The network site, engineered AI-native.
Veza Agency Network needed a site AI systems could actually read, and a content engine that scales across brands, resources, and careers. We built it AI-native on Next.js and Vercel, with Sanity running the content.
The situation
Veza Agency Network runs a network of specialist agencies. The site has to do more than describe them. It has to be read by people, by search engines, and by the AI systems that now sit between a buyer and a shortlist. The old setup could show the work. It could not feed the machines making decisions about it.
That is the gap we build for. Not a chatbot bolted onto a brochure. A site that is AI-native at the layer that counts: the markup, the content model, the way pages render. Read the code and the decision shows.
What we built
We built vezanetwork.com as a VezafyOS project. Next.js on the App Router, server-rendered, so every page ships as HTML an AI can parse on the first request. No client-side hydration gate between a crawler and the content. Deployed on Vercel.
The content runs on Sanity. We modeled 24 document types and 18 modular content blocks, so the network composes pages from a shared kit instead of rebuilding layouts. Brands, capabilities, industries, case studies, resources, job listings, legal pages: each is a structured type, not a slab of copy. Editors assemble. Engineers stay out of the loop on a new landing page.
AI-native, at the markup
AI-native here is not a slogan. It is a set of decisions you can inspect. Every page is server-rendered, so the content lives in the response, not assembled after the fact.
The site publishes an llms.txt and an llms-full.txt: a structured summary and a full reference written for language models, so a system crawling the site gets the network's shape without guessing. The robots policy names the AI crawlers explicitly. GPTBot, Claude, Google-Extended, PerplexityBot and five more are allowed through to the content and blocked from the tooling. That is what "a site AI can read" means. Not a feature. A build standard.
The content engine, and the layers around it
Sanity handles content at scale: the resource library of articles, webinars, podcasts, ebooks, and events; the careers system with its own apply flow; the network directory; the interactive agency scorecard with a scored assessment behind an API. All of it structured, all of it queryable.
The domain knowledge sits in a Mintlify docs layer at docs.vezanetwork.com: the network's methodology, schemas, and processes, published as machine-readable documentation. Content engine on one side, knowledge layer on the other. Both legible to the same AI systems the site is built for.
The design system
The look is one system, held in lockstep across three surfaces: the running site, the internal design-system pages, and a Claude Design project that holds the same tokens. Change a token once, it moves everywhere. No drift between what the design shows and what the site ships.
What the network runs now
VAN runs a site that is theirs. VezafyOS is deployed on it: the stack, the content model, the instructions, the knowledge layer. Adding a brand, a role, a resource, or a landing page is an editor's afternoon, not an engineering ticket.
Weeks, not quarters. AI-native, not bolted on. That is the build.
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