Spreadsheets are great until you have more than ten leads and a team that actually needs to collaborate. In a recent DEV.to post, developer Nithya Iyer detailed how they used Vitara.ai to build a custom CRM from scratch, bypassing the usual weeks of boilerplate code and database schema design. The goal wasn’t to replicate Salesforce’s bloat, but to create a focused MVP that handled leads, deal stages, and follow-ups without the overhead of manual frontend and backend wiring.

The Prompting Strategy: Workflow First, Code Later

Iyer’s approach highlights a critical shift in AI-assisted development: defining the workflow before touching the tool. Instead of asking Vitara.ai to "build a CRM," which often yields generic templates, Iyer mapped out the exact sales process first. This included defining user roles, lead data fields, and pipeline stages (New Lead β†’ Qualified β†’ Proposal β†’ Negotiation β†’ Won/Lost). By feeding this structured logic into the initial prompt, the AI generated a functional skeleton that matched the specific business logic, rather than a generic contact database.

Iterative Refinement Over One-Shot Generation

The build process relied heavily on iterative, small-scale prompts rather than massive, monolithic requests. Iyer started with the dashboard, adding cards for active opportunities and pipeline value, then moved to lead management with search and filter capabilities. Subsequent prompts focused on granular improvements, such as "Add a filter for lead owner" or "Show overdue follow-ups on the dashboard." This method allowed for rapid testing and adjustment, turning the AI into a responsive junior developer that could tweak specific components without breaking the entire application.

Key Takeaways

  • Define the user workflow and data structure before prompting; vague requests yield generic, unusable scaffolding.
  • Use small, specific prompts for feature additions (e.g., "Add expected deal value") rather than rewriting the whole app.
  • Prioritize core CRUD functionality and pipeline visibility before attempting advanced AI features or integrations.
  • Product thinking remains essential; the AI builds the interface, but the developer must still decide which data points matter to the sales team.

The Bottom Line

For internal tools and MVPs, AI builders like Vitara.ai are finally viable alternatives to writing boilerplate CRUD apps, provided you treat them as execution engines for well-defined logic rather than magic wands for vague ideas.