The Truth is Out There
Finding data can feel like an episode of the X-Files. The truth is out there, but no matter how much you search it’s always just out of reach.
Where To Look?
The first problem is knowing where to look.
Which databases do you have access to? Where is information being stored? As much as it might seem logical that organizations store their data in clearly marked and easily accessible locations, as most of us who have worked in corporate environments can attest, that’s rarely the case—like tribbles, corporate databases tend to proliferate.
Not Just Databases
Useful information isn’t just in databases. It’s also in email and Slack and Confluence and Webex and whatever other collaboration and productivity apps (aka information black holes) that exist in your corporate ecosystem. Finding useful information in any of these apps can easily feel like a job for a pair of intrepid detectives.
Seek and You Will Find. And Find. And Find.
Corporate databases aside, searching for information in library databases, research databases, or professional publication databases can be worse. Even once you figure out which databases you should search, figuring out the right search terms can be its own special nightmare. It often feels like a game of “Guess what the author was thinking.” Make your search too narrow and you won’t find what you’re looking for. Make it too wide, and you get buried in results, most of which are irrelevant.
What’s Useful?
Once you’ve spent the time to figure out where to search and what to search for, evaluating those results is also maddening. Is this information close enough to be useful? What about this? Human decision-making is a finite resource and it’s exhausting using it to find the right information just so you can get on with the real work. The more effort expended finding and sorting through irrelevant information, the less energy and concentration are available to actually use that information once you have it. The hit to morale and productivity is huge.
But Wait, There’s More
The problems discussed thus far relate to data that is relatively static, or at least changing slowly. One possible solution to that problem is dumping it all into a vector database and letting an AI sort it out. That might work for some information in databases—if you don’t mind time and cost of ETL, data security, and data duplication issues—but it won’t work for email, Slack, Webex, Confluence, and so on.
And those are not the only types of data for which the vectorize solution won’t work. Modern manufacturing systems are extremely data intensive. A chip fab, for example, collects sensor data about every step of the manufacturing process. If any machine raises an alert, you need to know what just happened. That sensor data won’t be sitting in some database; rather, it’s new data. You need to be able to find that data and analyze it immediately, not put it in a vector database and train an AI on it. You don’t have time for that.
Find and Refine
Fortunately, there is a way to use AI to find the right data quickly.
SWIRL enables you to have a conversation with your data, refining your search dynamically using natural language. You can securely leverage AI to break down silos, find relevant data, and dramatically boost productivity.
A software development organization uses SWIRL to find appropriate data for development and testing, cutting 40% off the time it takes to release software.
A large company uses SWIRL to locate data for research and report generation. SWIRL reduces the time it takes to find appropriate, relevant data from 2-3 hours per day to less than one hour, decreasing employee burnout and sharply increasing productivity.
SWIRL provides the ability to use any approved AI LLM to talk to virtually any datastore, giving you access to all of your data—including data you didn’t know about (provided you are authorized to access it). You don’t need to wonder where to search; SWIRL will handle that for you. And SWIRL can find relevant data in your email, Slack channels, Confluence, Webex, and the like. SWIRL can also search real-time datastores, so it will find that sensor data when you need it.
You can use any LLM, machine learning, or deep learning model provided it’s available based on your organization’s policies. SWIRL enables you to use specialized AI models, so you can choose the most appropriate AI for the task at hand.
SWIRL follows a zero-trust security model, eliminating the need for data migration and instead enabling AI systems to connect directly with source systems safely within the corporate firewall. Data remains secure in your data repositories; SWIRL brings the AI to the data.
SWIRL’s innovative combination of metasearch and Retrieval Augmented Generation grounds results in facts, reducing or eliminating the risk of AI hallucinations and errors.
You no longer need spend hours going through your results to find what really matters: SWIRL organizes results by relevancy to the original query, providing you results you can use—and trust.
The truth is out there. SWIRL helps you find it.
Contact SWIRL for more information on how you can boost productivity in your organization.