Riverty, the Bertelsmann-owned fintech processing over 80 million transactions per month, has gone live with a new AI-infused customer service platform after just 100 days of implementation. Partnering with systems integrator Cluster Reply, the company consolidated phone, chat, and email into a single agent workspace built on Microsoft Dynamics 365, Dataverse, and Copilot Studio. This deployment, now operational in eight markets and four languages, embodies an “AI-first, human-centric” philosophy where automation handles routine tasks while human agents focus on complex, emotionally sensitive interactions.

A Contact Center Reimagined—at Speed

The new platform replaces fragmented tools with a unified Dynamics 365 Customer Service interface. Agents can now see a customer’s full conversation history regardless of channel, thanks to Omnichannel routing and a centralized Dataverse data layer. According to Cluster Reply’s public announcement, the deployment spans voice, chat, and email, and includes immediate AI-powered functions: intelligent routing, automatic context recognition, real-time supervisor dashboards, and automated performance reporting. A staged roadmap adds Copilot Studio–powered voice and chat bots that will escalate smoothly to humans when needed.

What makes the 100-day timeline remarkable is the scale. Riverty serves tens of millions of consumers and a vast merchant network across 11 countries. Any misstep could erode trust or violate strict financial regulations. Yet the rollout appears to have been executed with a clear template: deploy core infrastructure first, layer on assistive AI next, and only then introduce generative agents under heavy guardrails. The architecture mirrors Microsoft’s recommended contact center patterns, where Dataverse acts as the single source of truth for conversations, Omnichannel handles agent orchestration, and Copilot Studio defines configurable AI behaviors—from summarization to voice bot scripts.

What’s Actually Under the Hood

For technology teams considering a similar move, here’s the concrete breakdown. The solution rests on three Microsoft pillars:

  • Dynamics 365 Contact Center (Omnichannel): The agent desktop, unified routing engine, and transcription backbone. It presents a single pane of glass that pulls in conversations from any channel and attaches them to a persistent customer case record. Supervisors get both real-time and historical dashboards for monitoring.
  • Microsoft Dataverse: The enterprise data plane. All transcripts, context variables, and case states reside here. This decouples data from AI logic, easing governance, auditing, and integration with downstream analytics.
  • Microsoft Copilot Studio: The behavior layer. Here, developers and business users can configure agents that understand intents, retrieve knowledge from curated sources, answer in multiple languages, and hand off to human agents with full conversation context. Agents aren’t just chatbots; they can handle voice interactions too, with the same context-preserving escalation.

Cluster Reply’s press materials emphasize that the initial rollout focused on agent-assist features—intelligent routing and context recognition—rather than full-blown automation. Copilot agents are coming later, staged carefully. This incremental approach is both prudent and repeatable. It avoids the trap of overpromising AI autonomy before the underlying data and routing are solid.

For the People on Both Ends

Riverty and its partner have been vocal about the “human-centric” label. That’s not just marketing. In a regulated fintech context, empathy matters. When a customer is struggling with a debt repayment or a disputed transaction, an AI bot can’t improvise genuine compassion. So the design puts AI where it reduces cognitive load—auto-summarizing transcripts, suggesting knowledge base articles, routing cases to the right agent—while keeping humans firmly in control of sensitive conversations.

For contact center agents, this means less time hunting for information and more time solving problems. Real-time dashboards and automated reporting also give supervisors faster insights into team performance, potentially reducing burnout by rebalancing workloads. For customers, the immediate gain is typically shorter wait times and fewer repetitive identity checks, because the system already knows who they are and why they’re calling. Over time, the planned voice and chat agents could offer 24/7 self-service for routine inquiries, but the vendors have explicitly built in hand-offs so a live person is always just one escalation away.

How We Got Here: A Fintech’s Quiet Evolution

Riverty didn’t suddenly leap into AI. The company has been scaling its financial services for years, operating under various brands like AfterPay and collecting decades of transaction data. With 4,000 employees and a growing international footprint, the existing patchwork of customer service tools was becoming a bottleneck. Meanwhile, Microsoft’s contact center story has matured rapidly. The launch of Dynamics 365 Contact Center with Copilot-first capabilities, tighter Dataverse integration, and credible voice capabilities gave Riverty a low-friction path to modernization. Cluster Reply, as a specialist Microsoft integrator, brought prebuilt accelerators that likely cut weeks off the implementation timeline.

