Building the Future of AI Safety
Preventing AI Hallucinations in Real-Time
TauGuard is developing enterprise-grade AI oversight technology to address the critical challenge of AI hallucinations—a problem that will define trust and reliability in the AI-powered future.
Current Status
Investor Documents
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The Vision
Making AI systems trustworthy and reliable for enterprise deployment
The Problem is Real and Growing
As organizations rapidly adopt AI systems, they face a critical challenge: AI hallucinations. These aren't minor glitches—they're false information generated with complete confidence, appearing credible but potentially catastrophic in regulated industries like healthcare, finance, and legal services.
Current solutions are inadequate. Manual review doesn't scale. Basic monitoring tools lack semantic understanding. And enterprises are left choosing between innovation speed and risk management—a choice they shouldn't have to make.
Our Solution: Real-Time AI Oversight
TauGuard provides enterprise-grade, real-time hallucination detection that integrates seamlessly into existing AI infrastructure. Using advanced semantic analysis and topological mathematics, we detect when AI systems drift from truth before errors reach users.
This isn't just about catching mistakes—it's about enabling enterprises to deploy AI with confidence, maintain regulatory compliance, and build user trust in AI-powered systems.
Massive Market Opportunity
The AI safety market is exploding as enterprises race to deploy AI while managing unprecedented risks
Problem vs Solution
Addressing a critical gap in the AI deployment stack
🚨 The Problem
- AI hallucinations cost enterprises billions in errors, reputation damage, and legal liability
- No existing tools provide real-time semantic understanding of AI outputs
- Manual review is impossibly slow for production-scale AI deployments
- Regulatory compliance requires comprehensive audit trails that don't exist
- Enterprises can't confidently deploy AI in high-stakes environments
✓ Our Solution
- Real-time hallucination detection with 2.3ms average latency
- Advanced semantic analysis using topological mathematics
- Comprehensive audit logging for full regulatory compliance
- Multi-model coherence layer for complex AI deployments
- 99.7%+ accuracy in prototype testing with low false positives
Product Development
Working MVP demonstrating core technology capabilities
Core Detection Engine
Proprietary semantic coherence analysis engine capable of real-time hallucination detection. Prototype demonstrates 99.7% accuracy on test datasets with sub-3ms latency.
Dashboard & Analytics Platform
Comprehensive monitoring dashboard with real-time analytics, drift detection visualization, and event logging system. Fully functional demo available.
API Architecture
RESTful API and WebSocket infrastructure for seamless integration with existing AI systems. Ready for enterprise testing.
Pilot Customer Testing
Seeking initial design partners to validate product-market fit and gather real-world performance data across different use cases and industries.
Founder & Vision
Bootstrapped from vision to working prototype
Michal Harcej
Self-funded TauGuard from concept to working MVP, personally developing the entire technology stack including the semantic analysis engine, real-time monitoring system, and enterprise dashboard.
Combining deep technical expertise with product vision to solve one of AI's most critical challenges. Committed to building a category-defining company in the AI safety space.
12-Month Roadmap
Clear path from pilot customers to revenue and scale
Validation & Pilots
- Secure 5-10 design partner pilot customers
- Gather real-world performance data
- Refine product based on feedback
- Complete seed funding round
Product & Team
- Launch commercial beta program
- Hire first 3-5 team members
- Enhance enterprise features
- Build initial customer success
Scale & Revenue
- Launch full commercial product
- Target $500K ARR by year-end 2026
- Expand to 20+ enterprise customers
- Prepare for Series A fundraise
Seed Funding Round
Raising seed capital to accelerate product development, acquire initial customers, and build a world-class team to capture this massive market opportunity.
Use of Funds
- 40% - Product Development: Enhance core technology, build enterprise features, SOC 2 readiness
- 30% - Team: Hire CTO, Sales Lead, ML Engineers
- 20% - Go-to-Market: Pilot programs, marketing, sales enablement
- 10% - Operations: Infrastructure, legal, compliance