Do you want the foundation of your AI future to be built on concrete or quicksand?
AI models are getting better every day. But when everyone can get high-powered AI models, the models are no longer the most important components of an agentic AI world.
Instead, as a recent article in Forbes observes (The AI Race Is Now About Databases — Not Just Big Models), the most important component is the data: the terabytes and petabytes of data sitting corporate databases, emails, Slack, Teams, cloud drives, and so on. And, as the article also points out, most modern databases aren’t built for the type of demands that agentic AI creates. Postgres? Built for batch jobs. Periodic queries. Peak loads in the thousands. Agentic AI needs far more than what Postgres, with its architectural limits, can hope to provide.
A Brand-New Database! Hooray?
The touted solutions, though, are dead ends: more new databases. More vendors with vector databases, smart databases, “work-flow native” databases (whatever that’s supposed to be). The dirty little secret of all these purported solutions is that they require you to move your data, centralize into the latest and greatest database technology that will solve everything… until it doesn’t. And that’s not even getting into the technical morass of pulling data together from across the typically diverse corporate technology landscape. On the bright side, by the time you find out the “just install this and centralize” approach doesn’t work, there’ll be a new technology, a new database, ready for you to buy because “this time for sure!”
The problem with all these solutions that sound so good in theory (or in a Powerpoint presentation) is that they are dealing with the world as the designers wish it were, not the corporate data landscape as it actually is.
Data is scattered. Silos exist, for good reasons and bad. Technologies are wildly diverse. And the story data can tell us is deeply embedded in its context. Beyond the months of effort required for data migration, moving the data into these new databases loses all of that context, creating blind spots that won’t become obvious until it’s too late to go back. Well, unless you’re planning to maintain multiple copies of all the data, which is its own special nightmare.
Businesses can’t afford to redesign their entire technology stack whenever some hot new idea comes along. AI needs to build on what is, not wait for some perfect, utopian future. Chasing that shiny future vision is building on a foundation of quicksand: ever changing, ever shifting, never stable.
A Solution for the Real World
SWIRL was built for exactly this reality. It doesn’t ask you to rip out your systems, rewrite your data strategy, or forklift your information into a proprietary platform. SWIRL connects directly to your existing technology stack—databases, cloud drives, messaging platforms, content systems—using native APIs and secure search interfaces. Your data stays where it is. Your access controls stay intact. And your AI agents get the visibility they need, in real time, without compromising security or creating fragile synchronization jobs.
This isn’t yet another “single source of truth” fantasy. It’s a federated, orchestrated approach that works with the messy, multi-system infrastructure real companies already have. SWIRL doesn’t just find data—it ranks it, deduplicates it, and delivers it to AI agents with context intact. No extra pipelines. No extra copies. No surprises.
Agentic AI doesn’t need another database. It needs access. Insight. Confidence. And it needs to work within the guardrails of enterprise reality. That’s what SWIRL delivers: a foundation built on concrete—not wishful thinking.
To find out more about SWIRL, request a demo today.