Zero-Training AI™ Demos
Budget Allocator
Automatically allocate a starting budget across hundreds of meda buys for Half Hours of television time for infomercials to maximize overall NET PROFIT. This is Real Media Buying that makes serious money!
Try Budget Allocator
Let's Go To Mars!
A spacecraft departs Earth for Mars under real constraints. Adjust thrust limits, fuel, time-of-flight, gravity strength, perturbations, and goal tolerance and watch trajectory re-compute instantly.
Let's Go to Mars
Robot Arm Balancer
A simple animated 2-joint “robot arm” tries to hold a target point. When user moves the target, the arm smoothly finds a stable configuration. No “machine learning”—just pure real-time control.
Try Robot Arm Balancer
Drone Hover Stabilizer
A small quad-drone icon tries to remain level. You press buttons like Wind Gust, Tilt, and Disturbance, and the drone re-stabilizes instantly. Visual scaling represents relative control energy, not physical motion.
Try Drone Hover
Flipper Market Allocator
Paste a list of 2 items + prices + fees. Zero-Training AI™ outputs a buy/hold/sell allocation under hard constraints: budget, max-per-item exposure, minimum liquidity, and fee-adjusted profit targets.
Try Flipper Allocator
LLM Hallucination Eliminator
User types a prompt. Model outputs 5 candidate sentences. Zero-Training AI™ filters them and highlights the “safe, consistent, non-hallucinated” one.
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Zero-Training AI™ is a versatile foundation for any domain where constraints, objectives, and real-time response are critical. Below is a detailed overview of current applications my company is devloping and some of which are currently available.
Disaster Relief & Humanitarian Logistics (ReliefScope®)
We just released ReliefScope® that optimizes truck, personnel and supply allocation from known inventories and assessor reports, drone imagery, road status, fuel limits, priority levels, accessibility in seconds.
Home & Community Energy Management
We are in the testing phase of an application that balances solar generation, battery storage, and household demand under physics-based constraints (weather forecasts, pricing, blackout prevention). Delivers cost savings and grid stability with zero historical training — perfect for off-grid or low-income homes.
Assistive Robotics & Prosthetics Control
We are in the testing phase of real-time joint/torque resolution for prosthetic limbs, exoskeletons, and mobility aids. Adapts instantly to user weight, terrain, or fatigue without retraining — emphasizing safety and smooth motion.
Special Companion Robots & Emotional Support
We are developing a "behavior controller" that governs context-aware, affectionate behavior
(touch timing, tone, gaze, foreplay, etc.) in humanoid robots.
Enforces strict consent, safety, and consistency constraints — reliable and explainable.
Personalized Education & AI Literacy Tools
Privacy-preserving adaptive curricula that sequence lessons from known prerequisites and attention limits — no data collection. Ideal for homeschooling, teaching real AI decision skills to kids in underserved communities.
Hospital & Public Health Resource Allocation
Optimizes beds, staffing, triage, and drug distribution under ethical, capacity, and urgency constraints. Provides auditable, real-time decisions for overwhelmed facilities or public health crises.
Environmental Conservation & Wildlife Protection
Plans optimal drone patrol routes and resource deployment for anti-poaching and ecosystem monitoring using known animal behavior and terrain constraints.
Autonomous Vehicle Governance & Safety Layers
Serves as the deterministic "brakes and steering" overlay — enforcing safety, policy, and comfort constraints on top of perception models in real time.
Smart Grid & Renewable Energy Balancing (GridZero)
Dynamically balances solar/wind input, battery charge/discharge, and demand under strict physics constraints (voltage stability, line limits, no blackouts) using only real-time measurements and known grid equations — no historical load profiles or learned forecasting models required. Ideal for microgrids, off-grid communities, and utility-scale renewable integration where explainability and guaranteed stability matter most.
Large-Scale Field Service & Preventive Maintenance Routing
Schedules and routes hundreds of technicians across cities/regions over weeks/months while respecting technician skills, travel time, vehicle capacity, priority SLAs, and hard time windows — all deterministically resolved from current constraints without training on past tickets. Used by utilities, telecoms, and facility management firms to minimize downtime and travel costs at massive scale.
Critical Infrastructure Safety Governor (FailSafe Layer)
Acts as a deterministic overlay on any autonomous or semi-autonomous system (drones, vehicles, industrial robots, power plants) — instantly enforces hard safety invariants, ethical boundaries, and regulatory constraints in real time even when perception/ML layers produce uncertain outputs. Guarantees "never violate X" properties mathematically.
Crisis Hospital Resource & Triage Resolver
In mass-casualty or pandemic surge scenarios, instantly re-allocates ventilators, ICU beds, staff, and medications across patients/units under ethical priority rules, capacity hard-limits, and urgency scores — fully auditable and deterministic so decisions can be reviewed and trusted by medical boards.
Flight Controller for Drones
Zero-Training AI™ demonstrates how deterministic decision systems can operate without training data, datasets, or machine learning models. The systems shown here evaluate candidate actions in real time within a defined Decision Space, producing stable and explainable outcomes across multiple domains.
These applications are not exhaustive — the framework is domain-agnostic and can be adapted rapidly to new problems where structure is known and determinism is essential. Ongoing development focuses on open-source expansion, more live demos, and community contributions.
Conclusion
Zero-Training AI™ proves that intelligence does not require massive datasets, retraining cycles, or opaque inference. When constraints and objectives are explicit, optimal decisions can be resolved mathematically — reliably, explainably, and in real time.
The future of this work is collaborative and open — let's build deterministic intelligence that benefits everyone.