Over the past few years, AI has become one of the key technologies being adopted by organizations worldwide. From helping search and analyze data and summarize findings to support business decision-making, AI is no longer a distant or unfamiliar technology for enterprises.
However, when AI works with Enterprise Data, organizations need to consider more than just the capabilities of the AI model. They also need to understand where the data is processed, who can access it, and the extent to which they can control the environment in which the AI operates.
Every organization has its own sensitive data, internal information, and security requirements. As a result, the deployment model has become another critical factor in ensuring that AI can be implemented in a way that aligns with each organization’s infrastructure, security policies, and data requirements.
Using AI is easy. Deploying AI in an enterprise is a different problem
Adopting AI may seem simple. Organizations can simply choose an AI Model or AI Tool that meets their needs and start using it right away. But when AI is used to work with enterprise data, the considerations go beyond what the AI can do. Organizations also need to understand how AI works with their data.
Organizations need to know:
- Where is the data being sent?
- Who can access the data?
- Which LLM is processing the data?
They also need to ensure that data does not leave the environment under their control, AI usage can be monitored and audited, and, most importantly, for organizations with strict security requirements or policies that prohibit Internet connectivity, the environment in which AI operates must be defined from the outset.
Therefore, choosing AI is not simply about asking, “Which AI is the most capable?” Organizations must also consider, “how will AI work with our data?”
Your AI, Your Deployment, Your Choice
Due to the difference in infrastructure environment, security policies, and data management requirements. As a result, there is no single Deployment Model that fits every organization.
For this reason, Blendata Apollo is designed to support a wide range of deployment options, allowing organizations to choose the model that best fits their infrastructure and data requirements.
Organizations can choose from three deployment options:
1. On Cloud
Organizations can use Blendata Apollo on the Cloud without having to install the system within their own infrastructure. This approach provides greater flexibility and is well suited to organizations that already operate in a Cloud environment. Blendata Apollo is hosted and managed by Blendata, while the Agent responsible for connecting to data is installed within the customer’s own Cloud environment, such as a VPC.
At the same time, data remains within the Cloud environment where Blendata Apollo operates, allowing Blendata Apollo to answer questions and analyze data while the actual data remains within the environment designated by the organization.
With the On Cloud deployment, organizations can connect to external AI/LLM models of their choice, such as Anthropic Claude, OpenAI GPT-4o, or Azure OpenAI.
2. Your Infrastructure (On-Premise)
For organizations that want to deploy Blendata Apollo on their own infrastructure, the system can operate within the organization’s environment and remain under its control across infrastructure, network, and data management. The Connector Agent is installed within the customer’s own infrastructure, while the component responsible for generating SQL commands remains connected to Blendata’s systems.
Data remains in the organization’s existing databases within its infrastructure, such as Oracle or SQL Server. Organizations can also choose to use external AI models in accordance with their policies, such as Claude, GPT-4o, or Azure OpenAI.
The Your Infrastructure deployment also supports Audit Logs that record questions, SQL commands, and access to results in an immutable format, enabling organizations to conduct audits and review activity retrospectively.
This deployment model is therefore well suited to organizations that need greater control over their infrastructure, network, and data access within their own environment.
3. Air-Gapped
The final deployment option is designed for organizations with stringent security requirements that need their systems and data to operate in an environment completely isolated from the Internet. The entire system operates within the organization’s infrastructure, with no connection to external cloud environments or networks.
The Blendata Apollo Connector Agent and all processing take place within the organization’s infrastructure, with no outbound connection to external Cloud environments. Similarly, all data remains in databases within the organization, and no data can be sent outside the organization’s network.
The LLM must also be an AI model deployed within the organization itself, such as Ollama. No APIs from external AI models are used.
This deployment model also supports Audit Logs that record questions, SQL commands, and access to results in an immutable format for retrospective auditing. All logs are stored within the organization, just like the other data.
AI That Fits Your Infrastructure
Adopting AI in an organization can improve efficiency in many ways, from reducing the time and steps in decision-making, making it easier to turn data into insights, and enabling organizations to identify important business data and trends more quickly. Therefore, choosing a Deployment Model that aligns with an organization’s constraints is an important part of ensuring that AI can work effectively with enterprise data.
Blendata Apollo supports On Cloud, Your Infrastructure, and Air-Gapped deployments, allowing organizations to choose the model that best fits their Infrastructure, Security, and data requirements.
For more information about Blendata Apollo, visit https://blendata.com/product/blendata-apollo/
