Budget Allocation for TV Infomercials
Simulation Inputs
12-Month TV Income vs Media
How Zero-Training AI™ Drives Media Buying
This demo is powered by a proprietary, Patent Pending, math-based decision engine. It is NOT an algorithm and does NOT use rules, training data, heuristics, or machine-learning models. Instead, it operates as a dynamical optimization system.
F(q, p) = (1/2) Σ pᵢ² − α Σ (ROIᵢ · qᵢ) + λ ( Σ qᵢ − 1 )²
• qᵢ = allocation weight for station i
• pᵢ = momentum of allocation change (decision inertia)
• ROIᵢ = front-end pull ratio combined with upsell amplification
• α = reward strength for profitability (user adjustable)
• λ = constraint strength enforcing total budget conservation
Each month, Zero-Training AI™ evolves its internal state (q, p) across multiple internal time steps. Media dollars flow naturally toward stations that generate higher total economic value — without rules, presets, or explicit instructions.
Although Zero-Training AI™ is built entirely from mathematics rather than data, it qualifies as real artificial intelligence because it performs autonomous decision-making. The system continuously evaluates thousands of possible allocations, responds to changing outcomes, and self-organizes its strategy in real time.
There is no training phase, no stored examples, and no static model. Intelligence emerges directly from the dynamics of the system itself. In short, Zero-Training AI™ does not execute decisions — it evolves them.
Legal & Technical Disclaimer
Zero-Training AI™ is a proprietary decision-optimization technology demonstrated here for informational and educational purposes only. The demonstrations on this website are simplified visual and interactive examples intended to illustrate conceptual behavior, not operational systems.
These demos do not represent physical simulations, real-world control systems, autonomous vehicles, medical devices, financial instruments, or deployed safety-critical systems. Any visual motion, scaling, or behavior shown is a mathematical or illustrative abstraction and should not be interpreted as modeling real energy, force, thrust, risk, or physical dynamics.
Zero-Training AI™ does not rely on training data, datasets, machine learning models, or statistical inference. Outputs shown are generated through deterministic mathematical evaluation and decision-selection logic applied at runtime.
No representation is made that these demonstrations are complete, production-ready, error-free, or suitable for any specific use without further engineering, validation, testing, and regulatory review.
This website and its contents do not constitute an offer to sell, a solicitation to buy, or a solicitation of investment interest in any security, product, or business opportunity. The site is not intended to solicit investors.
No Professional Advice Disclaimer
Nothing on this website constitutes legal, medical, financial, engineering, or professional advice of any kind.
Intellectual Property Notice
Zero-Training AI™, associated terminology, and underlying methodologies are proprietary and may be protected by patents, patent applications, trademarks, and other intellectual property rights. Unauthorized use or reproduction is prohibited.
Limitation of Liability
In no event shall the owners, developers, or affiliates of Zero-Training AI™ be liable for any direct, indirect, incidental, consequential, or special damages arising from the use of or inability to use these demonstrations.
No Regulatory Approval Disclaimer
These demonstrations have not been reviewed, approved, or certified by any regulatory authority and are not intended for regulated or safety-critical use.
No Warranties Disclaimer
All content is provided “as is” without warranties of any kind, express or implied, including but not limited to accuracy, completeness, fitness for a particular purpose, or non-infringement.