For any mission undertaken by government agencies, data is critical for reaching goals and meeting deadlines. If data is fragmented or siloed, your team’s ability to develop a single view of customers, data, and related parties is impacted. AI-enabled platforms help agencies connect related data to reveal previously hidden relationships, which help them make accurate decisions and foster transparency to improve trust.

There are some important differences between AI platforms and AI tools agencies need to understand to make the best choice of technology. Your goals and technology roadmap could be impacted by platform and tool options.

What’s an AI platform?

By definition, a platform is built to do more than a single tool. Easy enough to understand – but the definition becomes more complex when you add AI to the mix. For example, a platform may exist to only develop, deploy and manage AI applications – or it may use AI to perform other capabilities as the end result.

Either way, the platform likely includes tools, libraries, frameworks, and infrastructure components that are designed to support end-to-end AI workflows. AI platforms often offer a mix of functionalities, such as data preparation, model development, model training, deployment, monitoring, and integration with existing systems. This is the case with the Quantexa Decision Intelligence platform. The platform helps agencies solve a range of critical data quality and analytical challenges when they are fighting fraud, enforcing compliance, collecting taxes, investigating crime, and securing borders.

Think “comprehensive” when your organization or agency is considered an AI platform. In addition to specific technologies, AI-driven platforms typically offer scalability, integration with other services, support for various AI frameworks (like TensorFlow, PyTorch), and often an API for interacting with data models.

Here are the features you’ll usually find in an AI-driven platform:

• AI platforms offer a holistic approach with tools and services that cover multiple stages of the AI development lifecycle, from data preprocessing to deployment and monitoring.

• AI platforms provide flexibility and customization allowing developers and data scientists to leverage diverse algorithms, frameworks, and programming languages to build and customize AI models and applications.

• AI platforms offer scalability and efficiency and can handle large-scale data processing and model training, often leveraging distributed computing and parallel processing techniques to optimize performance.

• The AI-driven platforms help teams collaborate by providing features for version control, experiment tracking, model sharing, and managing AI development workflows allowing for collaboration and workflow management.

• The platforms have integration capabilities and offer integration with external tools, data sources, and frameworks, creating seamless interoperability with other systems and technologies.

What’s an AI tool?

A tool – AI or otherwise – are specific software or components that serve a particular purpose in an AI workflow. The tools focus on solving a task or providing a function. For example, with the Quantexa Decision Intelligence platform, the Entity Resolution tool creates a single view of entities dynamically across internal and external data sources, and rectifies data matching quality issues by highlighting over or underlinked data and the root cause of issues. You can use this feature of Quantexa as a stand-alone function to fill gaps in your existing AI ecosystem.

Here are the characteristics of AI tools:

• They have task-specific functionality and address a specific need or task, such as data preprocessing, model training, feature selection, model evaluation, or visualization.

• AI tools have a narrower scope compared to AI platforms. They typically serve a specific function within the AI development process, rather than providing a complete end-to-end solution.

• AI tools are often designed to be modular, allowing users to select and combine different tools based on their specific requirements and preferences.

• AI tools can have a single-function or be domain-specific. They can be specialized for a particular use case or domain, catering to needs or challenges within that area.

• AI-driven tools have a certain amount of interoperability and can often be integrated into AI platforms or used alongside other tools, providing flexibility and compatibility within broader AI workflows.

Which do you need: an AI platform or an AI tool?

The answer very much depends on where your agency is at in terms of budgets and missions. The guidelines below can help narrow down the decision to add tools that address specific needs, or to invest in an AI-enabled holistic platform:

• Match AI solutions to specific project needs and strategic goals.

• Consider the total cost of ownership and strategic product selection, especially for government agencies.

• Avoid vendor lock-in by querying vendors about open and flexible technologies that support integration with other solutions.

Whether you choose a full feature AI platform or pick an AI tool will depend on your needs. That's why Quantexa is designed in modules so you can either deploy them all in an integrated solution or apply a module to fill gaps in your existing AI ecosystem. Quantexa is built on open-source, industry best-of-breed technology for low-code integration into your IT environment.

To find out more about what Quantexa can offer, watch this video on how Quantexa is innovating with GenAI.