Business
Like many large insurance providers, this company was dealing with a flood of daily customer queries.
The problem wasn’t just volume — it was quality.
Responses took too long.
Customers often received incomplete or inconsistent answers.
Support teams were overloaded, and internal knowledge was scattered across tools and documents.
The result?
Customer satisfaction was declining, and even simple questions required manual handling.
The company knew automation could help — but generic chatbots had already failed them.
They needed something smarter — an AI agent designed for their business, not a one-size-fits-all solution.
The Approach: Understanding the Process First
At 913.ai, we start every project the same way: by understanding how teams actually work.
Before building anything, we spent time mapping out:
How their customer service team handled incoming queries
Where their internal knowledge lived (documents, SOPs, FAQs, portals)
What made responses inconsistent or slow
What “good” looked like in their support process
This deep process analysis showed us that the real issue wasn’t lack of data — it was lack of accessibility and structure.
The knowledge existed.
Agents just couldn’t find it fast enough, and customers were left waiting.
The Solution: A Knowledge-Driven AI Agent
We built a custom AI agent trained on their internal knowledge base — policies, FAQs, SOPs, and past responses.
This agent could:
Read and understand thousands of internal documents
Retrieve precise, verified answers instantly
Adapt tone and structure for each query type
Follow guardrails to stay accurate, compliant, and on-brand
The goal wasn’t to replace people — it was to empower them.
Human agents could now focus on complex requests, while the AI handled repetitive queries confidently and consistently.
To ensure safety and accuracy, we built custom guardrails around the AI — defining what it could and couldn’t say, and how it should handle uncertain answers.
Every response was traceable, explainable, and aligned with company policy.
The Results: Happier Customers, Trusted AI
Six months after launch, the results spoke for themselves:
✅ 20,000+ customer queries answered automatically
✅ Response times cut by over 60%
✅ Customer satisfaction scores increased significantly
When the company surveyed their users, feedback was overwhelmingly positive — customers were finally getting clear, timely, and helpful answers.
Internally, trust in AI skyrocketed.
What started as a single pilot in one support team soon expanded across multiple departments — from claims to onboarding to policy servicing.
The same AI agent became the foundation for internal adoption of AI, showing how focused, well-structured agents can make teams more efficient and customers more satisfied.
The Takeaway: Specific AI Agents Build Real Trust
The success of this project came down to one idea: specificity.
Instead of deploying a general-purpose assistant, we built an agent that:
Understood the company’s unique processes
Spoke their customers’ language
Followed compliance guardrails automatically
When AI fits the workflow, people trust it.
And when people trust it, adoption follows naturally.
That’s the real power of specific AI agents — not just faster answers, but better relationships between companies, teams, and their customers.
Mehr Durchsatz, weniger Aufwand
Qualität wie im Fachbereich
Schnell einsetzbar und skalierbar
Meet growing workloads and changing priorities instantly—without the cost or delays of hiring.