What You Need to Use AI to Turn Data Into Products

When we think about data products, most people think about downloading apps on their mobile devices or to their computers. While apps are certainly data, data products are hardly limited to intangibles like software. In our modern, technology-driven world, everything starts out as data. Cars, airplanes, computer chips, life-saving drugs, you name it, it starts as data distributed across the corporate technology ecosystem. Turning data into physical products is the key to creating value. How easily, quickly, and effectively companies—such as Boeing or GM—can turn the data representing an airplane into an actual airplane or turn data representing a car into an actual car depends on how easy it is to find and use data.

The Light’s Better Here

Computer systems are, however, extremely diverse and complex. It’s unlikely that any one person, or even one team, knows about all of the datastores. While its easy to look in the databases everyone knows about, that’s a bit like the old joke about searching for missing car keys not where they were lost but rather under the streetlight because the “light’s better here.” You’ll find the data you know to look for, but not the data you don’t know about (for instance, the part about making sure the doors don’t fall off the airplane).

Beyond databases, there is also a massive amount of knowledge trapped in the information black holes that are productivity and collaboration apps: Slack, Webex, Confluence, email, and so on. Despite the best efforts of app developers, the amount of information trapped in these apps swiftly outstrips the ability of people to find and use that information—search is a hard problem. It takes too long to find information and too long to read through all the hits and determine what is actually relevant.

Silo World

A business involved in manufacturing possesses a lot more data than they know about and can use effectively. Unfortunately, much of that data is locked away in organizational or technological data silos: geographically separated teams might not know about one another or have only the haziest idea of what the other groups are working on; different parts of the company might use different technologies, making interoperability difficult; or any combination. As organizations get larger, knowing what is known becomes ever more difficult.

AI to the Rescue?

The obvious question at this point is can AI help solve the problem?

That depends.

An AI isn’t going to just know the answers to your questions unless you’ve trained the AI on your data. While that’s a possibility, it’s also a lot of data movement, data preparation, and processing. And you still need to address new data and real-time data.

A more serious problem is that what an AI knows an AI can leak. Training an AI on your data means putting your data—the crown jewels of your business—into an AI. And that is a security nightmare. Sufficiently clever prompt engineering can trick the AI into divulging all sorts of information that the questioner should not be able to access.

Bringing the data to the AI is not a good solution. A far better solution is bringing the AI to the data.

Bringing the AI to the Data

It may seem like a minor distinction between training an AI on your data and allowing an AI access to your data, but in that distinction is a world of difference. In the former case, the data becomes part of the AI’s knowledge base. In the latter case, the AI can be used to analyze data and determine relevancy, but it doesn’t remember what it has seen. This second approach requires much less data movement and doesn’t compromise data security. It also lets you leverage the power of AI to identify relevant data in any location, including newly generated real-time data—for example, sensor data from manufacturing equipment or an ongoing data feed from a working prototype.

Bringing the AI to the data requires an AI infrastructure that provides several key capabilities.

Universal Connectivity

The AI infrastructure must be able to connect with numerous applications and data sources regardless of format or application type, including structured data from databases, content from email, office documents, and collaboration apps such as Teams, Webex, Confluence and Slack. Source agnosticism makes it possible to leave data in place, adopt existing security protocols, and search only those data sources that the user is permitted to view.

AI Flexibility

AI infrastructure software is not the One True AI that can solve everything. Rather, it is a framework that lets you connect to an arbitrary number of LLMs and AI models. You can take advantage of the power of any AI without designing your system around that AI. You can swap models in and out as the situation requires, whether that means using specialized models to handle specific, domain relevant tasks (such as monitoring the status of manufacturing equipment) or monitoring drift and swapping out models that are no longer delivering useful results.

Powerful, Unified Search

AI Infrastructure software should keep track of the data sources that an employee is permitted to access, so that the employee doesn’t have to keep track manually. It should enable searching of all permitted data sources and then use AI to help manage the results, pruning and ranking by relevancy so that users are able to quickly find the information they are looking for. It should enable dynamic, interactive refinement of the search results, essentially enabling you to have a conversation with your data.

