Much of the real work in AI quietly depends on product context.
It is obvious when you are building a recommendation engine or a pricing model, but the dependency shows up in less expected places, too. Market research tools need to distinguish categories from keywords. Sales assistants need to know which products are comparable substitutes and which are completely different segments.
In these cases, the AI isn’t just processing data; it is processing meaning. And that meaning usually lives in deep, industry-specific product knowledge.
When a team reaches this point, they typically follow one of two paths.
Option A: Feed the product data into the AI
The default approach is to bake product data directly into the AI, importing catalogues into vector databases or training sets. This works until the market moves. As new products launch and categories shift, the AI’s internal snapshot drifts further from reality. Worse, if multiple teams do this, each builds a slightly different version of the truth, leaving you with fragmented systems that disagree on basic facts.
Option B: Let the AI access context when it needs it
The scalable alternative is to leave the data where it belongs. Using a shared context layer (via standards like MCP), the AI retrieves authoritative product definitions only when needed. Platforms like Eidos maintain the "truth"—relationships, substitutes, hierarchy—while the AI handles the reasoning. When the market changes, the context updates once, and every tool sees it instantly—no retraining or data cleaning required.
Why this matters
Let's take market research. It's a perfect example because it relies entirely on shared understanding.
If an AI is analysing market trends, it must understand competition, not just keywords. It needs to know that two products are variants of the same SKU, while a third is a competitor from a different segment. It needs to recognize when growth in one category is actually cannibalization from another.
With the "import-everything" approach, every tool builds its own isolated, likely flawed mental model of the market. With a shared context layer, the AI reasons over a landscape that actually reflects how the industry operates.
The difference shows up in trust. The outputs are grounded. Explanations make sense. Results align with how practitioners actually talk about their market, rather than how a database happens to be labeled.
A scalable separation of concerns
This isn’t a magic trick; it is a shift in responsibility. Instead of demanding that every AI initiative become an expert in data management, you separate the concerns. One layer maintains the meaning; the other reasons over it.
MCP provides the bridge, and platforms like Eidos provide the source of truth. AI systems—whether for research, analysis, or sales—no longer need to own the complexity of the industry. They just need access to it.
Over time, this is the only scalable option. Industry knowledge doesn’t belong frozen inside a model. It belongs in a shared context that models can consult.