Scruuge.AI — AI Cost Optimization
Security and ops burden don't shrink at lifestyle scale.
Scruuge.AI — an AI cost optimization SaaS that sits between users' applications and their AI providers. Routes API calls through OpenRouter to the cheapest model meeting a quality threshold. Charges 20% of verified savings only — if no savings, no charge. OpenAI-compatible drop-in proxy with Zero Data Retention. Two routing tracks: same-quality-lower-cost (Track A, default) and cheapest-possible (Track B, opt-in). Target: indie developers spending $50-500/month and small businesses spending $200-2K/month on LLM API costs.
Two rubrics. Two verdicts.
The audit didn't change its mind. It answered a different question.
Could this be a fundable, scaling business?
Could one person at <10h/week reach $1–5k/month?
Same facts. Inverted signal weights. The audit doesn't reconsider the evidence — it reweights it. What counts as a positive signal under one rubric can be a fatal negative under the other:
- Small TAMconcern (no path to scale)fine (only ~100 customers needed at $20/mo)
- No moatconcern (incumbents will copy)fine (organic discovery + niche knowledge IS the moat)
- 6–12 month sales cycleacceptable for B2B SaaSfatal (no revenue within time budget)
- Ops linear to revenuefixable with team at scalefatal (no time budget for support)
- Security-review burdenamortize over many customersfatal (same friction at any scale)
Why this matters for honesty. A single-rubric service that defaults to venture framing would tell a stay-at-home parent or a side-hustler that their idea has “no moat” or “small TAM” — technically correct, but irrelevant to their actual goal. That's being right inside the wrong question. We'd rather ask the question first.