Let's get the numbers out of the way. Perplexity crossed $450M ARR in March 2026, according to FT reporting. Monthly active users hit 30 million by April 2025, up from 2 million in 2023. Retention sits at 85%. Citation accuracy: 97%. The company is targeting $656M for the full year and has a 2028 IPO on record. By any measure, it's a category-defining business.

Now ask a lawyer, a policy analyst, or a due diligence researcher what they think. The response is likely a frustrated exhale.

Because all that growth happened in web search. And web search isn't where professional research actually lives.

The Web Isn't the Research Stack

Consider what a typical due diligence analyst actually works with: a 140-page vendor contract, a 30-page whitepaper from a private company, an internal audit memo, a stack of earnings call transcripts, three NDAs, and a Slack thread linking to a Confluence page behind a SSO wall. None of that is in Perplexity's index. None of it is in Google's index either.

The Grand View Research AI productivity tools market report puts the total addressable market at $185B by 2027. But most of that growth is being captured by tools built for a specific type of knowledge work: the kind that lives on the open web. The legal researcher, the financial analyst, the academic — these people aren't doing public web queries for 8 hours a day. They're reading documents.

And that's a fundamentally different problem.

What "Document Intelligence" Actually Means

Web search works because the web is already indexed. Crawlers can reach it, parse it, rank it. The hard problem was already solved by Google decades ago — the AI just made the query interface better.

Document intelligence is harder. A PDF is not a webpage. It has no metadata, no links, no semantic structure. Tables are images or broken text. Charts are embedded. The reading order may be wrong. Handwritten annotations don't OCR cleanly. Multi-column layouts confuse standard parsers. And the document often lives behind a login, a firewall, or a file system — not on any public URL.

This is why most "AI research tools" do one of two things: either they give you a summary based on what they found on the web about your query, or they give you nothing at all when you point them at a file. Neither is useful when your actual work is reading the document itself.

The Enterprise Pivot and Its Blindspot

Perplexity has been transparent about its pivot: from "answer engine" to "agent company." The Computer agent product is expanding into Microsoft 365 workflows. Enterprise is the stated growth vector. That's a rational move — web traffic growth has flattened, and the next $100M in ARR is in enterprise seats.

But enterprise knowledge work isn't just email and spreadsheets. It's contracts, research reports, regulatory filings, internal memos, board decks. The Microsoft 365 integration helps you search across SharePoint and OneDrive. That's real value. But it still doesn't solve the parsing problem: what happens when the document you need is a scanned PDF, a faxed attachment, or a 400-page acquisition target report?

The tools that are winning on this problem — the ones doing actual document ingestion at scale — aren't the household names. They're vertical-specific platforms that built their foundation around document parsing first, then layered AI on top. The difference in outcome is significant: a well-built document intelligence layer can extract structured data from a messy PDF in seconds. A web search API called on the same document returns nothing useful.

The Real Market Gap

Here's the specific opportunity: document-intensive professionals — the ones whose entire job is reading, analyzing, and acting on long-form documents — have been poorly served by the AI boom. They've been offered better ways to search the web, better chatbot interfaces for their questions, and better API access to language models. What they haven't been offered is a reliable way to turn their own documents into queryable, analyzable, insight-rich knowledge bases.

That's the gap. Not the web. Not the model. The document layer.

The tools that close this gap won't just compete on citation accuracy or ARR growth rates. They'll compete on parse quality, language support for non-English documents, schema extraction from tables, and the ability to handle the 15 file formats that legal and financial teams actually use. That's a different kind of engineering investment, and it explains why the big numbers in AI research haven't translated into the document workflow improvements this audience needs.

Perplexity is a great product. But its $450M story is still, mostly, a web story. For the researcher who needs to understand what's actually in their files — the story hasn't started yet.