Legal Tech’s Data Wars: Relational DB vs. LLM-Vector DB

"Adapt or become obsolete." Y Combinator’s latest move to accept only AI startups underscores a fundamental truth: the companies that embrace LLMs aren’t just winning; they are rewriting the rules of the database.

Neither data structure will disappear but the fundamental order of them will. Does your LLM server your structured DB or does your structured DB server your LLM?

I think those that choose the later will be the survivors and be the winners of this next generation of software solutions.

For the past decade, legal tech has been built around structured data. CLMs, matter management tools, and compliance platforms rely on relational databases, with AI used to extract structured fields from unstructured legal documents.

However, LLM-first companies are flipping the script. Instead of forcing legal data into predefined schemas, they generate meaning dynamically, delivering insights as needed and in context. This isn’t just an efficiency play. It represents a fundamental shift in how legal data is structured, stored, and used.

This transformation is putting third-generation legal tech companies—the ones built around structured relational databases—on high alert.

How LLM-First Companies Could Win

LLM-first legal tech has four key advantages over structured-data incumbents:

1?? Zero-Structure Workflows: Rather than requiring users to manually enter metadata or follow predefined intake processes, LLMs enable free-form inputs such as emails, chats, and even voice. They structure the data automatically, eliminating the need for rigid categories and allowing fluid legal work.

2?? Semantic, Not Boolean, Search: Traditional legal tech relies on keyword filtering and database queries. LLMs make search contextual, retrieving insights based on meaning rather than exact matches. Imagine finding “contracts with aggressive termination clauses” without needing to define “aggressive” upfront.

3?? Adaptive Data Models: CLMs and legal ops tools often struggle with taxonomy updates, requiring manual schema modifications to accommodate new clause types, risk categories, or deal terms. LLMs dynamically learn and adjust to new data patterns, eliminating the rigidity of pre-structured taxonomies.

4?? AI as the Interface: Instead of users navigating complex dashboards, LLM-first platforms allow direct, natural language queries:

• “Which vendor contracts auto-renew next quarter?”
• “What risks have we flagged in the past 12 months?”

The LLM constructs the query, making database structure invisible to the user.

How Structured Data Companies Survive

For third-gen companies, relational databases are not disappearing overnight. Compliance, auditability, and structured workflows still matter. However, survival depends on how fast they can adapt. Here’s how they can stay competitive:

1?? Decoupling Workflows from Database Structure: Rather than requiring users to input data into rigid fields, companies should leverage AI to handle structuring dynamically. This allows for free-form inputs while maintaining structured outputs for reporting and compliance.

2?? Hybrid AI Models: Instead of fully replacing structured data with LLMs, companies can blend both approaches. LLMs can interpret and generate insights, while structured data provides verification, validation, and compliance reporting. This creates an LLM-powered insights layer with a structured data backbone.

3?? Automating Schema Evolution: One of the biggest weaknesses of structured data is the need for manual taxonomy updates. Companies that use LLMs to auto-classify new clause types, risk categories, and regulatory changes will have a significant edge over those reliant on hard-coded updates.

4?? Building AI-Native Query Layers: Rather than forcing users to filter and click through structured data, structured data companies should develop natural language interfaces that allow users to interact with data intuitively, based on how they think rather than how the database is structured.

5?? Prioritizing Interoperability: LLM-first companies thrive on data liquidity. Their models improve as they ingest more information. Structured data incumbents should focus on APIs, integrations, and flexible data models to prevent being locked into outdated schema-based limitations.

What the future holds

LLM-first legal tech companies are not playing by the old rules. They are not just extracting insights from structured data; they are redefining how legal data is structured in the first place.

Structured data incumbents can survive, but only if they unlearn their rigid database assumptions and embrace a future where context, rather than structure, dictates how legal work is performed.

The message is clear: Adapt, innovate, and embrace the AI-driven future, or risk becoming irrelevant.

What do you think? Can relational-database-based legal tech evolve quickly enough to compete with LLM-first challengers? Or are we about to witness a full-scale data model disruption?