Pick any month in 2026, and you’ll likely see new “AI search” announcements hitting the legal tech market. Natural language queries, ask-style interfaces, conversational search …  it’s suddenly everywhere.

But here’s what legal professionals need to understand: Asking questions in natural language is not AI search. It’s just an interface. A helpful interface, to be sure, but a better way of asking for what you want is not the same as a transformationally better way of giving you the intelligence you need to make smarter decisions or execute the task at hand.

AI search isn’t about how you ask the question. It’s about what happens after the question is asked. It’s how content is structured, indexed, filtered, and understood across the system. The question box is just where you start. The intelligence is what powers everything underneath.

AI search is dependent on what happens before the question is asked. It’s the process of transforming documents and metadata into a semantic index – a map of related terms, concepts, context and meaning that is distinct from a traditional lexical (i.e., keyword-based) index and enables AI search to surface relevant content that traditional search simply cannot uncover easily, if at all.

The Interface vs. The Intelligence

Natural language querying is a capability. A useful one. But layering it on top of a legacy search engine doesn’t transform that engine into intelligent search, nor does it magically correct shortcomings in traditional search driven by things like inaccurate, incomplete and/or inconsistent metadata.

True AI search depends on:

• How well the system understands content before you ask a question
• Whether it can leverage rich metadata, not just text matching
• If it filters by context like jurisdiction, document type, parties, and governing law
• Whether it synthesizes information across sources based on meaning or just retrieves matching keywords

This distinction matters because as “AI search” becomes a checkbox feature across the legal tech landscape, the real differentiator isn’t the question interface. It’s the intelligence infrastructure behind it.

And that’s where architectural maturity, deep expertise in search technologies, and strategic sophistication around AI wins.

Smart Answers: Built on Real Intelligence Infrastructure

Last year, we introduced Smart Answers to select customers as part of our Legal AI Assistant on the ndMAX platform. This wasn’t a reaction to market trends. It was the natural evolution of a platform already built for intelligent document management and search.

Smart Answers allows legal professionals to ask questions in natural language and receive clear, cited answers drawn directly from documents in their cabinets. But what makes it different isn’t the natural language interface. It’s what powers it:

Unlimited and conditional metadata

Our platform doesn’t have rigid metadata structures. Unlimited metadata fields can be configured conditionally, enabling dramatically more precise filtering and retrieval. This means Smart Answers can understand context that text-matching systems simply miss.

Advanced filtering capabilities

Intelligent search isn’t just about what words appear in a document. It’s about understanding jurisdiction, document type, parties, governing law, closing dates, and the relationships between them. Our metadata architecture enables this level of sophistication.

Comprehensive indexing

Full-text indexing combined with rich metadata analysis ensures nothing relevant gets missed. The system understands structure and content together, not just keywords.

Smart Answers searches across all accessible documents, analyzes both full text and metadata, synthesizes information across multiple sources, and delivers answers with document citations.

True semantic search capabilities

To be clear, Smart Answers today is not semantic search – and neither is any other application of an AI search agent that leverages a traditional lexical search engine. It enables our Legal AI Assistant to execute iterative lexical (full text and metadata) search on a natural language query to retrieve intelligence from documents across a firm or legal department’s repository, respecting access controls, data loss prevention policies, and other security measures. This initial release brings tangible benefits:

• Find the right precedent in minutes, not hours
• Synthesize information across multiple documents for drafting and analysis
• Get evidence-based insights backed by source documents
• Reduce risk by surfacing relevant documents you didn’t know existed

While this first version definitely helps busy legal professionals today, where Smart Answers becomes transformative – and where other options that just slap a search agent on top of limited, legacy search capabilities simply can’t compete – is with the pending addition of true semantic search capabilities to NetDocuments. This means full semantic indexing (aka “vectorization”) of firm and department document repositories, enabling NetDocuments AI search to retrieve intelligence based on meaning and context beyond the words used or metadata applied. This context understanding can also be applied to other search tools via NetDocuments ndConnect program. This is AI search, not just AI-assisted querying.

What to Ask When Evaluating “AI Search”

As AI search capabilities become more visible across the market, understanding what actually powers these tools helps legal teams make informed decisions about their technology investments.

When a vendor announces “AI search,” ask:

• What metadata capabilities support the search? Is it unlimited or fixed fields? Can metadata be conditional based on document type or context? Does metadata accuracy and completeness rely on users, or is it automated consistent with your organization’s taxonomy?
• Can it filter by complex legal criteria? Jurisdiction, document type, parties, governing law, date ranges, workspace-specific requirements?
• Is there a semantic index of all content in our organization’s repository? Or is this just a natural language interface on top of keyword search?
• What intelligence exists before I ask a question? How is content structured, classified, and indexed in the system?
• Do I have the ability to exclude content from semantic search or other AI capabilities? For example, do you have a simple data loss prevention (DLP) rule I can apply if a client dictates that content related to a matter cannot be processed through AI?
• Is it built on my single source of truth? Or does it create or leverage a shadow system of record?

These questions reveal whether you’re getting an interface enhancement or actual intelligent search infrastructure.

When to Use Smart Answers

Smart Answers works best when you’re specific about what you need. Instead of “find all merger agreements,” ask for “the key terms of three merger agreements governed by Texas law where one party is a bank.” Instead of “documents mentioning Judge Smith,” ask to “summarize five orders issued by Judge Smith and identify any consistent ruling patterns.”

Traditional search still has its place for exact matches, complex Boolean queries, or when you already know which documents you need. The key is knowing which tool fits the task.