Build 'TokenIQ'—a Fintech/B2B SaaS platform that acts as an 'AI CFO' to optimize LLM spending. The platform would: 1) Integrate with LLM APIs (OpenAI, Anthropic, Google) to track token usage by team, project, and user; 2) Deploy AI-driven cost optimization (e.g., 'Switch from GPT-4 to Claude Haiku for this query'); 3) Provide real-time alerts (e.g., 'Team X exceeded budget by 200%'); 4) Offer 'ROI Scorecards' to justify AI investments (e.g., 'This chatbot saved $50K in customer support'). Monetization via enterprise SaaS ($25K/year), pay-per-token savings (e.g., '10% of savings'), and CFO dashboards.

Market Catalyst & News Trigger

"NEA’s Tiffany Luck says enterprises are still figuring out their AI ROI / 'Pretty Crazy' Token Usage Is Testing Bosses’ Bet on AI"

The Workflow Friction

Enterprises are wasting millions on AI projects due to 'tokenmaxxing'—uncontrolled spending on LLMs without ROI tracking. Current solutions (e.g., manual usage logs, vendor-provided dashboards) lack granularity and real-time alerts. Uber’s $10M+ AI budget blowout highlights the need for guardrails. The cost of unchecked AI spending includes wasted compute ($50K+/month), lost productivity, and CFO scrutiny.

Problem Summary

Real-world problem signal validation.

One-Shot MVP Builder Blueprint (48 Hours)

A dashboard showing 'Token Burn Rate' with three features: 1) 'Cost per Query'—breaks down spending by LLM and team; 2) 'Optimization Suggestions'—auto-recommends cheaper models; 3) 'ROI Leaderboard'—ranks AI projects by savings.

Recommended Developer Tech Stack

• SvelteKit
• Rust (for cost optimization)
• TimescaleDB
• OpenAI API
• Stripe Billing