This week, financial services firm Riverty and Microsoft partner Cluster Reply unveiled a new omnichannel customer service platform built on Microsoft Dynamics 365, delivered in just 100 days across eight countries and four languages. The deployment, which consolidates voice, chat, and email into a single agent interface, marks one of the fastest rollouts of an AI-augmented contact center in the financial sector and sets the stage for future Copilot-powered voice and chatbot automation.

What Actually Changed

The nuts and bolts of the project show how quickly an enterprise can pivot when the right components align. Riverty, the fintech arm of Bertelsmann that handles payment processing, receivables management, and accounting for millions of consumers and merchants, now runs its customer service through Dynamics 365 Customer Service with the omnichannel and contact center add-ons. This replaces a likely patchwork of separate telephony, chat, and email systems.

Cluster Reply, a specialist integrator within the Reply Group that focuses exclusively on Microsoft technologies, led the implementation. The 100-day clock, according to the announcement, started from project kickoff and ended with a live, multi-market deployment. That speed is unusual for a regulated industry where compliance and security testing often stretch timelines.

The system went live with intelligent routing that uses AI to match incoming requests to the best-suited agent based on skills, availability, and customer context. Automated context recognition pulls up relevant account details and transaction history so agents don’t waste time searching. Real-time operational dashboards give team leads immediate insight into queue health and agent performance. And crucially, the platform is architected to onboard Microsoft Copilot Studio, which will let Riverty build and deploy conversational AI agents that can handle routine inquiries and, eventually, carry on voice conversations—with the promise that a human is always a click or a spoken word away.

Riverty’s customer base spans roughly 11 countries and includes millions of consumers and merchants, with tens of millions of transactions processed each month. The firm employs about 4,000 people. The new platform is live in eight markets and four languages, with a design that can scale to additional regions as needed.

What It Means for You

The impact of this deployment ripples out to different stakeholders in distinct ways.

If you’re a Riverty customer, the changes may feel subtle at first. Calls and chats should route more intelligently, and agents will have your full history at their fingertips without making you repeat information. Over time, expect more self-service options via chatbots and voice assistants—but with a commitment that humans remain available for complex or sensitive financial matters. The company is framing the AI as an assistant, not a replacement.

If you’re an IT or customer service leader at another enterprise, this project is a reference point. It demonstrates that a complex, multi-market omnichannel rollout doesn’t have to take years. The 100-day timeline, while vendor-reported and not independently audited, suggests that with the right Microsoft partner and a disciplined approach, you can compress deployment dramatically. The key is sticking to first-party Microsoft components—Dynamics 365, Dataverse, Entra ID, and Copilot Studio—which simplifies integration, security, and compliance. However, the speed comes with caveats. The AI features are staged. Riverty started with assistive capabilities (routing, context) before moving to autonomous agents. That’s a smart play: it lets you validate the foundation, train agents, and fine-tune AI models without exposing customers to half-baked chatbots.

For customer service managers, the single-agent interface is a practical win. Switching between application windows is a notorious time sink and a source of agent frustration. Having all interactions in one place, with AI-powered summarization and sentiment tracking, can reduce handling times and improve both employee satisfaction and customer outcomes. The live dashboards also empower managers to spot bottlenecks and coach agents in near real-time.

For compliance and security teams, the announcement underscores the need to scrutinize AI’s role in financial services. Generative AI can hallucinate, and a wrong answer about a due date or balance could have legal consequences. Microsoft Copilot Studio includes tools for grounding responses in verified knowledge sources, but the onus is on the deploying organization to configure and monitor these safeguards. Any firm following this path must map data flows, maintain audit trails, and ensure voice recordings are properly handled under GDPR or other privacy laws. The architecture’s reliance on Dataverse as the central data plane is a plus, as it provides a unified model for compliance controls, but the real work lies in governance and policy enforcement.

How We Got Here

Customer service technology has been inching toward unification for years, but the pandemic accelerated digital channel adoption and exposed the fragility of siloed contact centers. Microsoft responded by enhancing Dynamics 365 with omnichannel routing, sentiment analysis, and, more recently, generative AI via Copilot. The company now positions Dynamics 365 Contact Center as a full-featured alternative to standalone CCaaS platforms like Genesys or NICE.

Riverty, as a fintech managing sensitive payment data, needed a platform that could scale across language and regulatory borders while maintaining high trust. Its parent Bertelsmann has a long relationship with Reply Group, and Cluster Reply’s deep Microsoft expertise likely made the 100-day sprint feasible. The project aligns with a broader industry trend: “human-centric automation” that augments agents rather than automating them out of a job. Microsoft’s own guidance for Copilot Studio emphasizes shared context and clean hand-offs between bots and humans, a pattern Riverty followed.

Financial services have historically been slow to adopt customer-facing AI because of strict regulations and zero tolerance for errors. Riverty’s approach—using AI for routing and context while keeping humans in the loop for decision-making—is a template for how to start safely. The staged rollout also mirrors a mature DevOps philosophy: deliver value fast, measure, and iterate.

What to Do Now

If you’re evaluating a similar deployment, here’s a pragmatic checklist distilled from Riverty’s experience and industry best practices:

  1. Measure your starting point. Before touching a single system, capture baseline metrics: average handling time (AHT), first contact resolution (FCR), customer satisfaction (CSAT), and agent turnover. Without these, you can’t prove the ROI or identify what’s working. Set specific, measurable targets for improvement.
  2. Stage your AI rollout. Don’t try to boil the ocean. Start with agent-assist features (summarization, knowledge retrieval) to keep humans in control. Then add chatbots for simple, high-volume queries like password resets or balance checks. Only tackle voice automation after you’ve proven success in text channels and built robust escalation paths. Validate that AI-generated responses are accurate and that customers accept them.
  3. Harden data governance from day one. Map every data flow—from the customer’s utterance to the AI’s response and the agent’s screen. Apply least-privilege access, set retention policies for transcripts, and log all AI inferences for auditability. In financial services, regulators will want to see exactly how a decision was made. Use tools like Microsoft Purview to maintain visibility.
  4. Lock in predictable Copilot costs. Microsoft’s licensing for Copilot Studio and related AI services is still evolving. Negotiate usage caps, overage terms, and service-level agreements that cover AI behavior. Demand visibility into consumption metrics so you’re not surprised by a bill. Consider a pilot subscription before committing to enterprise-wide licenses.
  5. Plan for model safety. Constrain generative outputs to your own curated, versioned knowledge base. Set confidence thresholds; if the AI isn’t sure, it should escalate. Never let a bot give financial advice or modify account details without human approval. Regularly test the system with adversarial user inputs to uncover edge cases.

Outlook

Riverty’s 100-day deployment is a compelling proof point, but it’s still early. The real test will be whether those vendor-reported improvements in processing time and customer satisfaction hold up under independent scrutiny. Watch for third-party audits or detailed case studies with hard numbers. Also keep an eye on Copilot Studio’s general availability road map: as Microsoft adds voice and digital channel capabilities, the economics and feature set could shift quickly. Regulators in the EU and North America are already drafting AI accountability rules that will impact how financial services chatbots are designed and audited. And as voice bots become more common, customer acceptance—especially in sensitive domains like finance—will ultimately determine whether “empathetic automation” is a genuine advance or just marketing. For now, Riverty’s approach offers a realistic template: move fast, but don’t take your hands off the wheel.