+ THE EXECUTION LAYER FOR AI × FINANCE

Make financial
services
executable by AI.

AI understands the intent.
CADON connects it to a controlled financial action.

We’re building the infrastructure between AI assistants and financial institutions — with sensitive customer workflows outside the model and the institution in control.

Early-stage infrastructure. Connected MCP demo available.
THE MCP PRODUCT FLOWFictional demo data
AI ChatGPT + CADON
Show all offers for a €25,000 new-car loan.

The connected assistant checks safe context, then CADON displays its provider cards.

prepare_cadon_contextthen, when readyshow_car_loan_bank_options

This conversation example illustrates the actual plugin’s tool sequence. No application form is collected in chat.

THE INTERFACE IS CHANGING.
THE INSTITUTION STAYS IN CONTROL.
AI assistants → CADON → Financial institutions
01 / FROM CONVERSATION TO ACTION

Intent is the beginning.
Execution is the infrastructure.

An AI can understand a financial request. Turning it into an authorized action needs a workflow the institution can control.

  1. 01

    Discover

    Identify the relevant financial product or workflow.

  2. 02

    Initiate

    Create a session for the selected institution.

  3. 03

    Authorize

    The user verifies identity, reviews details and gives consent.

  4. 04

    Execute

    The institution processes the authorized request.

  5. 05

    Return

    Send the necessary outcome back to the assistant.

Target execution lifecycle. The connected MCP demo uses mock providers and does not execute live financial actions.

02 / A CLEAR TRUST BOUNDARY

Sensitive data stays
where it belongs.

The assistant needs the intent and an appropriate status. Identity documents, account credentials and application details belong in controlled financial systems.

AI ENVIRONMENT

Conversation context

IntentProduct typeRequested amountSession referenceAllowed status

Only the context needed to initiate and follow the workflow.

CONTROLLED
HANDOFF
FINANCIAL EXECUTION ENVIRONMENT

Identity. Consent. Authorization.

Identity & KYCFinancial informationDocumentsAuthenticationApplication data

Collected through CADON’s intended secure session or the institution’s systems.

Data-minimisation is an architecture principle. Production isolation and controls must be validated in each integration.
03 / TWO ECOSYSTEMS. ONE LAYER.

Your workflows.
Every AI channel.
One integration.

Financial institutions define which products and actions are available. AI developers get a consistent way to initiate them.

CADON’s platform direction is to connect both sides through shared execution infrastructure, rather than rebuild each connection.

Explore an integration
04 / BUILDING THE EXECUTION LAYER

Control at every step.

Our prototype makes the handoff tangible. The broader platform is being developed around these capabilities.

Agent connectivity

A shared interface between AI channels and institution workflows.

Platform direction

Controlled sessions

A dedicated environment for sensitive financial interactions.

Platform direction

Institution policies

The institution defines actions, limits and required approvals.

Platform direction

Consent & evidence

Explicit authorization and traceable events across the workflow.

Platform direction
05 / SEE THE CONNECTED MCP PRODUCT

The actual handoff.
From ChatGPT to CADON.

Talk to the live assistant on our website, or use CADON inside ChatGPT. Both connect to the same MCP and secure execution flow. Providers and outcomes remain fictional demo data.

CADON MCP browser sessionDemo
Actual CADON car-loan session launched by the connected MCP service
  1. 1. Start in ChatGPTAsk for car financing with the CADON app enabled. It checks safe context before displaying provider cards.
    prepare_cadon_context → show_car_loan_bank_options
  2. 2. Continue in CADONOpen the service’s browser session for identity, application details and consent. The website assistant uses the same MCP to prepare your session.
    get_cadon_demo_link
  3. 3. Retrieve the statusAfter the fictional workflow, the assistant reads the appropriate completion signal.
    get_demo_result

Actual screenshot of the connected MCP service. Provider cards in the interactive demo come from the actual MCP response.

Website launch access requires a code.
06 / ONE ARCHITECTURE. MANY WORKFLOWS.

Start with finance.
Build beyond a single product.

Car financing is the flagship MCP walkthrough. The connected plugin also offers fictional lending, savings and payment journeys.

01

Auto finance

“I need €25,000 to buy a car.”

Connected MCP demo
02

Personal lending

“I need financing for renovations.”

Connected MCP demo
03

Savings

“I want to open a savings account.”

Connected MCP demo
04

Payments

“I want to send money.”

Connected MCP demo
05

Insurance

Initiate a controlled insurance workflow.

Platform vision
07 / FOR DEVELOPERS

A financial action.
A controlled response.

The connected prototype exposes real MCP tools. This is the actual tool-call request used by the website to prepare a car-finance launch. Initialize the MCP transport before calling tools.

Connected MCP service: live tool calls Direct launch + status: server-side MCP integration
Discuss developer access
Actual MCP tool-call request
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "get_cadon_demo_link",
    "arguments": {
      "scenario": "car_loan",
      "market": "BE"
    }
  }
}

The service returns a launch reference and browser URL. Open the URL to begin the fictional flow; use get_demo_result to retrieve its minimal status. No fictional SDK is shown.

BUILT FOR ACTIONS, NOT JUST ANSWERS

User authorization

Financial actions need an explicit approval flow.

Institution control

The institution owns its products, requirements and execution.

Minimum disclosure

Return only the information the AI needs to follow the action.

Design principles, not certifications or a guarantee of regulatory compliance.

BUILD WITH CADON

Bring your financial
workflows to AI.

For financial institutions exploring a new distribution channel, and AI teams that need a controlled path to execution.

Discuss your use caseExplore the prototypeScope an integration

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