You’re driving down the highway at high speed. Suddenly, everything you see is delayed by 2 seconds. What happens? If you’re lucky, you just miss your exit. If you’re unlucky, you make the nightly news, although you may not be in a position to enjoy the publicity.
Running a business today is like driving on that busy highway. You’re moving fast, the environment is constantly shifting, and if your vision can’t keep up, you’re cruising for trouble. By the same token, if your search solution can’t keep up, your business is making decisions based on outdated data—which means you could easily miss a lucrative turn or slam into a wall you never saw coming.
Why do search solutions struggle with rapidly changing data? Older solutions rely on batch processing, and less old ones use indexing or centralizing data (in data lakes or vector databases) before search is possible. While rapid indexing does exist, particularly for crucial data that needs to be available as it’s created, such systems are also too computationally expensive to implement across the entire data ecosystem. Moreover, even the best indexing systems can be delayed by high system load or large data volumes. Batch and many indexing technologies will always be slightly out of date—a lag of minutes or hours between when data is created and when it becomes searchable. As a result, decisions are made on old data.
In 2025, that delay is no longer acceptable. Modern AI search solutions—like SWIRL—connect directly to structured and unstructured data sources, eliminating delays, data duplication, and outdated insights. You get instant access to the latest information—whenever you need it.
Traditional AI Search is Always a Step Behind
Most AI-powered search tools have relied on indexing to function. Indexing is fine for web data that is designed to be found and which no one expects to be perfectly up to date. But for internal enterprise data, indexing is more complicated. Internal data is in a variety of formats, is often unstructured, and is frequently locked away in data silos, all of which combine to make indexing extremely difficult. Indexing systems struggle with ambiguity, especially in unstructured data, limiting accuracy and, therefore, searchability. Systems that index data as it is created find it difficult to keep up with large data volumes. The net result is that data is invisible until (re-)indexing completes and may still be difficult to find if the indexing system doesn’t correctly understand the content or context.
If a team makes a critical update to a report, users won’t necessarily find the latest version until the search index refreshes. The uncertainty isn’t just frustrating—it’s a productivity killer.
Worse, when data is being created across multiple solutions—email, CRM, databases, chat logs, and more—indexing can never quite catch up and teams are always making decisions with incomplete information.
Indexing enterprise data that wasn’t designed for easy search is a complex undertaking. It requires overcoming challenges related to the variety of data, understanding content and context, and scalability.
SWIRL AI Search removes these barriers.
The AI Search Revolution
With AI search, businesses no longer need to index, copy, or move data before it can be searched. Instead, SWIRL:
- Connects directly to data sources—databases, file systems, cloud storage, messaging apps—without requiring migration.
- Surfaces insights the moment data is created—no waiting for batch updates or re-indexing.
- Eliminates data silos—by accessing structured and unstructured data via a single, searchable interface.
It’s like streaming data, rather than downloading a static snapshot. You’re always working with the most current information available. After all, that’s how we drive.
Faster, More Accurate Decisions
Real-time AI search enables organizations to accelerate decision making, get instant, reliable insights, eliminate manual searches, and increase productivity. The focus becomes execution, not searching for the latest data.
- For finance teams, real-time AI search means seeing the latest revenue numbers without waiting for reports to update.
- For legal departments, it means knowing which contracts expire next quarter the second they are updated in the system.
- For sales teams, it means instantly retrieving the most up-to-date customer interactions from CRM, email, and support logs—without piecing together outdated records.
Real-Time Search, No Data Movement
SWIRL’s approach to real-time AI search has an added benefit: no data movement. By searching data in place, SWIRL eliminates the need for complex—and expensive—ETL (Extract, Transform, Load) projects, data lakes, and vector databases.
No data copying or uploading to third-party servers is crucial for data security. SWIRL runs in your private cloud or completely on-premises, including in air-gapped systems. Your data never leaves your secure environment, ensuring compliance and eliminating security risks.
The Future of AI Search Is Real-Time
Businesses can no longer afford slow, outdated search solutions. Real-time AI search ensures teams always have access to the latest, most accurate information—when it matters most.
No more waiting. No more guessing. Just real-time insights, instantly.
Don’t drive blind. See how SWIRL delivers real-time AI search—schedule your demo today.