Model ML Leverages Microsoft Azure for Innovative Agentic Service in Highly Regulated Markets
Jul 10, 2025
6 MIN. READ
Read our blog with Microsoft here:
https://www.microsoft.com/blog/modelml
The rapid pace of innovation in AI is unprecedented—new models, techniques, and tooling emerge at a frenetic pace, demanding constant adaptation and ongoing iteration to stay competitive. That’s why Microsoft is always on the lookout for companies who keep pace with the latest technologies, even in the most challenging markets.
For this exact reason, Microsoft decided to partner with Model ML, an AI native workspace for financial services which not only keeps up with the incredible pace of evolving technology—it drives it, deepening its offering with agentic capabilities and expanding integrations and automation layers with every release.
With a flexible user interface (UI) and advanced agentic system, Model ML enables users to automate thousands of tasks and perform research at unprecedented speeds, across open data sources, financial data providers, internal documents, call transcripts, and more.
In the world of finance, however, mitigating risk is often more important than the maintaining the speed of innovation. Enterprise customers—especially in financial services—have strict requirements around security, privacy, and compliance. That’s why Model ML’s technology infrastructure is set up to push the boundaries of innovation while still meeting the rigorous standards through a two-layer approach:
A hardened base of architecture, security controls, and change management procedures.
A rapid development layer that maintains security as a first principle.
Just as important, Model ML empowers teams with tools that allow them to drastically reduce time spent on routine analysis, slide deck review, and report generation as well as perform new kinds of analysis that were previously unfeasible at scale. All within a workspace that feels familiar to any modern office worker.
"Model ML has the potential to entirely transform the financial services industry. By combining a simple, user-friendly interface with a powerful agentic system that integrates seamlessly across all your data sources, Model ML helps you work smarter, uncover deeper insights, and significantly improve efficiency."– Sir Noel Quinn, Group CEO at HSBC
Security at the core
Using a flexible single-tenant deployment model on Azure tailored for the security and operational needs of enterprise customers, Model ML provides each single-tenant client with a dedicated environment based on private Azure Virtual Networks, supplying private, customer-specific instances of databases, in-memory cache, secure Azure Blob Storage, and Azure Service Bus for asynchronous messaging—ensuring total isolation and compliance with SOC 2 and ISO 27001.
Critical enterprise requirements—such as encryption-at-rest, high-availability clusters, automated backups, role-based access control, and private endpoints—are fully implemented across the architecture. Monitoring and analytics are handled through Azure Monitor and Log Analytics, providing real-time insights, alerting, and audit trails for all activity.
With this foundation, onboarding a new customer can be achieved in as little as one hour—from provisioning through to full operational status—while maintaining robust security, compliance, and performance guarantees. With Azure’s deep commitment to enterprise security and innovation, Model ML empowers organizations to adopt state-of-the-art AI at the speed their business demands, without compromising on trust or control.
Azure Kubernetes Service (AKS) and Virtual Machine Scale Sets power the application layer, enabling zero-downtime deployments and seamless horizontal scaling. In so doing, Model ML can grow from serving a single team to an entire division or business vertical without disrupting its existing customers.
Security is at the core of Model ML’s approach to rapid innovation. The development process has been engineered to encourage and enhance innovation within tight security boundaries. This approach has three steps: Guard, Evaluate, and Control.
Guard is focusing on taking any idea from a sandbox to our main codebase. The dev environment has all the same security controls that the production environment has. As a result, nothing can be merged unless it adheres to the standards. Additionally, there is a strict vendor and open source software (OSS) security review process and internal development is always prioritized.
Evaluate is the route of introducing a new idea into production.Every new module or agent update undergoes rigorous internal validation using a proprietary validation framework, ensuring both high accuracy of responses and adherence to compliance requirements before reaching production environments.
Control is what Model ML gives its clients to satisfy their specific requirements. Model ML’s modular architecture and extensive configuration options empower customers to tailor deployments to their unique risk, governance, and data residency policies—whether that means enforcing bring your own key (BYOK) or limiting certain feature access to a subset of users.
Augmenting existing Azure infrastructure
Many Model ML customers also choose Azure as their cloud provider. In these instances, Model ML can deploy directly within their cloud perimeter, unlocking advanced features such as custom agents for internal datasets. Together, these capabilities allow analysts and decision-makers to see their data estates at a scale and speed never possible before, strengthening current processes and unlocking new opportunities.
"Azure gives us the building blocks to isolate every customer environment, run complex workloads at scale, and enforce enterprise-grade controls—all without slowing down the pace of innovation."–Roman Kastusik, Software Engineer at Model ML
Looking ahead with Model ML
Model ML is on a mission to transform the financial services industry by bringing the power of AI to every level of analysis and decision making. Having Microsoft as a partner not only helps address security and compliance requirements—it lays the groundwork for scalable, trust-based AI adoption as Model ML leads the next wave of transformation across financial services and beyond.
