Riverty, the financial services arm of Bertelsmann, and systems integrator Cluster Reply have delivered a unified, AI-first customer service platform built on Microsoft Dynamics 365 and Copilot Studio in just 100 days. The deployment, first announced via Business Wire on September 19, 2025, now handles telephone, chat, and email inquiries for Riverty’s operations in eight countries and four languages, with further Copilot-powered automation planned for voice and chatbot interactions.

A 100-Day Transformation

The new platform collapses three previously separate communication channels into a single Dynamics 365 interface for service agents. Instead of toggling between phone systems, chat dashboards, and email queues, agents now see a complete interaction history and customer context in one pane. Intelligent routing and automated context recognition went live immediately, aiming to slash transfer rates and speed up first-contact resolution. Real-time dashboards give supervisors live performance metrics without manual report-building.

Riverty handles tens of millions of transactions per month and supports millions of consumers across roughly 11 countries, according to its own corporate materials. The public statements specify that the platform is already active in eight markets and four languages, with scalability designed into the architecture for additional regions. The 100-day timeline covers design, integration, testing, and multi-market rollout—an aggressive schedule for an enterprise-grade contact center transformation.

The Technology Underpinning the Platform

The technical backbone is Microsoft Dynamics 365 Customer Service, which provides omnichannel routing, case management, and the Dataverse data store. Integrated directly into that layer is Microsoft Copilot Studio, a low-code environment for building AI agents that can answer simple queries autonomously, retrieve knowledge for agents, and hand off to a human when conversations become complex. Microsoft’s own documentation confirms that Copilot Studio agents can be connected to omnichannel conversations, share context during transfers, and operate in multiple languages.

This separation—core transactional data in Dynamics 365 and AI behavior configured in Copilot Studio—gives Riverty a modular architecture. Curated knowledge sources and business rules constrain the AI, reducing the risk of ungrounded responses. That design is critical for a financial services firm where regulatory compliance and accuracy are non-negotiable.

What Early Results Show

Vendor-reported metrics from Riverty and Cluster Reply indicate that request processing times are already declining and customer satisfaction scores are rising. No independent, third-party audit of these figures is available yet, so the improvements should be viewed as provisional. The technical feasibility of the solution is not in doubt—Microsoft’s product roadmaps and public case studies confirm that omnichannel routing, agent-assist summarization, and Copilot agent hand-offs are production-ready capabilities—but the specific operational uplift remains vendor-supplied until an external evaluator weighs in.

What It Means for Riverty’s Customers

For the consumers and businesses that rely on Riverty for payment plans, receivables tracking, and account management, the immediate benefit is a faster, more coherent support experience. Callers and chatters no longer need to repeat their issue when they switch channels. Over time, Copilot Studio–powered voice and chatbots—still in a staged rollout—will handle straightforward inquiries like balance checks or payment date reminders outside business hours, while leaving complex, emotionally sensitive conversations to human agents. Riverty’s explicit design principle is “AI to augment, not replace,” so the human element remains when empathy matters most.

Practical Takeaways for Enterprises Considering a Similar Move

If you manage an IT or customer service team and are watching this fast-tracked deployment, the playbook is instructive but demands caution. Here are steps gleaned from the Riverty–Cluster Reply approach and Microsoft best practices:

  • Baseline your KPIs first. Before any tech lift, capture average handling time, first-contact resolution rates, CSAT scores, and agent occupancy. You cannot measure improvement without a verified starting point.
  • Start with agent assist, not full automation. Deploy features like case summarization, knowledge retrieval, and automated context cards before you turn on autonomous bots. This de-risks the AI introduction and lets agents grow comfortable with the tools.
  • Harden data governance immediately. Financial services firms must map exactly where customer data, voice recordings, and AI inference data live, for how long, and under which jurisdiction’s rules. Microsoft’s compliance tooling (Microsoft Purview, Entra ID, audit logging) can underpin this, but you must configure and monitor it rigorously.
  • Pilot voice bots in a single language with tight guardrails. Voice automation is sensitive to accent, background noise, and call quality. Pilot narrowly, define clear escalation paths, and only expand once failover and containment rates are acceptable.
  • Negotiate predictable Copilot pricing. Microsoft’s licensing for Copilot features is evolving. Model costs with realistic usage estimates, and build in caps or review periods to avoid surprise bills.
  • Keep knowledge sources fresh and validated. An AI agent is only as good as the content it ingests. Assign owners to update policy documents, FAQs, and process logs, and remove outdated information on a schedule.

The Fintech Context: Why Speed and Safety Both Matter

Financial services firms have historically lagged behind other industries in deploying customer-facing AI because of regulatory risk and high-trust relationships. Riverty operates in a particularly sensitive space—payments and debt collection—where errors can have legal and reputational consequences. The decision to deploy on a tightly integrated Microsoft stack, with human agents still making all consequential decisions, lines up with the approach that regulators and risk committees prefer.

The announced deployment also signals competitive pressure. Better, faster customer service is a differentiator in payments and receivables management. Riverty’s public statements position this platform as a cornerstone of its wider ambition to lead AI-powered financial services, a message aimed at partners and markets as much as customers.

Next Steps for Caution and Adoption

Despite the headlines, three risk areas deserve attention from any organization benchmarking this case.

  • Vendor-reported metrics need independent proof. Without third-party data, board-level ROI conversations are speculative. Before taking a similar leap, insist on a defined measurement plan with regular external audits.
  • Hallucination risk in finance is real. A generative AI agent that misstates a payment amount or due date creates legal liability. The Riverty design uses curated knowledge sources and human escalation, but the ongoing challenge is keeping those guardrails effective across languages and ever-evolving content.
  • Vendor lock-in and cost predictability. A Microsoft-only stack simplifies integration but concentrates dependency. If Copilot pricing or regional availability shifts, the TCO model must adapt. Building a modular integration layer and maintaining strong licensing leverage are essential.

Looking Ahead

Three developments will determine whether this 100-day sprint becomes a benchmark for the industry. First, independent metrics or analyst commentary that validates the processing-time and satisfaction claims. Second, the maturation of Copilot Studio—Microsoft is rapidly adding contact-center-specific survey bots, connectors, and voice features; general availability and pricing updates will affect the economics of follow-on rollouts. Third, real-world voice bot acceptance: if completion rates stall or customer sentiment dips, the “empathic automation” promise will face its first true test.

For now, Riverty’s deployment demonstrates that an AI-first, human-centric contact center is technically achievable on a compressed schedule—as long as governance, language support, and careful staging accompany the technology.