AI agents.
From questions to action.
We connect AI agents to financial data and systems. Explore how they can support specific tasks.
Try the demo ↓ Discuss your idea ↗Ask about your finances
Choose a question about a fictional account. See the answer and the transactions behind it.
This is how a typed or spoken question could look. The demo types automatically; no microphone is active.
PLN · October 2026 · no sign-in
One question.
A clearer view of your finances.
Choose a question to see the result, calculations and source transactions.
Tell us which process you want to simplify. Together, we will define the scope of a first prototype.
How does it work?
Question → AI agent → MCP tool → API → data → answer
This is the intended integration flow. This demonstrator runs calculations in your browser using a local fictional transaction dataset. It uses neither an AI model nor an MCP server, and connects to no bank.
An AI agent performs a task.
MCP connects its tools.
A model helps interpret a question and prepare an answer. An agent uses exposed capabilities, while the backend enforces permissions. Model Context Protocol (MCP) standardises communication between AI applications, tools and data.
Read Michał Drynkowski’s article ↗Bring an AI agent
into your business.
We help select a task for an AI agent, build a prototype and connect it to your systems. Together, we define which data and tools it can use.
Discovery
Choose the use case, data and measurable outcome.
Architecture
Design MCP tools, access controls and responsibilities.
Prototype & integration
Validate the solution, connect systems and plan ongoing support.
The QRQS.eu PSA team combines its experience in co-developing PolishAPI with financial systems architecture and integration. QRQS.eu ↗
The demonstrator only reads synthetic data. A deployment using real data requires defined roles, processing grounds and access permissions. MCP is a technical interface, not permission to access an account.