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Solution hub

Solutions

Ways to apply Advanze by department, industry, operating model and business use case.

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Team discussing an operating model workflow on a shared screen.
Category pages should help people choose a direction.These pages now behave like curated hubs instead of single product feature pages.

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Choose the page that matches your problem

Use this hub to move into specific capabilities, use cases, services or implementation paths.

AI Ml

Explore AI Ml within the Solutions area.

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AI Operations

Explore AI Operations within the Solutions area.

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AI Powered Operations

Explore AI Powered Operations within the Solutions area.

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Banking

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Communication

Explore Communication within the Solutions area.

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Departments

Explore Departments within the Solutions area.

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Digital Transformation

Explore Digital Transformation within the Solutions area.

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Education

Explore Education within the Solutions area.

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Energy

Explore Energy within the Solutions area.

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Energy Utilities

Explore Energy Utilities within the Solutions area.

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Enterprise

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Finance

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Enterprise architects and product leaders reviewing a platform architecture together.
The structure matters because buyers need routes, not generic claims.A strong hub clarifies what belongs together, why it matters and which page to open next.

How to use this hub

Start with the operating problem, then select the capability

Advanze category pages are designed to connect business outcomes, platform foundations, product capabilities and pricing routes without forcing visitors through disconnected product descriptions.

Agentic use case

Where Solutions 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.

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Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 11Data, analytics and resilience work connecting business decisions to governed evidence.
Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 12Data, analytics and resilience work connecting business decisions to governed evidence.
Data and business teams reviewing analytics, resilience and recovery evidence.
Data confidence 13Data, 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