AI for fintech, where a mistake costs more than a delay.

I work as AI Automation Specialist at Conio. I designed a volatility and liquidity engine on XAU/USD; a validation bot trading only on its signals recorded a 54% win rate at a 1.4:1 risk/reward ratio. That is an engine result, not financial advice.

1. 01Discovery. Before any code: interviews, a look at the data you already have, technical and business constraints on the table.
2. 02Architecture. Data schema, component boundaries and failure points decided on paper, when changing them costs an hour and not a month.
3. 03Build. Short iterations with working demos instead of one delivery at the end. AI speeds things up where it makes sense; it never replaces review and testing.
4. 04Handoff & scaling. Documentation, runbooks and a system your team can run without depending on me. No lock-in.

Every project is priced after a scoping call: the same word, "chatbot" or "agent", can mean a two-week prototype or a system in production for thousands of users. What moves the price:

• number and quality of data sources;
• regulatory and audit requirements;
• update frequency (daily, hourly, real time);
• integration with existing systems and reporting.

The first call is free and ends with a clear answer: whether the project makes sense, roughly how big it is, and what to do first.