You don’t need to rebuild your information stack to use AI. You need to stop letting your stack run the show.
Every time a company tries to “get serious” about AI, the same pattern plays out: centralize the data, deploy a vector database, spend months re-architecting infrastructure, and then pray that something useful comes out the other side.
By the time the connectors are in place, the data is outdated. The LLM has changed twice. And the budget? Gone.
This may be “normal” if normal means “we’ve been trying variations on this theme for 25 years.” But let’s stop pretending that it works.
The problem isn’t where your data lives, it’s how you access it!
The common wisdom says fragmented data is the enemy. The truth is, data fragmentation is a fact of modern enterprise life. You’ve got critical information in GitHub, SharePoint, Salesforce, Slack, Notion, S3 buckets, ancient file shares, and custom internal tools. That’s not going to change—and trying to force it to change is where so many AI initiatives go to die.
What breaks enterprise AI isn’t data sprawl. It’s stack sprawl. It’s the belief that every question must be routed through a vector pipeline, every source copied into a warehouse, every system rebuilt from scratch. Centralization projects add technologies, not subtract them. The stack just gets more unwieldy.
But here’s the thing: most AI use cases don’t need that. They don’t need all the data, in one place, at one time.
They need just enough of the right data, at the right moment, with the right access controls. And when they need it, they need it fast.
The infrastructure tax is real. And it’s growing.
Here’s what traditional AI infrastructure demands:
- Endless ETL pipelines that are fragile, expensive, and slow
- Manual tagging and curation just to make data usable
- Duplicated storage that blows up your compliance and security risk
- Stack complexity that increases operational burden without increasing insight
Even if you survive the rebuild, you’re left with a brittle, rigid system that can’t adapt to real-world change. Add a new tool? Rebuild the pipeline. Update permissions? Redo the sync logic. Want to integrate a new LLM? Rewrite the whole stack again.
This isn’t AI. It’s a maintenance swamp with a pretty user interface. And more quicksand than you’ll find in a 1950s adventure movie.
Enter SWIRL: Your AI Assistant’s Real-Time Data Layer
SWIRL flips the script.
It doesn’t copy, migrate, or reformat your data. It doesn’t force you to centralize everything. It doesn’t ask you to trust a black box.
Instead, it connects to live systems—directly—on demand.
Here’s what that means in practice:
- Zero ETL: No pipelines. No replatforming. No extra infrastructure.
- Live search across all tools: SWIRL searches SharePoint, GitHub, Notion, Salesforce, internal wikis, file shares—wherever your data actually lives.
- Context-rich answers: SWIRL doesn’t just return documents. It ranks results by confidence, extracts relevant metadata, deduplicates content, and summarizes across formats.
- Real-time access controls: It respects your existing security model, so users (or agents) only see what they’re supposed to see.
- Agent-ready: It powers AI assistants with context-aware, actionable insights, turning them into reliable partners—not hallucinating liabilities.
This isn’t theoretical. It’s deployed and working. SWIRL delivers the connective tissue your AI needs—without making you tear out your existing architecture.
Stop chasing data. Start using it.
You’ve been told the only path forward is to rebuild everything. But that’s a myth that benefits vendors, not users.
The real path forward is intelligent access, not forced centralization. It’s about letting your agents and your team talk to your data, wherever it lives—without sacrificing security, context, or speed.
Let’s make that possible. Today.
SWIRL repeals the infrastructure tax. Your data stays where it is. Your team gets the answers they need. No migration. No friction. Just intelligence that works.
Contact us for a demo or download our most recent white paper.