and change logs.
Data OwnerApproves correction or restore decision.
Notification AgentDrafts stakeholder updates and creates downstream reconciliation tasks.
Challenge

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.

Orchestration

Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.

Success

Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.

Related execution patterns

Complex products usually touch more than one governed workflow.

Finance, Accounting, Control

Finance Reconciliation and Exception Resolution
Use agents to gather statements, ledger extracts, rules and prior exceptions, then propose safe matches and route unresolved items for review without posting journals automatically.

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Customer Experience, KYC, Risk, Compliance

Customer Onboarding with KYC and Risk Review
Coordinate onboarding, KYC checks, document review, risk scoring, approvals and customer communication through a governed agentic workflow.

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AI Governance, Finance, Platform Operations

AI Runtime Control
Control AI execution by routing tasks to the right model, setting run budgets, applying permissions, stopping loops and reporting usage by use case.

Explore pattern
Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 23Data, analytics and resilience work connecting business decisions to governed evidence.
Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 24Data, analytics and resilience work connecting business decisions to governed evidence.
Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 25Data, analytics and resilience work connecting business decisions to governed evidence.

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.
View related pricing Explore Multi-Agent Platform

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.

  1. 1
    Assess the current operating model

    Map the workflows, systems, data sources and manual coordination points around this capability.

  2. 2
    Design the target execution flow

    Define the data model, human approvals, agent tasks, service calls and governance controls.

  3. 3
    Launch a focused implementation wave

    Start with a bounded use case that proves the operating pattern and creates reusable platform assets.

  4. 4
    Scale 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 Platform Dataservices can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.

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