Enterprise AI Platform—Key Features for Success

Streamlined AI development is achieved through standardized tools and structured workflows. Structure enhances efficiency and ensures transparency and alignment with your business objectives. A well-organized framework makes managing AI projects more straightforward and productive.

An enterprise AI platform is a complete solution that provides organizations with means for creating, deploying, and managing AI applications on a larger scale. It includes tools that help with data discovery and analytics within the enterprise. It provides non-technical business users with data products they can use for large language model (LLM) training while also providing developers with multi-model support to choose the LLM that best fits their needs. It also offers no-code features for creating your own RAG workflows so you can customize your LLM along with centralized data governance and production-grade security.

As your AI applications grow, they must handle increased workloads without compromising performance. An enterprise AI platform provides the robust infrastructure, automated scaling features, and performance monitoring tools necessary to achieve this. 

This article explores the essential components of an enterprise AI platform and explains how it contributes to successful AI implementation and scaling within the enterprise.

Essential features in a complete enterprise AI platform

An enterprise AI platform has the following features.

The rest of the article elaborates on these concepts one by one.

Exploratory data analysis

Data is the building block of LLMs, providing the foundational element that enables these models to learn, understand, and generate human-like responses. High-quality and diverse datasets provide the raw material from which LLMs derive contextual understanding. 

Data scientists’ main concern when initially prototyping LLM apps is acquiring and preprocessing large, high-quality datasets. To customize LLMs for enterprise use cases, you require diverse data free from biases and privacy concerns. Balancing model accuracy with efficiency and navigating ethical considerations are also critical aspects that data scientists must address.

Exploratory data analysis—an overview

An enterprise AI platform should provide data analysis and discovery features for AI development. It should make it easy for data scientists to search for data available in your organization, preview it, and use it. It should continuously sample data and automatically compute characteristics such as mean, standard deviation, distribution of values, and outliers.

For example, Nexla is an enterprise AI platform that allows data scientists to access data instantly and test their AI application ideas quickly. It provides analytics and visualization tools to monitor performances, explore trends, and make informed, data-driven decisions. These capabilities are essential for understanding the nuances of AI use cases and continuously optimizing them to meet organizational goals.

Data product generation

Ensuring a dataset is ready for your AI use case requires much effort and resources. Common challenges include cleaning, organizing, and transforming raw data into a format LLMs can work with. This step is crucial to removing any noise, handling missing values, and for consistency. 

The data must also be accurate and relevant. This often means validating and enriching the data, a tedious and time-consuming process. Preprocessing large data volumes requires robust infrastructure and efficient processing tools, frequently distracting from the main focus of AI development.

That’s where data products come in. A data product is a cleaned and ready-to-use dataset designed to meet specific business needs—similar to a consumer product ready for use. They streamline data consumption, making it easier for stakeholders to access and utilize data effectively. 

An enterprise AI platform makes it easy for AI development teams to create and use data products. For example, Nexla connects to your data sources – files, databases, APIs, streams, etc. – and automatically generates data products. Your AI team can discover, create, and customize the data products before they use them for LLM training. Developers can get an API feed of the product to use directly in the AI application code, making it very convenient to use these data products in your AI application.

Enterprise AI platform supporting data product generation

Multi-model support

Enterprise developers can choose from many different large language models. For example:

An enterprise AI platform should support multiple LLM models so you can choose the best one for your use case. That way, you can improve existing off-the-shelf LLMs by tapping into organizational data to build custom applications

RAG workflow creation

Using off-the-shelf LLMs is cost-efficient and practical and gives faster time to market. Retrieval augmented generation (RAG) is the best approach to customizing an existing LLM with your data. RAG combines retrieval-based methods with generative models. It retrieves relevant information from an external dataset and uses the retrieved content to inform the off-the-shelf LLM’s generation process. That way, the LLM can answer questions about information not present in its original training data set. For example, you can build a chatbot that answers questions on your internal HR policies or an AI app that summarizes the latest news articles.

For RAG to work, you need data pipelines to move your custom data into a vector database. You can then set up your AI application to work as follows.

1. Convert prompt to vectors: Use an embedding service to transform the user prompt into a vector representation.
2. Retrieve similar vectors: Find vectors identical to the prompt vector in your vector database. (Search for information the user asks in your data)
3. Create an improved prompt: Use the context from the retrieved search results to enhance the original prompt.
4. Generate response: Pass the improved prompt to the AI model to generate a response. 

