Operational Autonomy at Scale

Recovering gold and rare earth elements from e-waste traditionally requires toxic chemicals like cyanide or aqua regia.

A tunable sorbent material selectively captures target metals from waste streams without hazardous reagents.

Amyloid-carbon composite materials, selective binding chemistry.

U.S. provisional patent filed, 2025.

Traditional extraction and refining methods are toxic, energy-intensive, and difficult to operate at a modular scale.

A closed-loop aqueous iodide process regenerates its own reagents in-line, recovering metals via electrowinning.

Electro-swing capture, divided-cell electrowinning, in-line iodine regeneration.

U.S. provisional patent filed, 2025.

Fixed process parameters can't adapt to variable e-waste feedstock composition.

AI models monitor and adjust process conditions in real time to maximize recovery yield.

Process-monitoring sensors, ML-based parameter optimization.

In active development.

Traditional refining requires large-scale industrial smelters, limiting where and how quickly capacity can be added.

A modular system footprint allows deployment closer to e-waste sources, supporting domestic material sovereignty goals.

Modular facility design, regulatory alignment with domestic materials policy.

Early-stage concept.

Cannabis search tools rely on rigid keyword matching that can't distinguish a medical query from a casual one.

A human-intent classification layer routes each query through the right retrieval path before an LLM reasons over the result.

Proprietary intent models, high-dimensional vector search, RAG pipeline over live business and research data.

Live in production across the WeedFinder platform.

Dispensary staff spend significant time answering the same product and strain questions.

A conversational agent integrates directly into dispensary POS systems in "Store Mode," handling discovery end-to-end.

LLM-backed conversational layer, POS integration hooks, recommendation engine.

In active development, targeting a 2025 launch.

Dispensary hours, stock, and pricing change constantly; static listings go stale within hours.

Direct sync pipelines keep listings, menus, and pricing current across every connected business.

Node.js/Express services, real-time sync jobs, PostgreSQL-backed listing infrastructure.

Live in production across the WeedFinder platform.

Dispensaries and brands lack access to real-time, ground-truth market intelligence.

Aggregate and anonymize platform-wide search and interaction data into geographic demand heat maps and trend forecasting.

Data pipeline over platform interaction data, geospatial analytics, predictive modeling.

Roadmapped.

Cultivation teams lack a unified, real-time view of what's actually happening across a facility.

Drone imagery and sensor data are fused into a single facility model that tracks plant health trajectories and flags anomalies as they emerge.

Sensor fusion pipeline, facility-mapping models, time-series plant health tracking.

Early-stage concept.

Manual scouting can't catch stress signals early enough to prevent crop loss.

Multi-sensor drone passes capture layered imagery across a facility, feeding directly into the diagnostics engine.

Multispectral/thermal/NDVI imaging, autonomous flight paths, sensor auto-integration.

Early-stage concept.

By the time a cultivation problem is visible, meaningful yield has often already been lost.

Continuous learning models trained on environmental patterns and outcomes flag early-stage stress signals and recommend interventions.

Proprietary diagnostics engine, continuous learning feedback loops, anomaly detection models.

Early-stage concept.

Cannabis cultivators need advanced growing intelligence, and building it from scratch inside a discovery-and-commerce platform isn't the fastest path to it.

AI Master Grower licenses defined capabilities to WeedFinder for its cultivation-facing services while the two technologies remain independent — WeedFinder gains cultivation intelligence and recurring revenue, AI Master Grower gains a commercial deployment environment and cannabis-industry distribution. Cannabis is the proving ground; agriculture is the market.

Licensing and integration layer, independently operated core technology.

Early-stage concept.

Revenue managers can't manually track competitor pricing and demand signals across dozens of channels in real time.

A dedicated agent ingests market and booking data continuously and executes pricing adjustments within defined guardrails.

Market data ingestion, forecasting models, automated distribution-channel pricing hooks.

In active development, backed by strategic hospitality partners.

Guest data is scattered across PMS, CRM, and POS systems with no unified view of the relationship.

A guest-intelligence agent unifies signals into one evolving profile that other agents can act on.

Entity resolution across property systems, preference modeling, lifetime-value scoring.

In active development.

Manual task coordination doesn't scale past a handful of properties.

An operations agent routes and prioritizes tasks in real time based on occupancy, staffing, and turnaround targets.

Task-routing engine, staffing/occupancy data integration, performance analytics.

In active development.

Traditional dashboards describe the present; they can't test a decision before it's made.

A continuously updated digital twin of each property enables simulating outcomes before committing to an action.

Real-time state modeling, simulation engine, property data integration.

In active development.

Young athletes have limited access to professional-level coaching and personalized feedback.

A conversational agent delivers real-time coaching, motivation, and skill-building guidance in the voice of a chosen athlete persona.

LLM-backed conversational layer, mobile-first delivery, computer vision integration planned.

Proof-of-concept chatbot completed; broader platform in active development.

Athletes can't objectively evaluate their own technique without expensive in-person coaching.

Computer vision models analyze movement and form against professional benchmarks to generate actionable feedback.

Computer vision pipeline, motion-analysis models.

Early-stage concept.

Traditional training environments can't simulate game-pressure scenarios on demand.

Immersive VR/AR environments let athletes practice technique and decision-making in simulated game conditions.

VR/AR experience layer, currently in development.

Early-stage concept.

Training, nutrition, and progress-tracking tools are scattered across disconnected apps.

A unified mobile platform consolidates training plans, nutrition guidance, and goal tracking.

Mobile app platform, progress-tracking and goal-setting tools.

Early-stage concept.