CreditRiskMonitor
NASDAQ listed credit risk dataIn pilotLetting customers ask an AI about credit data, safely
Customers point their own AI assistant, such as ChatGPT or Claude, straight at our credit risk data. Every request is checked against what that customer actually pays for.
Click any box in the diagram to see why it is there
Problem
Credit risk data is sold in slices. A customer pays for a defined part of it, not all of it. The moment their AI assistant starts asking for data directly, the question of what they are allowed to have has to be answered on every single request, not once when they log in.
Approach
A server built on the Model Context Protocol, an open standard for letting an AI assistant fetch data directly. The assistant asks, the server works out who is asking and what they may have, and returns only that.
Architecture
First it confirms who is asking. Then it checks what that customer actually pays for. Only then does it fetch anything. Every request is written to a permanent log, so there is always a record of what left the platform and for whom.
Constraints and scale
Each customer is walled off from every other, so their data can never reach anyone else. Usage caps are applied per customer, so one heavy user cannot slow the service down for everybody.
At a glance
- Built on the Model Context Protocol, an open standard that lets an AI assistant call a company data directly instead of a person logging into a portal.
- Three checks on every single request: who is asking, what they are entitled to, and a permanent record of what was sent to them.
- Each customer is walled off from every other. One customer data can never surface for another, and no single customer can slow the service down for everyone else.