About
I came out of Cal Rhetoric with the permanent suspicion that whoever controls the frame controls the fight.
That is the through-line.
Politics, law, media, sport, brands, software, interfaces, propaganda, protocols, AI agents — none of these are separate worlds to me. They are different arenas where human beings use symbolic systems to make reality actionable. A speech can move a country. A rule change can alter a game. A file format can discipline a workflow. A brand can organize trust. A protocol can decide whether public life belongs to platforms or to people.
My work begins there: with the belief that language is the primary human technology, and that most of the systems we call "technology" are later expressions of the same basic human act — making meaning, stabilizing it, transmitting it, and using it to coordinate action.
That is why this site exists. Not as a portfolio in the normal sense, but as a map of the systems I am building from that premise.
The Rhetoric Problem
I studied rhetoric because I wanted to understand how words become force.
That question became harder to treat as academic after 9/11. The early 2000s made the machinery visible: media frames, patriotic scripts, institutional language, state power, fear, repetition, moral simplification, consent manufacture, and the speed at which a country can be moved by a story it barely understands.
At first I thought the path might be law — copyright, digital media, technology policy. But the closer I got to the legal frame, the more I realized I was not especially interested in becoming fluent inside the structure as it existed. I wanted to understand why the structure had failed to anticipate the world that digital media was creating.
The real problem was upstream.
Before law, there is language. Before policy, there is framing. Before institutions act, they describe the world to themselves in a way that makes certain actions feel necessary, obvious, or impossible.
That is where my attention stayed.
Industrial Production
After rhetoric came media production.
I studied electronic media and worked in industrial filmmaking: training videos, executive interviews, product demos, internal communications, sales materials, corporate documentary work. The kind of work that rarely gets treated as art but quietly teaches you how organizations explain themselves.
That period mattered because it stripped away any romantic idea that meaning-making lives only in books, speeches, films, or campaigns.
Meaning is industrial.
It is produced in decks, websites, scripts, onboarding flows, product pages, internal videos, metadata fields, sales language, CRM stages, support documentation, and all the other symbolic machinery organizations use to make action possible.
A company is not only a legal entity or a group of employees. It is a system of signs trying to remain coherent under pressure.
That is what I eventually came to call the Symbolscape.
From Symbolscape to Industrial Semiotics
I ran the practice as Symbolscape starting in 2015.
The name was true, but it was too hidden. It sounded like a mood. What I was actually doing was more concrete: helping organizations build and repair the symbolic systems they depend on — websites, brands, content, automation, metadata, AI workflows, and the public-facing language that tells people what a thing is and why it matters.
So I rebranded the practice as Industrial Semiotics.
That is the phrase that finally says the quiet part directly: semiotics is not just an academic lens. It is an industrial discipline. Organizations are constantly producing meaning whether they know it or not. The question is whether that meaning is coherent, useful, machine-readable, persuasive, and aligned with what they are actually trying to do.
Industrial Semiotics is the client-facing practice. It is where the theory touches business systems.
travis.tech is where the theory becomes product infrastructure.
The Open Web Turn
The same symbolic problem now exists at web scale.
The public internet is full of meaning, but the meaning is badly organized. Articles circulate without context. Social posts detach discourse from source material. Platforms trap conversation inside feeds. Search engines flatten source types. AI systems ingest the web while barely knowing what kind of thing they are reading.
That is the problem behind Skysquare and Infoscape.
Skysquare asks: what if the conversation came back to the source? What if, when you read an article, you could see the Bluesky discourse around it — who shared it, what they quoted, which passages mattered, and how the conversation connects to other people and sources?
Infoscape asks the next question: what kind of source is this? A newspaper, a university, a government agency, a think tank, a propaganda outlet, a spam farm, a trade publication, a local blog? What domain of knowledge does it belong to? What topic area does it participate in? How should a reader navigate authority and relevance instead of drowning in undifferentiated links?
That is why I care about protocols over platforms.
Platforms capture attention. Protocols can carry meaning across surfaces.
Agentic Development
AI agents changed the build process for me because they made the symbolic layer executable in a new way.
I am not a traditional software founder who came up through computer science. I came through rhetoric, media, marketing systems, and production. For years, that meant I could see systems clearly before I could always build them at the speed I wanted.
Agentic development changed that.
But I do not think the real breakthrough is "AI writes code." That is the shallow story.
The deeper story is that AI makes language operational inside the production process itself. A good instruction becomes a build artifact. A good architecture brief becomes leverage. A good repo map lets an agent act with continuity. A good review prompt can catch structural failures before they become permanent.
That is why I built ContextQB.
ContextQB is not about prompting as a trick. It is about teaching people to think architecturally in an age when language can directly move software. If language is now part of the build system, then context, documentation, naming, boundaries, and review discipline become infrastructure.
AI can write code. Someone still has to think.
What This Site Is For
travis.tech is the home for the product side of the work.
It is where I collect the systems I am building around open-web discourse, source classification, agentic development, owned marketing infrastructure, and semiotic media workflows.
The projects are different, but the theory is the same:
- Skysquare brings public conversation back to source material.
- Infoscape makes source type, topic area, and authority navigable.
- ContextQB teaches people how to command AI agents with architectural discipline.
- Channelscape helps teams move marketing systems toward infrastructure they own.
- Industrial Semiotics Studio applies semiotic discipline to media assets, metadata, and generative workflows.
Industrial Semiotics remains the practice for client work.
travis.tech is the field station: products, writing, open-web infrastructure, investor conversations, agentic-development experiments, and the ongoing attempt to build technology for better meaning-making.