za.
All work

GENERATIVE AI / AUTOMATION

Modnance

A conversational finance assistant connecting Gemini, messaging platforms, and automated transaction workflows.

Period
October 2026 — present
Status
In progress
Architecture illustration of the Modnance messaging, Gemini, automation, and database workflow

Architecture illustration of the Modnance messaging, Gemini, automation, and database workflow

The problem

Recording expenses and retrieving financial information can interrupt the flow of everyday conversations. Modnance explores a messaging-based way to manage those tasks.

Contribution & scope

An individual role is not specified in the available project material.

Project implementation

  • Gemini-based intent recognition, transaction extraction, and financial insights.
  • Discord and WhatsApp integrations using Python, REST APIs, and Evolution API.
  • n8n workflows connected to PostgreSQL for recording and retrieving financial activity.
  • Docker Compose services for modular deployment and system integration.

The approach

Google Gemini recognizes financial intents and extracts transaction details from messages. Python services connect Discord and WhatsApp to n8n workflows and PostgreSQL for transaction recording, balance inquiries, history, and financial reports.

Architecture & workflow

  1. Discord / WhatsApp
  2. Python + REST APIs
  3. Gemini intent extraction
  4. n8n workflow
  5. PostgreSQL
  6. Financial response

Technical challenges

  • Connecting natural-language requests to structured financial workflows.
  • Coordinating messaging integrations, automation, and persistent transaction records.

Outcomes

  • An ongoing implementation covering conversational transaction tracking, balance inquiries, history, and AI-generated reports.
  • A modular service architecture combining messaging, model inference, workflow automation, and a relational database.

Evidence & context

  • Connects messaging, Gemini, n8n workflows, and PostgreSQL in a modular finance assistant.

    Ongoing development described in the CV. This documents implementation scope, not audited financial accuracy or production adoption.

    CV — professional experience and projects

Scope & limitations

  • This implementation is still in development; no audited production deployment or financial-accuracy benchmark is documented.
  • The architecture illustration explains the intended workflow and is not a live product demo.

What I learned

  • Useful AI assistants need a clear connection between language understanding and the systems that execute a task.
  • Separating messaging, automation, and storage makes an integrated workflow easier to develop and maintain.

Built with

  • Python
  • Google Gemini
  • n8n
  • PostgreSQL
  • REST APIs
  • Docker