Is a Brand Context Layer the Same as a Semantic Layer?
No. A semantic layer is one component of a brand context layer, not a synonym for it. A semantic layer is the shared dictionary between data and its users: it defines what every measure, hierarchy, and time period means, the same way everywhere. A brand context layer is the superset. It contains semantics plus 3 things a dictionary cannot hold: data (which items and markets are yours), memory (what the system has learned about your business), and rules (how your business works).
The confusion is understandable, because the data industry is currently building semantic layers under the banner of "context." Snowflake's Horizon Context, announced in June 2026, is described as a governed context layer: it collects metadata across a company's data estate, enriches it with business definitions and lineage, and activates those definitions so that every agent, BI tool, and application computes revenue the same way [1]. Snowflake's opening example is two executives getting two different Q3 revenue numbers from the same data, and its diagnosis is right: scattered, ungoverned definitions make AI answers untrustworthy [1].
What a semantic layer does, and where it stops
Take the two layers into a CPG brand's Monday-morning meeting and the difference stops being abstract.
A semantic layer knows what % ACV distribution is: the share of a market's all-commodity volume flowing through stores that scanned your product, per NielsenIQ's published definitions, along with TDP as the sum of item-level % ACV distribution, and velocity as rate of sale, measured several ways, of which sales per point of % ACV distribution is one [2]. A good one also knows that these are constructed measures with constraints on how they combine. That knowledge is necessary, and every brand using the same provider could share it, because none of it is about your brand.
A brand context layer also knows things no other brand could share:
| Question | Semantic layer | Brand context layer |
|---|---|---|
| What does % ACV distribution mean? | Yes, defined once, used everywhere | Yes, inherited as its semantics component |
| Which of these 40 item-file rows are ours? | No | Yes, that is the data component |
| What does "the club pack" refer to? | No | Yes, the 24-count, learned from the team |
| Should food service count in the total? | No | No, and it remembers you said so |
| When does the fiscal year start? | Sometimes, if configured | Yes, applied to every period comparison |
| Who is the competitive set? | No | Yes, including the competitor's seltzer product the provider files in another subcategory |
Rows 1 and 2 draw the line cleanly. Defining a measure is generic knowledge. Knowing which rows are yours, what your team calls them, what you corrected in week 2, and which calendar you run is knowledge that exists nowhere except inside your business, and it is exactly the knowledge that turns a technically correct query into a commercially correct answer.
The enterprise context layer, sized for a brand team
Horizon Context and its peers are built for enterprises whose problem is drift across thousands of internal definitions and dozens of tools. That is a real problem, and the governance instinct behind it, one meaning, defined once, enforced everywhere, is one this guide shares. The gap is what fills the layer and who it serves. An enterprise context layer governs a company's internal metric definitions. It does not arrive knowing SPINS, Circana, and Nielsen measure construction, it holds no opinion about your item universe or competitive set, and it has no mechanism for accumulating your team's corrections into standing brand knowledge.
A CPG brand team's problem is shaped differently: the definitions that matter most are the providers' (published, strict, and violated by naive tooling), and the context that matters most is operational (items, retailers, calendars, and the language of the trade). In Sous, the semantics sit alongside provider rulebooks that enforce each provider's aggregation math on every query, and both sit inside the larger brand context that the system learns over time. The architecture of that stack, semantic layer plus rulebook plus verification, is drawn out in how an agent works with SPINS, Circana, and Nielsen data.
The one-line answer
If someone asks whether your AI tool has a semantic layer, the answer should be yes, and the follow-up question matters more: what else does it hold about your brand? A semantic layer makes the math mean the same thing everywhere. A brand context layer makes the answer about your business. The full anatomy of what "about your business" requires is in the four components, and what it looks like filled in for a real operator is in the CPG worked example.