Most corporate AI rollouts fail spectacularly. Employees ignore the tools, productivity stagnates, and executives wonder where their investment went. Berlin-based fintech Unzer just proved there’s a better way.
Their internal AI platform, UnzerAI, has reached something rare in enterprise software: genuine enthusiasm. Four out of five employees actively use the system. That’s not compliance or mandated adoption. That’s organic buy-in from people who found something genuinely useful.
What Actually Happened
Unzer rebuilt their earlier assistant tool from the ground up. The previous UnzerGPT handled basic queries, but UnzerAI connects directly into the company’s knowledge infrastructure. Think of it as upgrading from a chatbot to a digital colleague who actually knows where everything is filed.
The engagement numbers speak volumes. Over 300 employees generate roughly 1,200 conversations weekly, exchanging nearly 10,000 messages through the system. For a financial services company handling sensitive data, that level of trust signals something significant.
Here’s what makes it work: instead of hunting through separate systems for company information, employees get instant, cited answers. The platform pulls from internal databases, documentation, and knowledge bases in real time.
The system operates exclusively within Unzer’s firewall. No data leaves the building. No external AI services process sensitive information. Every response includes citations pointing back to verifiable internal sources.
Why This Breakthrough Matters
When 80% of your workforce embraces an AI tool, you’re not just automating tasks. You’re amplifying institutional knowledge across every department. Sales teams can instantly verify policies. Operations staff can access complex procedures without waiting for responses from other teams.
Consider the math: if each employee saves 30 minutes daily on information searches, that’s 150 hours of reclaimed productivity weekly across 300 users. Scale that across quarters, and you’re talking about meaningful competitive advantage.
“Our clients entrust us with their most sensitive data,” explains Niv Liran, Unzer’s Chief Product and Technology Officer. “With UnzerAI, we affirm that robust AI can harmoniously coexist with rigorous privacy measures.”
The security component matters deeply here. Financial services companies operate under strict regulatory frameworks. A single data breach or compliance failure can trigger massive penalties and reputational damage. Unzer’s approach demonstrates that AI capabilities don’t require sacrificing security protocols.
The Technical Architecture That Works
UnzerAI operates through dual specialized agents. The Unzer Agent handles internal processes, company guidelines, HR policies, and compliance procedures. The General Agent manages broader tasks like document preparation, drafting, and research support.
But Unzer isn’t stopping with general-purpose tools. Specialized agents for legal, sales, and operations departments are in active development. Each will provide function-specific capabilities tailored to departmental workflows and regulatory requirements.
Every system response includes full documentation trails. Employees can trace answers back to original sources, maintaining audit compliance and verification standards critical for financial services operations.
“We didn’t just craft an AI assistant,” says Robert Bueninck, Unzer’s CEO. “We’ve developed a secure, intelligent platform embodying our company’s core values. UnzerAI is now an indispensable asset within our organisation, enabling streamlined efficiency while ensuring data integrity.”
What Comes Next
Unzer’s roadmap extends beyond internal operations. Enhanced integration with merchant services and advanced analytics capabilities are planned for future releases. That suggests the company sees AI as a customer-facing competitive advantage, not just an internal efficiency tool.
The model they’ve created offers lessons for other enterprises struggling with AI adoption. Success appears to hinge on three factors: genuine utility for daily workflows, ironclad security frameworks, and seamless integration with existing systems.
Still, questions remain about scalability. Can this approach work for larger organizations with more complex hierarchies and varied security requirements? Early indicators suggest yes, but real-world testing at enterprise scale will provide clearer answers.
The broader implication is significant. If a financial services company can achieve 80% AI adoption while maintaining strict security standards, other industries have fewer excuses for failed implementations.
How do you think other companies should approach internal AI deployment? What lessons from Unzer’s success could apply to your industry?
