Bill SerGio has taken products from invention to national television, celebrity distribution, and nine-figure sales. That same operator discipline — constraints, objectives, and accountable results — is now encoded in Zero-Training AI™: finished applications, offered free to qualified companies for evaluation and distribution, while the core engine is expanded for high-consequence domains.
Grant reviewers should know the team is not starting from a concept. William SerGio invented products, produced national television programs, put major celebrities in front of live offers, and used early decision software — the predecessor of today’s DecisionEngine™ — to buy media only when the math said the spend could return a profit. His success was featured in Forbes, The Wall Street Journal, and other national media like People Magazine: Read More
These applications are complete enough to evaluate, operate, and — where there is fit — distribute. We are seeking partners and research sponsors to expand the same engine into aviation, defense, medicine, and other high-consequence decision systems.
Zero-Training AI™ for stock selection and portfolio decisions. Uses an equation-based Decision Engine™ — not historical-data training — to determine the best authorized allocation given current market state, investor objectives, and hard constraints.
Business software for accounting, marketing, and net-profit optimization. Automatic transaction categorization, IRS audit-risk flags, predictive ad profitability analysis, and tools for free / per-inquiry advertising.
Generates professional branded reports from live databases (SQL Server, SQLite, Oracle, PostgreSQL, MySQL, MariaDB). No SQL authoring, no training data, no developers required — a connection string is sufficient.
Core of Zero-Training AI™. Explicit Dimensions, Relationships, and Constraints compute the best permitted action in real time. Transparent and auditable for defense, aviation, medicine, and other high-stakes use. Customized per client and can ship as a branded application under the client’s logo.
Media-buying and direct-response software evolved from the original StationBreak system. Analyzes TV and radio stations, predicts response, and calculates maximum profitable bids so spend can be held to a net-profit constraint.
In just 40 kilobytes of code Zero-Training AI™ solves Rubik’s Cube in the MINIMUM NUMBER of moves in a thousanth of a second with NO training data!
In just 40 kilobytes of code Zero-Training AI™ plays chess at the highest level. Evaluates legal moves, looks ahead, anticipates responses with NO training data.
In just 40 kilobytes of code Zero-Training AI™ plays backgammon at the highest level. Evaluates legal moves, dice, risk, position strength and constraints.
In just 40 kilobytes of code Zero-Training AI™ becomes an agent that can handle Emails, Chat, and other tasks by users verbally asking a question or typing a question. It can be integrated into any applications to provide intelligent decision-making capabilities without the need for any training data.
PCB-integrated metamaterial skin for companion robots embeds Zero-Training AI™ in our specialmaterial itself—so the skin is the decision engine, NOT a sheetof sensors feeding a separately trained model. Touch becomes local, recognizable interaction: pressure, motion, and intent isanswered instantly to provide *special* physical comfort without ANY TRAINING.
Modern AI has extraordinary capabilities in prediction, classification, language, and pattern recognition. High-consequence operational decisions often require something different: hard constraints, limited data, and an accountable reason for the action.
Physical limits, safety boundaries, mission rules, resource limitations, and operating requirements may not be negotiable.
Some environments do not provide the enormous historical datasets required by conventional training approaches.
In high-consequence environments, operators need to understand not only what decision was made, but why it was valid.
Zero-Training AI creates decision intelligence directly from structured domain knowledge rather than depending exclusively on massive historical training datasets.
Knowledge from pilots, physicians, engineers, military planners, scientists, operators, and other subject-matter experts.
Explicit relationships, rules, equations, operating requirements, objectives, and decision criteria.
Safety boundaries, physical limitations, mission requirements, available resources, and changing conditions.
A valid action accompanied by the reasoning, variables, constraints, and objectives that produced it.
The objective is not simply to generate an answer. The system must evaluate the relevant conditions, respect critical constraints, pursue defined objectives, and provide an understandable basis for the resulting decision.
Performance depends heavily on available training examples.
The system learns statistical relationships from data.
Outputs may represent a probabilistic or likely result.
Internal reasoning may be difficult to inspect directly.
Domain knowledge can be encoded directly into the decision system.
Rules, equations, objectives, and relationships are represented directly.
Decisions are evaluated against defined requirements and constraints.
The system is designed to identify the factors that produced its decision.
Convert domain expertise into structured, executable decision intelligence.
Incorporate explicit mathematical relationships, rules, contraindications, and operating logic.
Evaluate decisions against physical, operational, safety, and mission constraints.
Evaluate competing objectives and determine actions that satisfy the defined mission.
Respond to changing conditions, sensor inputs, system states, resources, and mission requirements.
Recognize degraded components, conflicting inputs, lost communications, and changing system conditions.
Identify the variables, rules, constraints, objectives, and safety boundaries behind a decision.
Create a decision process that can be examined rather than treated as an unexplained output.
Commercial applications are already operating. Research support is sought to harden and extend the same architecture into environments where changing conditions, explicit constraints, and operational accountability are not optional.
Decision-making for unmanned aircraft, ground vehicles, maritime systems, industrial robots, and other autonomous platforms.
Decision support involving mission objectives, rules, sensor confidence, resources, timing, safety, and operational constraints.
Flight-control and operational decision systems that account for aircraft state, environmental conditions, failures, and mission requirements.
Decision workflows involving triage, treatment prioritization, patient risk, contraindications, and resource allocation.
Resource and operational decisions under changing conditions, limited resources, and competing priorities.
Operational decision support for systems where reliability, safety, resource allocation, and explainability are important.
Zero-Training AI is focused on transforming complex human and mathematical knowledge into executable, explainable decision systems — then proving those systems in domains that cannot tolerate an opaque guess.
How can complex expert knowledge be represented in a form software can evaluate and execute?
How can multiple safety, physical, operational, and mission constraints be evaluated simultaneously?
How can competing objectives and available actions be evaluated to identify the best valid action?
How can every operational decision be accompanied by an understandable record of why it was selected?
Zero-Training AI already has operating applications and a founder who built national-scale companies from scratch. Grant and government collaboration is sought where conventional data-driven methods face limits: scarce data, hard constraints, explainability requirements, and rapidly changing conditions.
We work with government programs, research institutions, subject-matter experts, and operators who need a decision they can defend.
They need decisions that respect the mission, obey critical constraints, respond to changing conditions, and provide a clear basis for why an action was selected.