TL;DR Replaced a costly, slow, and unscalable manual review process with an LLM-powered AI Agent—achieving 10x cost savings, 100x speedup, and zero scaling friction, while matching human accuracy.
A company I worked with relied on a flexible external workforce (think platforms like AWS MTurk, Prolific, Clickworker, etc.) to manually review product matches. While performant enough, the process was costly, slow, and couldn’t scale on demand. Scaling up meant recruiting, testing, and onboarding people. Scaling down led to churn of these people. As commercial success rised in the company, this bottleneck had to go.
I led and implemented a solution to replace this manual labor with an AI Agent. It needed to match human accuracy, reduce cost and latency, and solve the scaling problem—without introducing risk.
I built an LLM-based AI Agent that could autonomously decide to approve, reject, or escalate reviews to humans. Human-in-the-loop was implemented as one of several tools the agent could invoke, based on confidence and case complexity. I crafted a stratified real-world test dataset to benchmark against human performance, engineered dynamic prompting strategies, and built high-throughput execution using multithreading. Missing data remained the hardest problem—classic garbage in, garbage out situation.
The agent matched human accuracy while delivering:
- 10.73x cost reduction
- 107.6x speedup per review
- Fully eliminated scaling overhead
No employees were displaced—this replaced only low-level external labor. The success has led to a follow-up project, where I’m now automating a second major manual workflow using the same AI Agent pattern.