Embeddings and MCP: The Hidden Infrastructure Powering the AI Products You Love
Most AI products people love don’t feel technical. Search feels instant. Recommendations feel obvious. Answers feel relevant.
What users don’t see is the infrastructure making that experience possible. Two of the most important pieces of that invisible layer are embeddings and MCP.
They are not features. They are foundations. And without them, modern AI products would feel slow, shallow, and disconnected.
The Hidden Layer Beneath AI Products
When an AI product feels “smart,” it’s usually because it understands meaning and context, not just words or clicks. That understanding doesn’t come from the interface or even the main model alone. It comes from how information is represented and how context is delivered at the right moment.
That’s where embeddings and MCP come in.
Embeddings: How Products Understand Meaning
Embeddings convert text, images, or other data into numerical vectors that capture meaning and similarity.
This allows products to:
Understand intent instead of keywords
Match related ideas even when words are different
Group, search, and recommend content by meaning
When you type a vague query and still get the right result, that’s embeddings at work. When a product recommends something that feels uncannily relevant, that’s embeddings too.
They power:
Semantic search
Personalization
Recommendations
Retrieval for generative AI
Embeddings are why AI products feel like they “get it.”
MCP: Giving Models the Right Context at the Right Time
Large models are powerful, but they’re not omniscient. They don’t automatically know your latest order, your company policy, or today’s prices.
MCP (Model Context Protocol) solves this by allowing AI models to securely access external systems and tools in a structured way.
Instead of retraining models every time data changes, MCP lets products:
Pull real-time information
Access internal systems safely
Control exactly what the model can see or do
Keep knowledge fresh without rebuilding models
This is what turns static AI into context-aware AI.
Why Products Need Both
Embeddings and MCP solve different but complementary problems.
Embeddings help AI understand what is relevant.
MCP helps AI understand what is current and allowed.
Together, they enable:
Accurate retrieval of the right information
Safe and controlled access to live data
Scalable personalization without model sprawl
This is why modern AI products feel coherent instead of stitched together.
Examples You Use Every Day
When a support chatbot answers using your latest account data, that’s MCP providing context.
When it finds the most relevant policy or help article even if you phrase the question oddly, that’s embeddings.
Products like ChatGPT plugins, Notion AI, enterprise copilots, and modern search tools all rely on this hidden infrastructure layer.
What This Means for Product Managers
You don’t need to implement embeddings or MCP yourself. But you do need to design with them in mind.
PMs should ask:
Where does meaning matter more than keywords?
What context must the AI have to be useful?
What data should the model never see?
How do we keep responses accurate as data changes?
These are product questions, not engineering trivia.
The Future of AI Infrastructure
As AI products scale, the winners won’t be the ones with the biggest models. They’ll be the ones with the cleanest, safest, and most flexible infrastructure.
Embeddings and MCP are the quiet enablers of that future. They let products understand, adapt, and act without becoming fragile or opaque.
Final Thought
The best AI products don’t feel impressive. They feel obvious.
That feeling comes from hidden systems doing the hard work silently. Embeddings give products understanding. MCP gives them context. Together, they form the backbone of AI experiences users trust and keep coming back to.
The more invisible this infrastructure becomes, the better the product feels.