The marketing is built from your files
Giant Context builds a business's marketing site with a model. The whole point is where the content comes from.
A model handed nothing but a request invents. It writes a warm testimonial no customer gave, a user count from nowhere, a feature the product does not have. That is an ordinary language model doing what it does, and the result is a liability rather than marketing, because a site full of claims that were never true is worse than no site.
The value is in what the model is not free to make up. The customer brings their own material, their documents and specs and pricing and the record of the business. The site is built from that, and every claim on the page traces to something the customer actually provided.
You bring the files. It builds the marketing.
Why a model invents
A model told to build a homepage with nothing behind it has a testimonial section to fill and nothing to fill it with. It fills the section with something plausible. The model is not lying but completing a pattern. A page with an empty testimonial section is a pattern with an obvious completion. Left alone it produces a site that looks finished and is full of claims no one ever made. The failure is quiet, because an invented fact looks exactly like a real one.
The files are the source
The fix is to give the model something real to build from. That material is the customer's own files. A business has a corpus of them, product docs, pricing sheets, case studies, the team page, the specs, the record of what it does and who it is. Once the customer uploads that corpus, it becomes the ground the site is built on. Before the model plans a section, the grounding pipeline reads the files and does two things with them. Its own description states both:
The grounding pipeline, in its own words
Grounding. Retrieval conditioning + verification against knowledge_chunks.
1. Retrieval conditioning. Fetch the chunks most relevant to a query and format them as a PROJECT FACTS block ... so generation anchors on real project data instead of inventing values.2. Verification. For each extracted inventory item, search for a fact-labeled chunk above a cosine similarity threshold, and drop items that don't anchor. Stops hallucinated testimonials / stats / team-members at the source.The first job conditions the writing. It pulls the file content most relevant to the section at hand and puts it in front of the model as real project data, so that the model writes from the customer's material instead of from nowhere. The second job verifies. Every concrete fact the model wants to place, a testimonial, a statistic, a named team member, has to anchor to file content above a similarity threshold. Anything that does not is dropped. A claim reaches the page only if it came from the customer's files.
A law firm uploads three things, a page of practice areas, two closed-case write-ups, and a rate sheet. The site that comes back lists those practice areas, quotes the two cases as proof of work, and shows the rates from the sheet. It invents no fourth practice area to balance the grid and manufactures no client quote to fill the testimonial row, because neither is anywhere in the files. The page is shorter than a fabricated one. Every line of it is true.
Fact and context
Not everything in a customer's files is a fact to state. A business uploads its own pricing. It also uploads a comparison against a competitor. The first is theirs to claim. The second is background, useful for reading the market but not a price the business charges.
So every file is tagged when it is uploaded, fact or context. Because the verification pass will not let a context chunk anchor a claim, the competitor's prices never surface as the business's own. The tag is set by the person who knows, at the moment they hand over the file. The model never guesses which prices are the customer's.
What it holds
The customer's own facts, pricing, cases, team
Background, like a competitor comparison
Can anchor a claim
Yes, its content can become a stated fact
No, never a stated fact
Its job on the page
Provides the claims
Shapes the emphasis and framing
Why this is the product
This is the whole product. A model that writes a marketing site from a prompt is a commodity. A system that writes the site from the business's own files produces marketing the business can stand behind, because every line of it came from something the business provided. That is the difference between a toy and a tool a company pays for. Anyone can call a model. What is hard, and worth paying for, is turning a company's corpus into its public face without inventing a word.
Ground first, then generate
When a model does work that has to be true, a clever prompt is not enough. It needs the source, which is the customer's own files. Build the grounding first. Verify every fact against the corpus the customer provided and drop whatever does not trace back. Then the model does the part it is good at, arranging the customer's material into a page. The business gets marketing that is theirs down to the last claim.
Building something like this
I'm Jesse. I build platforms end to end, and I'm open to work. If this is the kind of engineering you need, get in touch.