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
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