CASE STUDY 001
In-app AI Agent for banking
Designing an assistant inside a banking app that answers with public information without performing transactions or compromising sensitive data.
01 · PROBLEM
The information exists, but finding the right answer is still difficult.
Customers move between pages, FAQs, and service channels to understand products, requirements, and processes. The goal is to reduce that friction without opening transactional access in the first version.
PRODUCT PRINCIPLE
Build trust before increasing autonomy.
The MVP answers only from approved knowledge and escalates when it lacks sufficient evidence.
02 · SOLUTION
A read-only agent inside the channel customers already use.
The experience lives inside the banking application and understands natural questions about products and institutional information.
- Approved public information
- Evidence-based answers
- No access to personal data
- No transaction execution
- Controlled escalation
03 · ARCHITECTURE
Clear layers to reduce risk and enable evolution.
01Mobile channelCustomer experience
→02AI GatewayPolicies and context
→03MastraOrchestration
→04RAGApproved knowledge
→05ModelControlled response
04 · MEASUREMENT
Success is not measured by completed conversations alone.
01Accuracy
02Evidence coverage
03Latency
04Correct escalation
Are you exploring AI agents for your product?
Let's talk ↗