AI can generate 10,000 words in seconds. Most of them are garbage without human review. We know because we've built 6+ production AI systems — and the gap between 'AI-generated' and 'production-ready' is where most projects die.
The AI Slop Problem
You've seen it. Blog posts that say nothing in 2,000 words. Reports that hallucinate statistics. Code that passes a smoke test but breaks in production. This is AI slop — output that looks right but isn't.
The problem isn't the AI. It's the pipeline. When you treat AI output as final output, you get slop. When you treat AI output as a first draft that needs human review, you get quality.
Why Pure AI Output Fails
- ▸Hallucinations: LLMs confidently cite sources that don't exist. They invent statistics. They misattribute quotes. Without verification, these errors ship.
- ▸Brand misalignment: AI writes in a generic voice. It doesn't know your brand's quirks, your audience's pain points, or the political context of your industry.
- ▸Subtle errors: The kind that pass automated checks. A financial model with the right format but wrong assumptions. A competitive analysis that's accurate but misses the most important competitor.
- ▸Edge cases: AI handles the 80% case beautifully. The 20% — the edge cases that matter most in production — is where it breaks.
The Proxie Model: Agents for Scale, Humans for Soul
Every deliverable at Proxie goes through a three-layer quality pipeline:
- ▸Layer 1 — AI QA Agents: Fact-checking agent validates claims against sources. Security scanner (proxie.in) checks any generated code. Quality agent checks for logical consistency and completeness.
- ▸Layer 2 — Cross-Agent Review: A different agent reviews the output of the first. Like code review, but for all content types. Catches errors the producing agent is blind to.
- ▸Layer 3 — Human Review: A human expert reviews every deliverable before it ships. Checks strategic alignment, brand voice, nuance, and the things AI can't evaluate about itself.
What Humans Review
| Review Area | What We Check | Why AI Misses It |
|---|---|---|
| Factual accuracy | Every statistic, claim, and citation | LLMs hallucinate with confidence |
| Brand voice | Tone, terminology, cultural sensitivity | AI writes generic by default |
| Strategic alignment | Does this serve the client's actual goals? | AI optimizes for the prompt, not the business |
| Edge cases | Unusual scenarios, adversarial inputs | Training data doesn't cover your specific context |
| Political context | Industry relationships, competitive dynamics | AI doesn't read the room |
The Cost of NOT Having Human Review
A fintech startup shipped an AI-generated compliance document without human review. It contained 3 incorrect regulatory citations. The fix cost them 6 weeks and $40K in legal fees. The AI-generated draft took 10 minutes. The review would have taken 2 hours.
The math is simple: 2 hours of review vs. 6 weeks of cleanup. Human review isn't a cost center — it's insurance.
We're a new agency. We have a small but growing client base. But our team has shipped AI systems serving 50M+ users at companies like Zeta, CRED, and Vance (YC W22). We don't ship slop because our reputation is all we have right now — and we intend to keep it.
See Our Quality Guarantee
Every Proxie engagement includes human review as a non-negotiable part of our process. Not as an upsell. Not as an add-on. It's built into our 15-agent architecture because quality is the product. Want to see how it works? Check out our services or get in touch.