Founder
Cory Tortorici leads architecture, implementation, experimentation, and research direction.
LinkedInAbout Global Intent Company
Independent AI research and systems engineering.
The people, objectives, and operating principles behind GIC’s research and private-AI infrastructure development.
Company
Founder-led AI research translated into working systems.
Global Intent Company is a founder-led, pre-incorporation AI research and development startup. Current work centers on privately deployable AI systems, custom language models, the INTENT model family, private inference infrastructure, and Virtual Lab for AI-assisted scientific and computational experimentation.
AI is core to the company's products and research: Global Intent Company develops and trains custom model architectures, operates inference infrastructure, and builds the software that connects models to private and scientific workflows rather than relying exclusively on third-party AI APIs.
Company status
Pre-incorporation · Bootstrapped and founder-funded · $0 external funding raised · No institutional or lead investor.
Funding
Bootstrapped and founder-funded. Total external capital raised to date: $0. There is currently no lead VC or institutional investor.
Business status & location
Global Intent Company is currently pre-incorporation, so there is no incorporation date yet. Business correspondence address: 178 Jackson Circle, Cleveland, Georgia 30528, United States.
Cleveland, Georgia
Inspect the source organization
Public repositories provide source-level provenance for selected research, systems, models, and software.
Technical vision
A vertically integrated AI research stack.
Global Intent Company is being developed around a single thesis: useful AI should be more than a remote model endpoint. Models, private infrastructure, scientific instruments, local learning, multimodal perception and verifiable artifacts can operate as one inspectable system.
The programs below include shipped software, active experiments and longer-horizon research directions. They are presented as a roadmap, not as claims that every component is production-complete.
PLM · Private Model Layer
PLMN, PLMH and PLM Connect form the private-model infrastructure direction: operator-controlled model nodes, hubs, secure connectivity, local memory, signed device identity and auditable inference.
INTENT · Model Family
A model-development program spanning compact INTENT models through INTENT-R recurrent language modeling, with Multi-INTENT extending the architecture toward multimodal perception, routing and specialized model cooperation.
Virtual Lab
A scientific-computing environment designed to connect AI, experiments, sensor acquisition and reproducible evidence. Virtual Lab treats experiments as inspectable systems rather than isolated model demos.
Virtual Lab Instruments
Software-defined instruments and the Instrument Console bring cameras, RF, environmental sensing and future laboratory hardware into synchronized acquisition, measurement, replay and analysis workflows.
Edge Training AI
Research into models that can learn, adapt and run closer to the user: browser-native training, WebGPU execution, mobile/edge inference and bounded local retraining instead of requiring every learning loop to live in a remote AI service.
Autonomous Microbiology
A forward research direction for combining Virtual Lab instruments, machine vision, experiment control and specialized AI into closed-loop microbiology workflows where observations can drive bounded autonomous measurement and experimentation.
Artifact AI · 3D INTENT
A spatial and artifact-centered direction for AI that reasons over persistent digital objects, 3D environments and inspectable generated artifacts—not only text conversations. This includes spatial interfaces, 3D representations and executable research artifacts.
BitVision
Tokenizer-free and alternative-representation research exploring bits, visual/bitmap-like representations and specialized scientific variants such as BitVision-MS, with the broader goal of testing representations beyond conventional text token pipelines.
How the pieces converge
PLM provides the private execution substrate. INTENT provides model research. Multi-INTENT expands perception across modalities. Virtual Lab and its instruments connect models to measurable scientific environments. Edge training moves adaptation toward devices and browsers. Artifact AI and 3D INTENT make outputs persistent, spatial and inspectable. BitVision tests alternative representations. Autonomous microbiology is a domain where these layers can ultimately converge into bounded, evidence-producing scientific agents.
Engineering principles
Technical ambition backed by explicit evidence.
The standard is not whether a claim sounds advanced. It is whether the implementation, experiment, or documentation makes the claim inspectable.
Private by architecture
Design deployment so operators can keep inference, model assets, and application context inside infrastructure they control.
Efficiency over brute force
Investigate capability per parameter, memory footprint, runtime overhead, and practical deployment constraints.
Verify before execution
Treat reachability, protocol compatibility, authentication, model readiness, and stream readiness as distinct states.
Inspectable systems
Prefer architectures and tooling that expose meaningful runtime state instead of hiding every layer behind a remote API.
Evidence before claims
Separate implemented behavior, measured results, design objectives, and open hypotheses so readers can tell what has actually been demonstrated.