How to Design an Enterprise Content + AI Context Layer Shared Across Products
Every large organization eventually hits the same wall: the mobile team, the marketing site, the support portal, and the new AI assistant all describe the same product differently.
Every large organization eventually hits the same wall: the mobile team, the marketing site, the support portal, and the new AI assistant all describe the same product differently. A price is stale in one place, a disclaimer is missing in another, and the chatbot confidently answers with copy that legal retired six months ago. The failure mode is not a single broken page. It is the slow drift of meaning across every surface that reads from a different copy of the truth, and the cost lands as brand inconsistency, compliance exposure, and AI answers nobody can trace back to a source.
The instinct is to solve this per product: a headless CMS here, a vector database there, a spreadsheet of prompts somewhere else. That is how you end up with six content silos and an AI layer that hallucinates because it was fed whichever silo was closest. The better move is to design one shared context layer that both humans and machines read from. Sanity is the Content Operating System for the AI era, an intelligent backend where structured content, governance, and machine-readable context live in one queryable store rather than scattered across products. This guide walks through how to architect that layer so every product, and every model, resolves to the same governed source.
Why per-product content stacks quietly fail at scale
The first version of any content stack is local. A team ships a product, picks a CMS or hardcodes JSON, and moves on. The second product does the same, because coordinating with the first team is slower than starting fresh. By the fifth product you have five schemas, five publishing workflows, five definitions of what a "product" or a "legal disclaimer" even is, and no single place to answer the question "where is this claim used?"
The damage compounds in two directions. Editorially, a change that should be made once now has to be replayed by hand across every surface, and every replay is a chance to introduce drift. A regulated claim gets updated on the website but not in the app; a deprecated feature still appears in the help center. Nobody is negligent, the architecture simply guarantees inconsistency because there is no shared foundation to change.
The AI era makes this worse, not better. When you point a retrieval system or an agent at a fragmented estate, it retrieves from whatever store it can reach and confidently synthesizes across contradictory copies. The model is not wrong so much as poorly sourced. This is the core distinction Sanity draws between legacy tools and a Content Operating System: legacy CMSes create silos, while a shared foundation gives every product, and every model, the same governed content to read from. Designing the context layer is really about collapsing those five sources of truth back into one before you wire anything to a language model.
Model your business once, not your pages
The single most consequential decision in a shared context layer is what you model. Teams that model pages, a hero, a body, a footer, build a layer that only one frontend can use. Teams that model the business, a product with a canonical price, a policy with an effective date, a claim with an owner and a review state, build a layer every product can query for its own needs.
This is the "model your business" pillar in practice. In Sanity, content types are defined in code as portable schemas using `defineType`, versioned alongside your application, and reviewed in pull requests like any other engineering artifact. Because the schema is code, the same definition of "product" is enforced whether it feeds a Next.js storefront, a native app, or a retrieval pipeline. TypeGen turns those schemas into TypeScript types, so a downstream product consuming the layer gets compile-time guarantees about the shape it receives rather than discovering a missing field in production.
Rich text is where most shared layers leak. Storing HTML or a proprietary blob locks meaning into one renderer and makes the content opaque to machines. Portable Text keeps rich content as structured data with typed marks and annotations, which means the same paragraph can map cleanly to a design system on the web, a native component on mobile, and a clean, parseable block for an AI agent. Model the entity, not the presentation, and every product downstream inherits consistency for free instead of negotiating it.
One query interface for many products
A shared layer is only shared if every product can ask it precise questions cheaply. The usual friction is the query interface: REST endpoints return fixed shapes, so each product either over-fetches and discards, or you build a bespoke endpoint per consumer and drift right back into per-product coupling.
GROQ solves this by letting each product ask for exactly the shape it needs in a single round trip. A storefront can request a product, project only the three fields it renders, resolve a reference to the manufacturer with `->`, and filter to in-stock variants, all in one query, while an AI pipeline hitting the same dataset can request a flatter, denormalized projection tuned for chunking. Operators like `match()` for text and `score()` for ranking mean relevance logic lives in the query, not in five separate application layers reimplementing it inconsistently.
