Ivan Antonov - payments from strategy to working software
What is it? A natural-language chatbot answering questions about transaction data. Merchant payment teams can ask questions and get answers in seconds, without the need to rely on other BI or analytics teams
What problem does it solve? Payment teams get real-time visibility on approval rates, provider outages and costs, as well as recommended actions - something that previously required several days of analysis
How it works? The chatbot runs an LLM query across the transaction dataset. On this page, a synthetic dataset with 100k transactions is used, reproducing the statistical patterns of a book at this scale. In a production setting when deployed at a merchant, the chatbot can be connected to the company's systems and data sources
Read more and try it →What is it? A mock payment gateway modeling five processor archetypes (global acquirer, regional bank processor etc.) with realistic patterns for decline codes, 3DS flows etc.
What problem does it solve? When merchant integrates a new provider, provider sandboxes always work correctly, however reality in is different. This tool simulates what merchants actually see in production, so integrations can be built and tested correctly before going live
How does it work? The simulator is built as an API-first service in Python/FastAPI, with a payment state model, idempotency handling and webhook delivery logic that reproduces realistic transaction outcomes. More than 600 payment patterns are encoded into the simulation layer to mimic the approval, decline, latency and retry behaviours merchants see in production. The simulator can be either installed in merchant infrastructure, or called through an API, and it also connects into Case 1’s chatbot as a routing-recommendation backend
Read more and try it →What is it? A merchant-side recovery decision engine that scores card declines and recommends whether to retry, skip, or defer the next attempt. It is designed to plug into the merchant payment flow
What problem does it solve? Many transactions which were initially declined do succeed if reattempted, but the retry decision logic is complex, and blind retries increase costs and negatively affect the customer experience. This engine scores declines, prioritizes those with the highest expected value and shows why each recommendation was made.
How does it work? A LightGBM model is trained on retry-chain data derived from a synthetic payment book, then wrapped in a business policy layer that optimizes net recovery value. Merchants can deploy it in their own stack, call it through an API, and use the portfolio demo to inspect sample decisions and explanations
Read more and try the demo →I build and optimize payments for financial institutions, fintechs and merchant payment teams, from strategy to working software