Read our blog with Microsoft here:
https://www.microsoft.com/blog/modelml
The rapid pace of innovation in AI is unprecedented—new models, techniques, and tooling emerge at a frenetic pace, demanding constant adaptation and ongoing iteration to stay competitive. That’s why Microsoft is always on the lookout for companies who keep pace with the latest technologies, even in the most challenging markets.
For this exact reason, Microsoft decided to partner with Model ML, an AI native workspace for financial services which not only keeps up with the incredible pace of evolving technology—it drives it, deepening its offering with agentic capabilities and expanding integrations and automation layers with every release.
With a flexible user interface (UI) and advanced agentic system, Model ML enables users to automate thousands of tasks and perform research at unprecedented speeds, across open data sources, financial data providers, internal documents, call transcripts, and more.
In the world of finance, however, mitigating risk is often more important than the maintaining the speed of innovation. Enterprise customers—especially in financial services—have strict requirements around security, privacy, and compliance. That’s why Model ML’s technology infrastructure is set up to push the boundaries of innovation while still meeting the rigorous standards through a two-layer approach:
A hardened base of architecture, security controls, and change management procedures.
A rapid development layer that maintains security as a first principle.
Just as important, Model ML empowers teams with tools that allow them to drastically reduce time spent on routine analysis, slide deck review, and report generation as well as perform new kinds of analysis that were previously unfeasible at scale. All within a workspace that feels familiar to any modern office worker.
"Model ML has the potential to entirely transform the financial services industry. By combining a simple, user-friendly interface with a powerful agentic system that integrates seamlessly across all your data sources, Model ML helps you work smarter, uncover deeper insights, and significantly improve efficiency."– Sir Noel Quinn, Group CEO at HSBC
Security at the core
Using a flexible single-tenant deployment model on Azure tailored for the security and operational needs of enterprise customers, Model ML provides each single-tenant client with a dedicated environment based on private Azure Virtual Networks, supplying private, customer-specific instances of databases, in-memory cache, secure Azure Blob Storage, and Azure Service Bus for asynchronous messaging—ensuring total isolation and compliance with SOC 2 and ISO 27001.
Critical enterprise requirements—such as encryption-at-rest, high-availability clusters, automated backups, role-based access control, and private endpoints—are fully implemented across the architecture. Monitoring and analytics are handled through Azure Monitor and Log Analytics, providing real-time insights, alerting, and audit trails for all activity.
With this foundation, onboarding a new customer can be achieved in as little as one hour—from provisioning through to full operational status—while maintaining robust security, compliance, and performance guarantees. With Azure’s deep commitment to enterprise security and innovation, Model ML empowers organizations to adopt state-of-the-art AI at the speed their business demands, without compromising on trust or control.
Azure Kubernetes Service (AKS) and Virtual Machine Scale Sets power the application layer, enabling zero-downtime deployments and seamless horizontal scaling. In so doing, Model ML can grow from serving a single team to an entire division or business vertical without disrupting its existing customers.
Security is at the core of Model ML’s approach to rapid innovation. The development process has been engineered to encourage and enhance innovation within tight security boundaries. This approach has three steps: Guard, Evaluate, and Control.
Guard is focusing on taking any idea from a sandbox to our main codebase. The dev environment has all the same security controls that the production environment has. As a result, nothing can be merged unless it adheres to the standards. Additionally, there is a strict vendor and open source software (OSS) security review process and internal development is always prioritized.
Evaluate is the route of introducing a new idea into production.Every new module or agent update undergoes rigorous internal validation using a proprietary validation framework, ensuring both high accuracy of responses and adherence to compliance requirements before reaching production environments.
Control is what Model ML gives its clients to satisfy their specific requirements. Model ML’s modular architecture and extensive configuration options empower customers to tailor deployments to their unique risk, governance, and data residency policies—whether that means enforcing bring your own key (BYOK) or limiting certain feature access to a subset of users.
Augmenting existing Azure infrastructure
Many Model ML customers also choose Azure as their cloud provider. In these instances, Model ML can deploy directly within their cloud perimeter, unlocking advanced features such as custom agents for internal datasets. Together, these capabilities allow analysts and decision-makers to see their data estates at a scale and speed never possible before, strengthening current processes and unlocking new opportunities.
"Azure gives us the building blocks to isolate every customer environment, run complex workloads at scale, and enforce enterprise-grade controls—all without slowing down the pace of innovation."–Roman Kastusik, Software Engineer at Model ML
Looking ahead with Model ML
Model ML is on a mission to transform the financial services industry by bringing the power of AI to every level of analysis and decision making. Having Microsoft as a partner not only helps address security and compliance requirements—it lays the groundwork for scalable, trust-based AI adoption as Model ML leads the next wave of transformation across financial services and beyond.
