We built TruthGuard, an AI-powered validation platform designed to detect synthetic, low-quality, or fraudulent survey responses in large-scale research datasets — a $10B+ issue in global data collection.

TruthGuard runs a multi-stage validation pipeline combining:

LLM-based semantic verification (OpenAI, Anthropic, Azure models)

Vector similarity scoring using Qdrant/Chroma

Anomaly & pattern detection for response duplication

Adaptive thresholding tuned with live dataset feedback

It processes 100K+ responses per day with 99%+ accuracy, cutting operational costs by over 60% for our enterprise clients.

I’d love to get feedback from this community — especially around:

Improving real-time validation at scale

Better approaches for prompt consistency between multiple LLMs

Efficient ways to benchmark accuracy on mixed human + AI datasets

Code architecture and system design overview (non-confidential parts) are here: github.com/vivekjaiswal-ai/truthguard

Thanks for reading — open to ideas, critiques, and collaborations!

— Vivek