Prompt Enhancement

Because an AI is not a Generalized Omniscient Device, an AI can return incorrect, inaccurate, or hallucinatory results. AI infrastructure software should incorporate prompt enhancement and use of Retrieval Augmented Generation in order to minimize or eliminate these problems. Combining prompt enhancement, RAG, and other techniques for managing search, increases the validity and trustability of results and frees the user from the laborious process of attempting to take these steps manually.

Ease of Use

One of the biggest limitations on organizations adopting AI is the lack of trained experts. AI infrastructure software should be simple to install. It should be low-code and configuration based and shouldn’t require an army of computer science PhDs to make it work. A major benefit of AI infrastructure software is to make it easier for everyone in your organization to benefit from AI.

AI Infrastructure Software Isn’t Science Fiction

AI infrastructure software may sound like science fiction but it exists today and is available right now. SWIRL’s innovative approach to AI infrastructure—SWIRL AI Connect—provides a powerful, efficient, and flexible framework for incorporating AI into your business. SWIRL can evolve and scale with technological advancements so you can avoid being locked to a specific AI model.

Capabilities

SWIRL provides several key capabilities that help you maximize the return on your AI investments:

Connects to Everything: SWIRL connects with over a hundred (and counting) apps and databases, so you can access data no matter the location or format.
AI Agnostic: Avoid vendor lock-in and ensure flexibility by seamlessly swapping AI models to suit your evolving needs.
Specialized Models: Leverage state-of-the-art Large Language Models (LLMs) and easily integrate new, specialized models for specific use cases.
Low-Code and Configuration-Based: Accelerate AI-powered app development with minimal code, freeing you to focus on business results rather than complex infrastructure.
Granular Data Access and Firewall Protection: Maintain tight control over sensitive data and ensure top-level security by deploying AI models within your firewall.
Streamlined AI operations: SWIRL is the middle layer between applications and AI models, helping to manage complexity, optimize resource usage, and ensure the infrastructure adapts to the rapidly changing demands of AI workloads.
Scalability: SWIRL enables AI systems to handle complex and variable demands, such as large-scale model training and deployment, without compromising performance. SWIRL simplifies enterprise wide deployments, so you can easily scale AI initiatives across your business.
Powerful Search: SWIRL combines unified search, prompt enhancement, and Retrieval Augmented Generation to provide more accurate and contextually relevant results than generative AI models can provide on their own. SWIRL enables you to have a conversation with your data—it’s like having an intelligent, interactive data catalog or expert librarian to guide you.

Benefits

SWIRL provides significant benefits:

Better Decision-Making: Enhance strategic decision-making with accurate, real-time insights and predictions.
Increased Efficiency: Reduce expenses and increase overall workflow and productivity.
Improved Customer Satisfaction—RAG enhanced, context-aware AI responses enhance customer interactions and service quality, improves the user experience, and leads to higher customer retention and loyalty.
Enhanced Feedback—Break down silos and make key performance data accessible and available for analysis.
Faster Time-to-Market—Accelerate development and launch of new AI-powered products and services, enabling quick adaptation to market changes and customer needs.

Manifesting Data

For businesses that seek to turn data into products, a key factor in rapid value generation is their ability to find the right data at the right time, make use of that data, and respond dynamically to feedback. AI can dramatically accelerate each step in that process. SWIRL’s AI infrastructure software is the framework that lets businesses take full advantage of the strengths of AI to maximize the organization’s ability to find and use data. SWIRL provides the combination of universal connectivity, AI flexibility, search, prompt enhancement, and ease of use that turns AI from an expensive toy into a powerful tool.

To find out how you can be a leader in making AI work for your company, contact SWIRL today.

SWIRL was named by KM Magazine as one of the top 100 companies empowering intelligent knowledge management.