You can’t build a house without a foundation. However, if you had enough people constantly repairing it and putting supports under the house, it would stand for a while—at least until the first woodpecker comes along. Living in that house wouldn’t be much fun as you’d never know when you were about to fall through the floor. Similarly, provided you have enough people patching it and dealing with the inevitable errors and crashes, software without a good foundation can be kept running until it becomes so unwieldy it collapses under the weight of its complexity. Just pray that the technical debt lands on someone else. 

AI systems—generative AI, machine learning models, or deep learning models—are like all other software projects. You can run them without a good foundation, but why would you? At some point—and that point is usually when it’s most inconvenient—it’ll all collapse.  

The common element in generative AI, machine learning, and deep learning is data. To take advantage of the power of AI systems, they need access to extremely large amounts of high-quality, relevant data. That need for data gives rise to three important considerations: 

• Security—Data needs to be kept secure and compliant with regulatory requirements. 
• Scalability—Scalability means both that the system needs to run smoothly as demand increases, and that AI solutions must scale across the enterprise. 
• Monitor and Maintain Models—Machine learning and deep learning models drift over time. Manual monitoring is error-prone and is so person-intensive that it prevents enterprise-wide deployments. Rather, models need to be monitored automatically, swapped out when they drift, and—when relevant—retrained with updated data. 

Attempting to manage these considerations without a good foundation is a recipe for frustration and potential disaster. As easy as using AI may seem at first, as systems proliferate and are used for more significant tasks, the complexity grows exponentially. Pretty soon you’re spending all your time running around repairing and supporting the systems and losing out on all the benefits AI is supposed to bring. You are working for the AI rather than the other way around. 

Infrastructure Matters 

AI infrastructure software is vital to long-term, scalable, successful AI projects. AI infrastructure software gives you control over your AI systems, whether you are using GenAI, machine learning, or deep learning models, or any combination. Infrastructure software is the foundation that enables you to truly benefit from your investments in AI. 

SWIRL Makes Infrastructure Work 

SWIRL AI Connect offers a comprehensive solution for AI infrastructure that combines ease of access to data, with security, scalability, and the ability to monitor and maintain AI systems.  

SWIRL AI Connect provides the foundation for scaling AI across the enterprise. SWIRL AI Co-Pilot takes things a step further by offering intelligent assistance directly to business users, acting as a virtual collaborator that provides real-time insights and decision support across numerous business functions.  

SWIRL AI Co-Pilot transforms any capable generative AI into a chatbot, allowing users to seamlessly converse with their data while incorporating metasearch and RAG. SWIRL runs on-premises or in your private cloud; integrates with existing security protocols, ensuring no unauthorized access to data; requires no bulk copying or indexing of data; and supports all languages compatible with the installed LLMs.  

Finding the Right Data 

Your company has all the data you need. It resides in siloes and in all the places where you have never looked for it. SWIRL enables businesses to securely leverage AI to unlock their data and dramatically boost productivity.  

SWIRL can query over a hundred different apps and data stores, including team collaboration and productivity apps—such as Teams, Confluence, Slack, Webex—as well as email programs and the user’s local file system.  

You can use any approved AI to talk to virtually any data store, giving you access to all of your data—including data you didn’t know about (provided you are authorized to access it). SWIRL breaks down silos. 

SWIRL’s innovative use of vectorless RAG and automated prompt enhancement grounds results in facts, reducing or eliminating the risk of AI hallucinations and errors. Swirl uses meta-ranking and reranking to organize results by relevancy to the original query, providing you results you can use—and trust.  

Security 

SWIRL AI Connect does not require that data be loaded into vector databases or taken outside the corporate firewall; rather, SWIRL brings AI to the data, leaving the data in place.  

SWIRL follows a zero-trust security model, eliminating the need for data migration (and therefore eliminating the need for time-consuming, expensive ETL processes) and instead enabling AI systems to connect directly with source systems safely within the corporate firewall. Data remains secure in your data repositories. 

Scalability 

AI systems need to handle complex and variable demands such as large-scale model training and deployment or market-driven spikes in activity. 

SWIRL enables horizontal and vertical scaling. This flexibility allows AI systems to handle sudden changes in demand without compromising performance. 

SWIRL also enables AI projects to scale across the enterprise. Because SWIRL simplifies and automates AI deployments, and allows users to work with any approved AI, users spend more of their time doing productive work and less time working for the AI.   

Monitoring and Maintaining Models 

SWIRL automates continuous monitoring of AI performance so you can detect drift early. SWIRL makes it easy to retrain models or swap out existing models in favor of newer or more specialized and sophisticated models.  

Automated monitoring also makes it easier to scale AI projects across the enterprise.  

A Strong Foundation 

Just as a strong foundation is essential to having a house that is sturdy and comfortable, so is a strong foundation essential to having AI systems that work for you rather than the other way around. SWIRL AI Connect and SWIRL AI Co-Pilot provide the essential infrastructure to build that foundation. With SWIRL, your data stays secure even as your AI systems have access to the data they need when they need it. You can monitor model performance and quickly swap out underperforming or drifting models and replace them with newer or more specialized models. SWIRL puts you in control.  

To find out more about how SWIRL’s AI infrastructure software can help your organization maximize the benefits of your investments in Generative AI, machine learning, and deep learning models, contact us.