The financial services sector is one of the most challenging arenas for artificial intelligence (AI) SaaS applications, where innovation must often navigate the complexities of strict data compliance policies and regulations. Financial institutions usually demand complete control over their data and infrastructure. Hyperplane AI, which offered an AI SaaS product for financial institutions, adopted the “Bring Your Own Cloud” (BYOC) deployment model, enabling it to deliver its AI solutions without asking our customers to compromise on their data control needs.
Abhishek Shivanna, a founding engineer at Hyperplane AI, led the development of the company’s AI infrastructure and customer deployments. He pioneered the adoption of the “Bring Your Own Cloud” (BYOC) deployment model at the company, which allowed it to generate multi-million dollar revenues. This article looks into the pivotal role in the success of the BYOC model at Hyperplane AI.
The BYOC model effectively bridged the gap between modern cloud-driven AI innovation and the operational control these institutions demanded. “Financial institutions have highly sensitive customer data,” Abhishek explains. “the BYOC model allowed us to provide AI solutions without asking our customers to change their data governance policies.”. “By enabling clients to run Hyperplane applications on their cloud infrastructure, we allowed clients to maintain full control over the data while giving them access to state-of-the-art AI models from Hyperplane.”
Abhishek adds, “It’s about meeting customers where they are.”
The introduction of the BYOC model brought technical and operational difficulties. At its core was a carefully crafted control plane and data plane architecture. “The control plane is the heart of the operation,” says Abhishek. That’s where we control deployment, orchestrate pipelines, and remotely oversee system health. However, the data plane resides entirely in the customer’s environment, i.e., doing so guarantees that sensitive data is never moved from the customer’s account, thereby mitigating critical privacy and compliance issues.”. This clear division of control over data assets allowed Hyperplane to succeed.
Abhishek adds, “Operating a SaaS product in a client-controlled cloud environment requires meticulous monitoring”. His team built a scalable observability layer that allowed the customer’s internal teams and Hyperplane engineers to monitor systems for failures. “We built a robust observability layer. Metrics and logs generated in the customer’s cloud were streamed in real time to our control plane, where we managed alerts. For customers who wanted more hands-on control, we provided access to these metrics and logs through customized dashboards designed to meet their specific needs. Abhishek notes that this transparency was a crucial element in Hyperplane’s success. Giving financial institutions full control of the operations and data helped their clients build trust in the product and the platform.
Updates to deployed systems without impacting operations were a further challenge that Abhishek’s team had to address. “Our updates were seen as non-disruptive events,” Abhishek explains. “We leveraged CI/CD pipelines using open-source tools like Terraform and Temporal to deliver incremental changes to the data plane without affecting reliability. We worked closely with clients to minimize downtime for critical updates.” This approach improved system reliability, and Hyperplane could innovate and deploy rapidly without disrupting the client’s operations.
Deploying AI models and managing data pipelines in a BYOC environment also requires meticulous planning and execution. Abhishek’s team built a modular framework for model updates that streamlined control of versioned deployment. “We designed our system to roll out updates incrementally,” he shares.
“We built a ‘shadow mode’ to deploy new models alongside existing ones to validate accuracy and compatibility before full rollout.” These systems allowed for uninterrupted client operations while cautiously rolling out models that could have a financial impact on the business. Maintaining data integrity in a distributed BYOC environment was equally critical. “Data integrity is paramount for financial institutions,” Abhishek emphasizes. To address gaps or inaccuracies in data pipelines, the team built automated systems to remotely deploy data backfills or reprocessing tasks directly within the customer’s data plane. “Our pipelines had monitoring built in to detect and flag anomalies early, ensuring proactive issue resolution,” he adds.
“BYOC isn’t just about technology, it’s about building an architecture that aligns seamlessly with customer requirements,” Abhishek explains. “For us, that meant creating a compliant, scalable, and transparent data and AI product.” Abhishek’s work at Hyperplane AI showcases not only technical innovation but also the ability to tackle challenges related to customer acquisition. The BYOC model represents a significant breakthrough in the company’s infrastructure design and is specifically tailored to meet the needs of financial institutions that operate under strict regulations.
Hyperplane AI’s journey demonstrates how businesses in regulated industries can thrive by being customer-focused and demonstrating this with operational flexibility. Abhishek and his team proved that even in complex, high-stakes environments, they can deliver impactful solutions when engineered with purpose. “Ultimately, success comes down to solving meaningful problems for your customers. Do that well, and everything else follows,” Abhishek reflects.