The timing also reflects broader market pressure. Competitors in financial services are adopting AI to handle routine inquiries, and customers increasingly expect quick, accurate digital service. Regulators, however, are watching. The EU’s AI Act and evolving data protection rules mean any generative AI in finance must be auditable and explainable. This deployment, with its staged approach and heavy emphasis on dashboards and observability, appears designed to stay ahead of those regulatory expectations.

What This Means for Everyone Else

This isn’t just a Riverty story. The blueprint—first-party Microsoft stack, channel consolidation first, agent assist before generative bots, telemetry from day one—is directly applicable to any enterprise in a regulated industry. The 100-day timeline is achievable not because it’s easy, but because the components are now mature and well-documented. Microsoft’s own learning resources outline the exact patterns for connecting Copilot agents to Omnichannel workstreams, storing transcripts in Dataverse, and building supervisor dashboards. So the technical feasibility is no longer in doubt.

That said, the risks are real and must be managed actively. Hallucinations from generative components could produce incorrect financial advice. Voice bots may struggle with accents or noisy environments. Data privacy obligations vary by country, and mixing voice transcripts with customer records creates a tempting attack surface. Moreover, the operational gains reported by the vendors—shorter processing times and higher customer satisfaction—remain unverified by independent auditors. Until Riverty publishes detailed before-and-after metrics, the improvements should be seen as plausible but provisional.

Getting Started: A Practical Playbook

If your organization is eyeing a similar transformation, here’s a step-by-step path distilled from the Riverty deployment and Microsoft’s best practices:

  1. Baseline everything. Before flipping any switch, capture average handling time, first-contact resolution, CSAT scores, channel volumes, and representative transcripts. You can’t prove value without a “before” picture.
  2. Start with agent assist. Deploy automatic case summarization, knowledge base retrieval, and intelligent routing. Measure the impact on agent productivity and morale. This builds trust and generates a quick win without the risk of autonomous customer-facing AI.
  3. Stage generative agents incrementally. Begin with a single language and constrained topics—say, balance inquiries or address changes. Require explicit confidence thresholds and mandatory hand-offs. Log every autonomous response for auditing.
  4. Harden governance early. Map data flows end to end, define retention and masking rules, version your knowledge sources, and enforce least-privilege access. The cost of retrofitting compliance later is steep.
  5. Contract for observability. Negotiate access to Copilot usage metrics, set caps on AI consumption to control costs, and bake in SLAs for behavior audits. Without this, you’re flying blind.
  6. Expand to voice only after text succeeds. Voice automation introduces accent diversity, ambient noise, and authentication challenges. Pilot thoroughly and monitor completion rates before scaling.
  7. Iterate continuously. Use live dashboards not just as showpieces, but as instruments to refine routing rules, bot topics, and knowledge base content every month.

This phased approach aligns with what Riverty and Cluster Reply have described: high-impact, low-risk features first; constrained generative agents next; and full-scale voice automation only after safety gates are met.

Eyes on the Horizon

The next chapters of this story will be telling. Watch for:

  • Verified KPIs from Riverty. Independent validation of reduced handling times and improved CSAT will transform this from a successful project into a proven case study.
  • Microsoft Copilot Studio updates. New features and pricing changes (especially for voice) could alter the economics for fast followers.
  • Regulatory clarity on generative AI in finance. As regulators issue guidance, the audit trails and explainability built into the Dataverse-Copilot architecture will be tested.
  • Voice acceptance rates in the wild. Whether customers actually complete interactions with voice bots will determine if the planned expansion is wise.

Riverty’s deployment is a compelling demonstration that you can move fast with AI without leaving humans behind. But the real test will be in the long-term operational results and the company’s ability to adapt as both technology and regulation evolve. For now, the message to other enterprises is clear: the toolkit exists, the blueprint works, but the discipline to baseline, stage, and govern is what separates a flashy pilot from a durable success.