For my company Pioneer Growth AI DO THIS Smart approach - starting generalized gives you maximum market reach and lets you identify high-value verticals organically. Here's how I'd structure the generalized system: **Universal Prompt Interface:** - Single input field where users describe their business need in plain language - AI prompt interpreter that identifies required agent types and workflows - Dynamic workflow generation based on complexity and scope - Progress dashboard showing real-time agent activities **Core Agent Orchestra:** - **Business Analyzer**: Interprets user intent, market research, competitive analysis - **MVP Builder**: Rapid prototyping across web apps, mobile, SaaS tools - **Marketing Engine**: Multi-channel campaigns, content creation, audience targeting - **Sales Automation**: Lead generation, outreach sequences, conversion optimization - **Data Intelligence**: Analytics setup, performance tracking, insights generation - **Technical Executor**: Backend development, integrations, scaling infrastructure **Standardized Output Framework:** - Consistent deliverable formats regardless of industry - Universal metrics (conversion rates, engagement, ROI) - Cross-industry best practices library - Automated reporting templates **Self-Learning System:** - Success pattern recognition across different business types - Continuous optimization of agent coordination - Performance benchmarking against similar projects - Predictive success scoring for new prompts **Pricing Strategy:** - Freemium tier with basic agent hours - Pay-per-project scaling based on complexity - Enterprise subscriptions for ongoing agent teams - Success-based pricing tied to measurable outcomes How are you planning to handle the initial training data for agents to perform effectively across such diverse use cases? And what's your go-to-market strategy for acquiring those first users who'll help refine the system? - Initial Deployment
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