How MarvelX’s Agentic AI Transformed Embedded Insurance

At MarvelX, we believe the hardest part of deploying AI in insurance isn’t technical complexity. It’s earning trust.

That belief was put to the test in June 2025 when we partnered with leading embedded insurance providers from Europe. This wasn’t just another pilot. It was a real-world opportunity to prove that our AI claims agent could deliver under pressure and meet the high reliability standards of modern insurance operations.

Embedded insurance demands instant, digital-first customer experiences. That makes the space ideal for automation, but also uniquely unforgiving. A system that technically works is not enough. It has to adapt quickly, respond accurately, and build confidence with every decision.

Here’s how MarvelX helped insurers scale their claims operations from pilot to full production in a matter of weeks using Agentic AI.

The Challenge: Scale Without Losing Control

Our clients needed to process a growing volume of travel claims triggered by events such as flight delays, strikes, and missed connections. Our target was clear: increase their claims processing capacity by 10x within a year.

Several options were on the table:

• Hiring more claims handlers would be too slow and expensive
  
  

• Outsourcing to a third-party administrator would mean losing access to critical data and decision control
  
  

• Rigid, rules-based automation would break under real-world edge cases

They needed a system that could scale quickly, handle complexity, and remain trustworthy and compliant. That’s where MarvelX came in.

The Agentic AI Approach

Our engineering team started by integrating directly into the insurer’s claims system and CRM. Using our secure API platform, we enabled claims ingestion and processing inside their cloud environment. Data privacy and compliance were preserved from day one.

Next, we implemented MarvelX’s claim-to-decision pipeline. This framework allowed us to customize the AI’s behavior to match the company’s policies, tone of voice, and regulatory standards. Rather than imposing a one-size-fits-all tool, we collaborated closely with the claims team to co-develop a system tailored to their exact needs.

Throughout this process, we kept the human team involved. Their feedback shaped each iteration of the agent. As the system learned from their decisions and handled increasing complexity, it quickly became a trusted co-pilot in daily claims operations.

Results and Performance

The impact was immediate and measurable. Within three weeks of deployment, the agent achieved:

• 90 percent decision accuracy, up from an initial 25 percent
  
  
  

• Average claim processing time reduced to an average of 1 minute from 1 day
  
  
  

• 10× increase in claims processing capacity without increasing headcount
  
  

"MarvelX’s AI agent has become an essential part of our claims workflow," Claims Lead

"It processes routine claims in minutes and gives our team time to focus on complex ones. It’s fast, reliable, and easy to trust."

From First Meeting to Full Deployment

1. Discovery and Alignment

Our CEO met with the insurer’s executive team to understand their growth goals and operational constraints. We aligned on the need to scale without sacrificing control, compliance, or accuracy.

2. Technical Validation

Our engineering leads worked closely with their tech team to map out the full claims process. We ensured our system could integrate cleanly into their data architecture.

3. Sandbox Testing

A secure test environment was launched using real policies and sample claims. This allowed the team to verify the AI’s behavior and provide early feedback before touching live data.

4. Shadow Mode Rollout

The agent was deployed in production shadow mode. It processed real claims in parallel with human agents, who reviewed and rated its decisions without any customer-facing impact.

5. Weekly Iterations

With feedback from the claims team, we released weekly updates to improve accuracy and responsiveness. The agent’s accuracy increased from 25 percent to 90 percent in just three weeks.

6. Full Integration

Once performance and reliability were established, the agent's decisions and recommendations were surfaced directly inside the live claims platform. Human agents now rely on the AI to handle routine cases while staying in control of exceptions.

Key Lessons Learned

Trust Requires Transparency

We earned the team’s trust not just with performance, but with transparency. The agent's decisions were explainable and clearly aligned with internal logic and standards.

Fast, Clean Integration is Possible

Despite the complexity of insurance systems, integration was efficient. Our flexible API architecture and modular pipeline made it easy to connect to existing tools and workflows.

Feedback Loops Are Game Changers

Close collaboration between claims handlers and our engineers created a fast and effective feedback loop. Weekly iterations drove meaningful improvements and built confidence in the system.

Adaptability Wins in Embedded Insurance

This market evolves fast. New partners, new products, and new customer journeys require systems that can adapt. Agentic AI is designed to learn and update quickly, without needing to start from scratch each time.

Final Thoughts

This rollout proves that AI doesn’t have to take years to deliver value in insurance. In just a few weeks, a high-growth insurer moved from experimentation to full-scale deployment using a system that is fast, explainable, and trusted by the team.

MarvelX’s Agentic AI is not limited to travel claims. The same co-pilot framework can support any line of business, from property damage to specialty claims and beyond.

If you're looking for an AI that works with your people and earns their trust, let’s talk.