Hey HN,
I built BiasGuards after watching teams spend 13+ hours manually debugging flawed strategic reasoning that could be caught in 30 seconds.
The Problem: Teams analyzing data, proposals, and competitive intelligence fall into systematic cognitive biases—confirmation bias, tunnel vision, outcome bias—that distort decision-making and lead to failed strategies. Analysis for complex projects costs $200K+, and most AI tools have 16-82% hallucination rates, creating trust issues.
What BiasGuards Does:
• Analyzes documents in <300ms per page
• Detects 800+ bias patterns (confirmation bias, anchoring, belief persistence)
• Identifies logical fallacies in proposals (hasty generalization, post hoc, ad hominem)
• Integrates with existing workflows
• Provides expert-validated confidence scoring
Early Results:
• 15-40% reduction in flawed strategic decisions
• 40% analysis cost reduction
• 15-25% improvement in proposal success rates
• 20-35% prevention of failed initiatives through rigorous reasoning
Tech Stack: Built with privacy-first architecture—we don't store proprietary data, only pattern metadata for bias detection improvements. Uses linguistic pattern matching combined with cognitive science frameworks to identify bias indicators.
Why This Matters: Every year, cognitive biases in business reasoning lead to failed products, bad strategic decisions, and millions in wasted resources. We're not replacing analysts—we're giving them X-ray vision for flawed reasoning patterns.
Sign up for FREE: www.biasguards.ai
Would love feedback from anyone working in AI/ML, decision science, product strategy, or who's interested in cognitive bias detection.
Technical Implementation: The system uses a multi-layer approach:
1. NLP-based pattern recognition for linguistic bias markers
2. Logic graph analysis for fallacy detection
3. Bayesian confidence scoring calibrated against expert validation datasets
4. Real-time processing with <300ms latency on standard documents
What We're Not Doing: Unlike most AI tools, we don't generate content. No LLM hallucinations. Just pattern detection against established cognitive science frameworks.
Open Questions:
• What other bias patterns would be most valuable in your workflow?
• How do you currently handle bias detection in strategic decisions?
• What would make this more useful for technical teams?
Some Context on the Cognitive Science: We built this on decades of research from Kahneman, Tversky, Gigerenzer, and others. The bias detection patterns are based on peer-reviewed frameworks, not vibes.
Confirmation bias alone causes an estimated 67% of failed strategic initiatives. Anchoring bias affects negotiations and pricing decisions. Tunnel vision prevents teams from considering alternative solutions.
Privacy & Security:
• End-to-end encryption for document uploads
• No persistent storage of user documents
• Only aggregated, anonymized pattern data retained
• SOC 2 Type II compliant
• GDPR compliant
Happy to answer questions about the architecture, methodology, research foundation, or use cases. Also open to criticism—if you think this approach won't work, I want to understand why.
Thanks for reading!
Hey HN, I built BiasGuards after watching teams spend 13+ hours manually debugging flawed strategic reasoning that could be caught in 30 seconds. The Problem: Teams analyzing data, proposals, and competitive intelligence fall into systematic cognitive biases—confirmation bias, tunnel vision, outcome bias—that distort decision-making and lead to failed strategies. Analysis for complex projects costs $200K+, and most AI tools have 16-82% hallucination rates, creating trust issues. What BiasGuards Does: • Analyzes documents in <300ms per page • Detects 800+ bias patterns (confirmation bias, anchoring, belief persistence) • Identifies logical fallacies in proposals (hasty generalization, post hoc, ad hominem) • Integrates with existing workflows • Provides expert-validated confidence scoring Early Results: • 15-40% reduction in flawed strategic decisions • 40% analysis cost reduction • 15-25% improvement in proposal success rates • 20-35% prevention of failed initiatives through rigorous reasoning Tech Stack: Built with privacy-first architecture—we don't store proprietary data, only pattern metadata for bias detection improvements. Uses linguistic pattern matching combined with cognitive science frameworks to identify bias indicators. Why This Matters: Every year, cognitive biases in business reasoning lead to failed products, bad strategic decisions, and millions in wasted resources. We're not replacing analysts—we're giving them X-ray vision for flawed reasoning patterns. Sign up for FREE: www.biasguards.ai Would love feedback from anyone working in AI/ML, decision science, product strategy, or who's interested in cognitive bias detection. Technical Implementation: The system uses a multi-layer approach: 1. NLP-based pattern recognition for linguistic bias markers 2. Logic graph analysis for fallacy detection 3. Bayesian confidence scoring calibrated against expert validation datasets 4. Real-time processing with <300ms latency on standard documents What We're Not Doing: Unlike most AI tools, we don't generate content. No LLM hallucinations. Just pattern detection against established cognitive science frameworks. Open Questions: • What other bias patterns would be most valuable in your workflow? • How do you currently handle bias detection in strategic decisions? • What would make this more useful for technical teams? Some Context on the Cognitive Science: We built this on decades of research from Kahneman, Tversky, Gigerenzer, and others. The bias detection patterns are based on peer-reviewed frameworks, not vibes. Confirmation bias alone causes an estimated 67% of failed strategic initiatives. Anchoring bias affects negotiations and pricing decisions. Tunnel vision prevents teams from considering alternative solutions. Privacy & Security: • End-to-end encryption for document uploads • No persistent storage of user documents • Only aggregated, anonymized pattern data retained • SOC 2 Type II compliant • GDPR compliant Happy to answer questions about the architecture, methodology, research foundation, or use cases. Also open to criticism—if you think this approach won't work, I want to understand why. Thanks for reading!