AI-Powered Credit Card Recommendation System with OpenAI GPT, Telegram & Google Sheets

Overview Confused about which credit card to actually get or swipe? With 100+ cards on the market, hidden caps, and milestone rules, most people end up leaving rewards, perks, and cashback on the table. This workflow uses n8n + GPT + Google Sheets + Telegram to recommend the best credit card for each user's lifestyle in under 3 seconds, while keeping the logic transparent with a value breakdown. What does this workflow do? This workflow: - Captures User Inputs - Users answer a 7-question lifestyle quiz via Telegram. - Stores Responses - Google Sheets logs all answers for resumption & deduplication. - Scores Answers - n8n Function nodes map single & multi-select inputs into scores. - Generates Recommendations - GPT analyses profile vs. 30+ card dataset. - Breaks Down Value - Outputs a transparent table of rewards, milestones, lounge value. - Delivers Results - Top 3 card picks returned instantly on Telegram. Why is this useful? Most card comparison tools only list features; they don't personalize or calculate actual value. This workflow builds a decision engine: - Personalized – matches lifestyle to best-fit cards - Transparent – shows value in real currency (rewards, milestones, lounges) - Fast – answers in under 3 seconds - Organized – Google Sheets keeps an audit trail of every user + dedupe Tools used - n8n (Orchestrator): Orchestration + logic branching - Telegram: User-facing quiz bot - Google Sheets: Database of credit cards + logs of user answers - OpenAI (GPT): Analyses user profile & generates recommendations Who is this for? - Fintech product builders – see how AI can power recommendation engines - Cardholders – understand which card fits their lifestyle best - n8n makers – learn how to combine Sheets + GPT + chat interface into one workflow How to adapt it for your country/location This workflow uses a credit card dataset stored in Google Sheets. To make it work for your country: - Build your dataset – scrape or collect card details from banks, comparison sites, or official portals - Fields to include: Fees, Reward rate, Lounge access, Forex markup, Reward caps, Milestones, Eligibility. - You can use web crawlers (e.g., Apify, PhantomBuster) to automate data collection. - Update the Google Sheet – replace the India dataset with your country's cards. - Adjust scoring logic – modify Function nodes if your cards use different reward structures (e.g., cashback %, miles, points value). - Run the workflow – GPT will analyse against the new dataset and generate recommendations specific to your country. This makes the workflow flexible for any geography. Workflow Highlights - End-to-end credit card recommendation pipeline (quiz → scoring → GPT → result) - Handles single + multi-select inputs fairly with % match scoring - Transparent value breakdown in local currency (rewards, milestones, lounge access) - Google Sheets for persistence, dedupe & audit trail - Delivers top 3 cards in <3 seconds on Telegram - Fully customizable for any country by swapping the dataset