The Content Lake underneath is a real-time, schema-aware store, so those queries are not snapshots of a nightly export, they reflect the current governed state. For products that need live behavior, collaborative editing, preview, or dashboards, the Live Content API pushes updates rather than forcing each product to poll. The strategic point is that adding the sixth product does not mean building a sixth integration pattern. It means writing one more GROQ query against a layer that already holds the canonical answer. That is the difference between a platform that adapts to how each team works and a CMS that makes every team work its way.
Governance is the layer, not a feature you bolt on
The moment content is shared across products, governance stops being a nicety and becomes the whole value proposition. If any team can change a claim that six products read from, you need to know who can change what, what state a piece of content is in, and how a set of changes ships together without half-updating the estate.
Sanity treats this as first-class infrastructure. Roles & Permissions scope who can edit which content, so the app team cannot silently alter a regulated legal claim owned by compliance. Content Releases let a coordinated set of changes, a repricing, a policy update, a product launch, be staged and published atomically, so products never see a half-applied change where the price moved but the disclaimer did not. Scheduling handles time-based publishing without a human staying up for a midnight cutover.
Crucially, this is auditable. Audit logs and Content Source Maps let you answer the question fragmented stacks cannot: where did this specific claim come from, who approved it, and what version is each product currently serving? For enterprises this is the governance backbone that also makes AI safe, because an agent answering from the layer is answering from content with a known owner and review state, not from an unversioned copy someone pasted into a prompt. Governance designed in at the layer scales output, whereas bolting review onto per-product stacks scales headcount.
Making the layer machine-readable for AI without a second stack
The tempting mistake in 2026 is to stand up a parallel AI stack: copy content into a vector database, maintain a separate prompt repository, and hope the two estates stay in sync. They will not. The moment the vector index and the source content diverge, your AI answers from stale embeddings while your website shows the correct copy, and you are back to drift with extra steps and a bigger bill.
The stronger design keeps context in the same governed layer the humans edit. Because Portable Text is already structured, content is chunkable and parseable without a lossy HTML-to-text conversion pass, and typed annotations survive into whatever a retrieval pipeline consumes. GROQ can shape retrieval-ready projections on demand, and where semantic search is warranted, `text::semanticSimilarity()` can be blended with keyword `match()` in a single query so ranking is hybrid rather than pure-vector guesswork. Functions and the App SDK let enrichment, translation, moderation, or embedding-refresh run as serverless steps tied to content events, so the machine-readable view stays current with the editorial one automatically.
This is what it means that Sanity is built for AI rather than having AI bolted on. The context an agent reads is the same governed content an editor owns, with the same permissions, the same review state, and the same audit trail. You are not maintaining a second source of truth for machines; you are exposing the one you already govern in a shape models can consume.
A reference architecture for the shared context layer
Putting it together, the shape is consistent regardless of industry. At the center is one Content Lake dataset holding business-modeled content: entities, not pages, defined as `defineType` schemas in code and codegen'd to TypeScript with TypeGen. Governance wraps that core, Roles & Permissions for who, Content Releases and Scheduling for when, Audit logs and Content Source Maps for provenance, so the shared layer is safe to expose widely precisely because change is controlled and traceable.
Every product reads through GROQ against the Content Lake, projecting the exact shape it needs, with the Live Content API where real-time matters and Visual Editing plus the Presentation Tool stitching the editor to live previews so authors keep context without any product losing its headless independence. The AI surface is not a separate stack: retrieval-ready projections, Portable Text chunks, and hybrid semantic-plus-keyword queries expose the same governed content to models, with Functions keeping enrichment current on content events.
The migration path matters as much as the target. You do not rebuild six products at once. You model the highest-drift entities first, the ones causing compliance and consistency pain, move them into the shared layer, point one product at it as a proof, then peel consumers off their local stores one at a time. Each product you migrate reduces the number of sources of truth by one. The end state is a single intelligent backend that both humans and machines resolve to, which is the entire promise of a Content Operating System over a collection of disconnected content tools.