Idea Executioner

The Problem

Every founder has the same bottleneck: they spend days or weeks researching whether an idea is viable before writing a single line of code. Market sizing, competitor analysis, technical feasibility, regulatory risk — it's 20+ hours of scattered Googling that produces inconsistent results. Most people skip it entirely and build blind.

What I Built

Idea Executioner runs 7 AI analysts in parallel — market sizing, competitor mapping, technical feasibility, monetization strategy, regulatory scan, go-to-market, and risk assessment. Each analyst specializes in one domain and produces structured findings. A synthesis layer merges all 7 reports into a single feasibility score with actionable next steps.

The key architectural decision: GPT-4o-mini for the analysts, Claude for synthesis. The analysts do narrow, well-scoped research where a smaller model excels. The synthesis needs reasoning across contradictory signals — that's where a larger model earns its cost. Total: ~$0.08 per full report vs $2-5 if you ran everything through a frontier model.

Built with Vercel AI SDK's streamObject for real-time streaming of each analyst's findings as they complete. The UI shows progress as each of the 7 analysts finishes, so you're never staring at a blank screen.

What You Can Steal From This

• Fan-out/fan-in pattern: Split complex analysis into parallel specialized agents, then merge. Works for any multi-dimensional evaluation — hiring scorecards, investment memos, product reviews.
• Model tiering by task complexity: Use cheap models for narrow extraction, expensive models only for synthesis and judgment. This alone cut costs 25x without quality loss on the individual analyses.
• Stream partial results: Users will wait 45 seconds if they can see progress. They'll abandon at 10 seconds of a blank spinner. Vercel AI SDK's streamObject with Zod schemas makes this trivial.