RAG workflow overview

Now, let’s consider a practical example of an HR support chatbot where we can use this workflow. Let’s say the user prompt is “What is our company travel policy.” Your RAG workflow will search for travel policy documents in your vector database and then enhance the prompt so the LLM can give a customized answer.

Data freshness is crucial in this scenario. When the travel policy changes, your enterprise AI platform should ensure that the vector database is promptly updated. This can be achieved through automated data pipelines that regularly sync your source documents with the vector database. 

For instance, whenever the HR department updates a policy document, the system can automatically detect the change, process the new content, and update the corresponding vectors in the database. 

Example prompt and response for RAG

An enterprise AI platform lets you build your RAG workflows without any code. For example, Nexla enables you to move your data from any data source to any vector database in just a few clicks.

Powering data engineering automation for AI and ML applications

Learn how Nexla helps enhance LLM models

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  Enhance LLM models like GPT and LaMDA with your own data
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  Connect to any vector database like Pinecone
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  Build retrieval-augmented generation (RAG) with no code

Centralized data management & governance

An enterprise AI platform provides a centralized system to connect data from anywhere in your organization to your LLM. A centralized approach is needed to ensure compliance with regulations and maintain accountability. You can also maintain clean, accurate, accessible data for all your AI projects. 

Having the correct information for LLM training (either in RAG workflows or prompt engineering/prompt tuning) ensures your models provide the desired response and gain user trust. Continuous access to up-to-date information prevents problems like LLM hallucination and data drift and increases customer trust. You can ensure that your AI applications are built on a solid foundation of reliable and contextually correct data.
For example, the enterprise AI platform Nexla offers unstructured and structured data integration to any vector database from any data source. You can manage all the data your LLM can access from a single system.

Production-grade security 

Security and compliance are non-negotiable in enterprise AI platforms. Beyond standard data encryption, AI systems require protection against model theft and adversarial attacks. Identity and access management (IAM) extends to controlling access to AI models and training data, while multi-factor authentication secures both user accounts and model deployment processes.

Role-based access control (RBAC) in AI contexts allows for granular permissions not just for data, but for model development, training, and deployment. This enables precise management of who can access, modify, and operationalize AI models and their associated data. You can read more about it in our LLM security guide.

Detailed logging and monitoring are crucial for AI systems. They track not only data access but also model usage, performance metrics, and potential anomalies in AI outputs. This helps promptly detect and respond to security incidents, model drift, or unexpected AI behaviors.

An enterprise AI platform should implement these AI-specific security measures alongside traditional safeguards. This ensures that AI applications perform well and meet legal and regulatory requirements, including those related to AI explainability and ethical AI governance. Such comprehensive security protects organizations from risks and liabilities unique to AI deployments.

For example, Nexla is secure by design—all data is encrypted by default, IAM is built-in, and you have complete choice and control over your platform deployment.

Recommendations

We give some recommendations for successful AI development with an enterprise AI platform.

Monitor LLM output quality

LLM performance can degrade over time due to data drift or changing user expectations. Regularly monitoring the quality of model outputs is essential. Implement automated monitoring tools to track accuracy and identify potential issues early. This ensures your AI application remains reliable and delivers high-quality results, maintaining user trust.

Human-in-the-loop

Incorporate human oversight into your AI systems to ensure accuracy and relevance, particularly in critical decision-making processes. Human-in-the-loop (HITL) methodologies involve human intervention in tasks such as data annotation, model validation, and handling edge cases or sensitive scenarios. This approach enhances reliability and helps address ethical concerns and biases that automated systems might miss.

Human in the loop in AI development

Ethical AI practices

Adhere to ethical guidelines and standards to ensure fairness, transparency, and accountability in your AI applications. Regularly audit your AI systems for biases and implement corrective measures when necessary. Ethical AI practices foster trust and credibility among users and stakeholders.

Conclusion

As we know, with the speed at which the AI space is growing, there is a need for a sophisticated and complete solution that provides a framework for organizations to work on AI use cases without worrying about data management and governance. Organizations need an enterprise AI platform that is reliable and offers a range of low-code/no-code data management features for data engineers, AI developers, and business leaders. Using solutions like Nexla reduces the development cost while increasing the efficiency of your AI development workflows.