Read our blog with Microsoft here:
https://www.microsoft.com/blog/modelml
The rapid pace of innovation in AI is unprecedented—new models, techniques, and tooling emerge at a frenetic pace, demanding constant adaptation and ongoing iteration to stay competitive. That’s why Microsoft is always on the lookout for companies who keep pace with the latest technologies, even in the most challenging markets.
For this exact reason, Microsoft decided to partner with Model ML, an AI native workspace for financial services which not only keeps up with the incredible pace of evolving technology—it drives it, deepening its offering with agentic capabilities and expanding integrations and automation layers with every release.
With a flexible user interface (UI) and advanced agentic system, Model ML enables users to automate thousands of tasks and perform research at unprecedented speeds, across open data sources, financial data providers, internal documents, call transcripts, and more.
In the world of finance, however, mitigating risk is often more important than the maintaining the speed of innovation. Enterprise customers—especially in financial services—have strict requirements around security, privacy, and compliance. That’s why Model ML’s technology infrastructure is set up to push the boundaries of innovation while still meeting the rigorous standards through a two-layer approach:
A hardened base of architecture, security controls, and change management procedures.
A rapid development layer that maintains security as a first principle.
Just as important, Model ML empowers teams with tools that allow them to drastically reduce time spent on routine analysis, slide deck review, and report generation as well as perform new kinds of analysis that were previously unfeasible at scale. All within a workspace that feels familiar to any modern office worker.
"Model ML has the potential to entirely transform the financial services industry. By combining a simple, user-friendly interface with a powerful agentic system that integrates seamlessly across all your data sources, Model ML helps you work smarter, uncover deeper insights, and significantly improve efficiency."– Sir Noel Quinn, Group CEO at HSBC
Security at the core
Using a flexible single-tenant deployment model on Azure tailored for the security and operational needs of enterprise customers, Model ML provides each single-tenant client with a dedicated environment based on private Azure Virtual Networks, supplying private, customer-specific instances of databases, in-memory cache, secure Azure Blob Storage, and Azure Service Bus for asynchronous messaging—ensuring total isolation and compliance with SOC 2 and ISO 27001.
Critical enterprise requirements—such as encryption-at-rest, high-availability clusters, automated backups, role-based access control, and private endpoints—are fully implemented across the architecture. Monitoring and analytics are handled through Azure Monitor and Log Analytics, providing real-time insights, alerting, and audit trails for all activity.
With this foundation, onboarding a new customer can be achieved in as little as one hour—from provisioning through to full operational status—while maintaining robust security, compliance, and performance guarantees. With Azure’s deep commitment to enterprise security and innovation, Model ML empowers organizations to adopt state-of-the-art AI at the speed their business demands, without compromising on trust or control.
Azure Kubernetes Service (AKS) and Virtual Machine Scale Sets power the application layer, enabling zero-downtime deployments and seamless horizontal scaling. In so doing, Model ML can grow from serving a single team to an entire division or business vertical without disrupting its existing customers.
Security is at the core of Model ML’s approach to rapid innovation. The development process has been engineered to encourage and enhance innovation within tight security boundaries. This approach has three steps: Guard, Evaluate, and Control.
Guard is focusing on taking any idea from a sandbox to our main codebase. The dev environment has all the same security controls that the production environment has. As a result, nothing can be merged unless it adheres to the standards. Additionally, there is a strict vendor and open source software (OSS) security review process and internal development is always prioritized.
Evaluate is the route of introducing a new idea into production.Every new module or agent update undergoes rigorous internal validation using a proprietary validation framework, ensuring both high accuracy of responses and adherence to compliance requirements before reaching production environments.
Control is what Model ML gives its clients to satisfy their specific requirements. Model ML’s modular architecture and extensive configuration options empower customers to tailor deployments to their unique risk, governance, and data residency policies—whether that means enforcing bring your own key (BYOK) or limiting certain feature access to a subset of users.
Augmenting existing Azure infrastructure
Many Model ML customers also choose Azure as their cloud provider. In these instances, Model ML can deploy directly within their cloud perimeter, unlocking advanced features such as custom agents for internal datasets. Together, these capabilities allow analysts and decision-makers to see their data estates at a scale and speed never possible before, strengthening current processes and unlocking new opportunities.
"Azure gives us the building blocks to isolate every customer environment, run complex workloads at scale, and enforce enterprise-grade controls—all without slowing down the pace of innovation."–Roman Kastusik, Software Engineer at Model ML
Looking ahead with Model ML
Model ML is on a mission to transform the financial services industry by bringing the power of AI to every level of analysis and decision making. Having Microsoft as a partner not only helps address security and compliance requirements—it lays the groundwork for scalable, trust-based AI adoption as Model ML leads the next wave of transformation across financial services and beyond.
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