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Commercial Model

MAP

A simpler way to plan adoption, consolidation and scale.

MAP provides a commercial view of the Advanze platform. Pricing is designed to support consolidation, predictable adoption and a clearer relationship between platform investment and operating outcomes.

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Platform and governance team reviewing AI execution, controls and architecture.
MAP starts with people trying to make the right call. Teams want to use AI more widely.

Commercial Model / MAP

Commercial clarity for platform adoption

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 MAP 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.

Platform and governance team reviewing AI execution, controls and architecture.
Where MAP becomes real work people can trust. Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Core capabilities

What MAP enables

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

Agent-Based Pricing

Pay per active AI agent deployed, not per LLM token or API call. Predictable monthly costs for agent runtime, orchestration, and execution with transparent capacity allocation.

Multi-Agent Economics

Volume discounts for deploying agent teams and workflows. Scale from individual task agents to complex multi-agent orchestrations with tiered pricing that rewards platform adoption.

Agent Builder Included

No-code agent creation and training tools included in platform subscription. Build custom agents without separate development environments, IDE licensing, or AI model fees.

Orchestration Governance

Agent coordination, task distribution, and workflow automation included. Centralized control plane for managing agent lifecycles, monitoring execution, and enforcing governance policies.

Execution Monitoring

Real-time agent performance tracking and cost attribution dashboards. Understand which agents drive value, where execution time is spent, and how to optimize agent efficiency.

AI Workforce Scaling

Treat AI agents as organizational resources with predictable capacity planning. Add agent capacity as business needs grow without complex token budgets or LLM provider negotiations.

The Advanze Stack - Business Services, Platform Services, Technology Foundation

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

PredictabilityPlan cost by suite, service and adoption pathway.
Consolidation economicsEvaluate savings from retiring overlapping tools.
Value alignmentConnect spend to execution outcomes.
Scale readinessSupport growth across users, agents, records and APIs.
Platform and governance team reviewing AI execution, controls and architecture.
The outcome is not just automation. It is confidence in what happens next. When MAP runs inside a governed execution model, teams can move faster without losing judgement, accountability or trust.

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
    Scope the suite

    Identify which applications, services and integrations should be included in the first adoption wave.

  2. 2
    Model value

    Estimate consolidation savings, productivity lift and execution capacity using conservative assumptions.

  3. 3
    Choose the path

    Start with a focused pilot or move directly into a platform foundation programme.

  4. 4
    Scale deliberately

    Expand suites, agents and integrations with clear governance and measurement.

Why this pricing path matters

MAP pricing should be understood through the work it helps govern.

Teams want to use AI more widely.

What makes it harder in the real world: The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.

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

Router AgentSelects the smallest capable model or workflow path for the task.
Budget AgentTracks per-run and monthly use-case budgets.
Evaluation AgentChecks output quality, policy fit and repeated failure patterns.
Runtime Control AgentStops loops, retries safely and escalates when budget or quality thresholds are breached.
OwnerReviews value, adoption, cost and exceptions.
Challenge

The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.

Orchestration

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

Success

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

Related execution patterns

Complex products usually touch more than one governed workflow.

DataServices, Data Governance, Operations

Data Quality Exception and 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.

Explore pattern
GRC, Security, Operations

Incident Response with Governed Escalation
Coordinate incident intake, severity classification, evidence collection, stakeholder notification, escalation and remediation tasks through governed agentic workflow.

Explore pattern
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.

Explore pattern
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 13Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 14Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 15Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Control model

Useful AI execution needs pricing, permissions and governance to move together.

That is why pricing has to connect to the operating model: the number of users is only one part of the cost; the real value comes from governed execution, reusable controls and agent work that can safely scale.

  • The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.
  • 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.
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Next step

Build this into your execution platform roadmap

Explore how MAP can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.

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