How I Wired Claude AI into My SaaS and What Actually Worked vs. What Was Just Hype

Short summary

Building a marriage biodata SaaS in India required solving two AI-shaped problems (multi-language translation and template recommendation) plus infrastructure challenges like rendering Devanagari and Urdu in PDFs. Claude solved translation via 15 prompt iterations to preserve proper names and cultural terms, but simple rule-based logic outperformed overthinking ML/fine-tuning for template selection. Real-world serverless architecture (Lambda + headless Chromium) worked well, though provisioned concurrency required careful cost tradeoff analysis.

• •Multi-language translation in Claude required 15 prompt iterations to preserve proper names and cultural terms (not literal translations)
• •Simple rule-based template recommendation outperformed ML-based approaches—sometimes the simpler logic is the right technical choice
• •Serverless + headless Chromium solved complex script rendering (Devanagari, Urdu), but provisioned concurrency cost tradeoffs had to be carefully managed

Generated with AI, which can make mistakes.