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Microsoft Copilot Studio

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Copilot Studio is Microsoft's low-code platform for building custom AI agents — internal helpdesk bots, customer-facing chat assistants, knowledge-grounded Q&A. It connects to your data sources (SharePoint, Dataverse, third-party APIs) and supports authenticated multi-turn conversations.

Low-code

Build agents without traditional development cycles.

Knowledge Sources

Ground responses in SharePoint, Dataverse, custom data.

Connectors

Connect to 1000+ systems via Power Platform connectors.

Multi-channel

Deploy to Teams, websites, mobile apps.

Licensing

License model

Per-tenant + Message-pack consumption

Commitment options
  • Monthly
  • 1 year

Pricing has consumption (message) and tenant base components — sizing per usage volume.

Who is this for?

Customer-service heavy operationsKnowledge-base companiesInternal IT/HR self-service

Frequently Asked Questions

Is this a replacement for Copilot for M365?

No — different purpose. M365 Copilot embeds in productivity apps. Copilot Studio builds custom agents for specific use cases (helpdesk, customer service, knowledge base).

What if we already have a chatbot?

Copilot Studio can complement or replace existing platforms. Strengths: deep Microsoft Graph + Dataverse integration, M365 license-aware authentication, Power Platform connectors. Migration paths from Power Virtual Agents (legacy) are well-supported.

How is consumption priced?

Message packs (typically per 25K messages) on top of tenant base. Sizing requires understanding expected interaction volume — we model this in discovery.

Xen Bilişim Deployment Process

  1. 1. Discovery & sizing: Current environment, user count, OS/cloud distribution and compliance requirements analysed; correct SKU and licence count proposed.
  2. 2. Pilot deployment: A 10-25 device subset goes live; integration with existing security stack tested; alerting + reporting configured.
  3. 3. Full rollout: Phased rollout across all endpoints; policy templates applied; user training and IT runbook delivered.
  4. 4. Optimisation & follow-up: 90-day post-launch tuning: false-positive triage, policy hardening, KPI review and quarterly health-checks.

Typical end-to-end timeline: 2-4 weeks (varies by user count and integration scope).

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