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Model Context Optimizer: Intelligent Token Budget Management for LLM Apps

Engineering teams waste 40-60% of LLM costs on redundant or low-value context, but manually optimizing prompts is tedious and error-prone.

App Concept

  • Automated context window analyzer that identifies redundant, low-value, or unnecessary tokens in your prompts
  • AI-powered summarization that condenses context while preserving semantic meaning and accuracy
  • Dynamic context selection based on query relevance using semantic search and retrieval strategies
  • Real-time token budget allocation across system prompts, examples, and user input
  • A/B testing framework proving cost savings don't hurt quality metrics
  • Template library of optimized prompt patterns for common use cases (RAG, agents, code generation)

Core Mechanism

  • SDK intercepts LLM calls and analyzes prompt structure (system, user, assistant messages)
  • Semantic analysis identifies redundant information, low-relevance context, and verbose formatting
  • Intelligent compression using extractive summarization, entity consolidation, and format optimization
  • Query-aware context selection: only include relevant documents from your knowledge base
  • Token budget enforcement: automatically trim context when approaching limits
  • Performance monitoring: track cost savings vs. quality impact with statistical significance testing
  • Optimization recommendations: "36% of your tokens are system prompt boilerplate—here's a better version"
  • Visual token profiler showing where every token is going in your requests

Monetization Strategy

  • Free: Analyze up to 100K tokens/month, view optimization recommendations
  • Starter: $149/month for 5M tokens optimized, automatic compression, basic templates
  • Professional: $499/month for 50M tokens, custom compression strategies, A/B testing, API access
  • Enterprise: $2,000+/month for unlimited tokens, dedicated optimization consulting, custom model fine-tuning
  • Revenue share: Take 15% of token cost savings for first year after implementation

Viral Growth Angle

  • Public calculator: "Input your prompt, see instant optimization suggestions"
  • Monthly transparency reports: "We've saved customers $2.3M in LLM costs this quarter"
  • Open-source token profiler tool that becomes standard for prompt engineering
  • Viral case studies: "How we reduced RAG costs by 67% without losing accuracy"
  • Token optimization challenges: community competitions for most efficient prompt designs
  • Browser extension showing token costs in real-time for ChatGPT/Claude web interfaces
  • Conference talks revealing shocking token waste statistics across industries

Existing projects

Evaluation Criteria

  • Emotional Trigger: Limit risk (control runaway AI costs), be prescient (optimize before costs spiral)
  • Idea Quality: Rank: 7/10 - Clear ROI and pain point, but requires user education on token economics
  • Need Category: ROI & Recognition Needs - Demonstrating measurable cost savings and resource efficiency
  • Market Size: $1.5B+ (cost optimization subset of $12B AI Operations market)
  • Build Complexity: Medium - Requires NLP for semantic analysis, token counting libraries, integration patterns
  • Time to MVP: 2-3 months with AI coding agents (basic analyzer + compression for 2 providers), 4-5 months without
  • Key Differentiator: Only platform combining automated context optimization, semantic preservation verification, and A/B testing specifically for token budget management