For decades, digital products have been shaped around lists, tables, and schemas. CRMs revolve around contacts and companies. ERPs are built on rigid entities like orders and invoices. As designers and developers, we’ve grown used to this way of structuring reality.

But the rise of artificial intelligence and vector embeddings challenges this foundation. Instead of organizing information into fixed categories, we can represent it as context in a multi-dimensional space. Similarities, relationships, and intent emerge not from predefined rows and columns but from how data relates to other data.

This shift forces us to ask: are classic data structures reaching the end of their dominance?

From Schema-First to Context-First

Traditional databases thrive on order: a column for every attribute, a row for every record. Vector embeddings flip this on its head. They capture meaning, not just structure.

With embeddings, “similar” contacts, leads, or companies aren’t defined by a rigid filter like industry = SaaS or size > 50. They’re found by proximity in vector space—a mathematical representation of context.

Instead of sorting by fields, we start querying intent:

• “Find companies that feel like early-stage fintechs in Scandinavia.”

• “Show me customers similar to our best three champions, but not yet using product X.”

No schema required. Context becomes the organizing principle.

How UX Changes Without Rows and Columns

For designers and frontend developers, this shift is seismic. Without rows, filters, and lists, we need new mental models—and new interface patterns.

Some early lessons:

1. Intent-first input → Replace complex filters with natural queries.

2. Explainability by default → Show users why results are returned (similarity scores, signals).

3. Progressive structure → Start with context clusters, but allow saving as lists or cohorts.

4. Feedback loops → Simple controls like “more like this” or “less like this” to refine results.

5. Safety rails → Apply hard business rules (permissions, compliance) alongside fuzzy similarity.

UX here isn’t about recreating tables with AI inside—it’s about rethinking discovery itself.

Architecture: Hybrid by Necessity

Does this mean relational databases are dead? Not at all. In practice, we’re entering a hybrid era:

• Relational DBs for truth and constraints (IDs, transactions, billing).

• Vector stores for meaning and similarity (context, cohorts, semantic retrieval).

• Orchestration layers to combine both worlds, often with RAG (retrieval-augmented generation).

• Event streams to keep truth and meaning synchronized.

The lesson: keep invariants in tables, push discovery to context models.

A Glimpse Into Context-First Apps

At Tetrifox, we’ve begun experimenting with applications that skip the classic “lists of contacts, leads, or companies.” Instead:

• Entities are represented as embeddings.

• Interfaces show clusters, cohorts, and narratives, not tables.

• Queries are sentences, not field-based filters.

• Outputs are ranked explanations—with signals and confidence, not just data dumps.

It feels strange at first. But it also feels inevitable.

The Risks (And How to Design Around Them)

Going context-first comes with challenges:

• Opacity → Solve with transparency: show confidence, explain signals.

• Model drift → Version embeddings and let users know when models change.

• Bias → Audit training data, and show contrasting results for balance.

• Over-automation → Leave escape hatches: CSV export, hard filters, old-school views.

Trust is the currency here. Users need to feel they’re in control.

FAQ (For the Schema Skeptics)

Are relational databases obsolete?

No. They remain the backbone of transactions, billing, and invariants.

What’s the point of vector embeddings?

They unlock similarity search, natural clustering, and context-based retrieval—things rigid schemas can’t do well.

Will classic data structures disappear completely?

Unlikely. But their role will shrink as context-first design proves more natural for discovery and planning.

The Big Question

Every big shift in software forces us to rethink what’s “normal.” Spreadsheets once replaced ledgers. Relational databases replaced flat files. Now, context models may challenge lists and tables.

The open question is: how long will classic data structures hold?

Will they remain the backbone for decades—or will context-first AI make them feel like legacy scaffolding?

As a UX’er and frontend dev, I believe we’re at the beginning of that transition. And once users get used to asking for meaning instead of navigating rows, there may be no going back.