What started as an internal tool has today evolved into a new line of business and solutions for ParamNetwork. With AI Studio, ParamNetwork wants to enable organizations to build, deploy, and manage sophisticated agentic AI workflows with enterprise-level security, governance, and scalability. In a conversation with ET Digital, Vaideeswaran Sethuraman, Founder of ParamNetwork, talks about what enterprise AI adoption requires, what AI Studio has to offer and how SMEs can capitalise on the AI trend. Edited excerpts.
The Economic Times (ET): What gap in the enterprise AI market did you identify that led to the creation of AI Studio?
Vaideeswaran Sethuraman (VS): As the founder of Divum, with 15 years of experience and over 1500 projects delivered across mobile, web, and cloud, and of ParamNetwork, with six years of deep work alongside large enterprises and unicorn startups, we recognized the need for an accelerator to build and deploy production-ready AI workflows. AI Studio started as an internal tool to fast-track our GenAI-driven development. Over the past 12 months, it has evolved into a robust, standalone platform; now ready to empower enterprises across the globe.
We identified that enterprise AI solutions need to work seamlessly with existing systems like ERP, SCM, and internal data lakes, going far beyond the general knowledge of trained LLMs. There was a clear gap for a native data-integrated GenAI platform that could connect to domain-specific and enterprise-specific data across SQL, MongoDB, Influx, APIs, and other sources.
At the same time, enterprises still needed consumer AI capabilities to source information from the web and real-time data streams. The market lacked truly integrated platforms - most solutions addressed only one aspect of the enterprise AI puzzle rather than providing a comprehensive approach
We saw a critical need for enterprise-class data decentralization that ensures AI has the right access, at the right time, for the right user - maintaining security while enabling powerful insights
These gaps led us to create AI Studio as an integrated platform that connects enterprise data systems and real-time streams while maintaining strict security through decentralized data access controls.
ET: How does AI Studio differentiate itself from other GenAI platforms currently available to enterprises?
VS: First and foremost, AI Studio is designed from first principles for enterprise deployment patterns, with built-in governance, security, and integration capabilities that many platforms only add as afterthoughts.
It’s genuinely model-agnostic, supporting any LLM rather than locking customers into specific providers, giving enterprises both flexibility and cost control. Our platform integrates three critical technologies—L RAG, Quantum Minds, and MCP Streams—creating a complete solution rather than requiring multiple disconnected tools.
Finally, it’s built on our proprietary blockchain foundation, creating unprecedented data security through a decentralized data lake architecture that ensures AI agents only access appropriate data collections
ET: Let’s talk about the proprietary stack—L RAG (Lightning RAG), Quantum Minds, and MCP Streams. Could you elaborate on the unique role each one plays in enabling agentic AI workflows?
VS: Each component of our proprietary stack serves a specific purpose in the agentic AI workflow. L RAG (Lightning RAG) is our advanced retrieval augmented generation engine that processes structured, semi-structured, and unstructured data simultaneously. Unlike conventional RAG implementations that struggle with mixed data types, L RAG optimizes processing across your entire data landscape, making it ideal for enterprises with diverse data sources.
Quantum Minds is our low-code workflow builder that democratizes AI development. It provides pre-built agentic operators like textToSQL, pandasAIdf, and MCPClients that business users can assemble visually without deep technical expertise, dramatically accelerating deployment. Eliminating the need for review any agentic Python code, unlike other consumer agentic AI platforms.
MCP Streams is our implementation of Model Connect Protocol that connects to various data streams and enterprise systems. It functions like ‘Zapier++ for GenAI,’ creating seamless connections between AI workflows and business systems while maintaining model flexibility.
ET: What specific security and governance features are built into AI Studio to meet regulatory standards like GDPR?
VS: Security and governance are foundational to AI Studio, not add-ons. Our blockchain-based decentralized data lake ensures that AI agents can only access appropriate data collections, creating granular access control.
We have implemented comprehensive audit logs that track every AI interaction, decision, and data access, creating complete traceability throughout the AI lifecycle. For GDPR compliance, our Quantum Minds workflow tool enables “AI ETL” processes that can mask personal identifiers and implement data minimization before the information is ever used for insights or automation. Our platform features robust explainability tools that help compliance teams understand how AI recommendations are generated, addressing the “black box” concern that often blocks enterprise AI adoption in regulated industries.
ET: Can you talk about your cloud-agnostic deployment capabilities and why that’s important for enterprises today?
VS: Our cloud-agnostic approach gives enterprises complete deployment flexibility; public cloud, virtual private cloud, or fully on-premises. This is crucial for three reasons. First, data sovereignty requirements vary by region and industry, and many enterprises need to keep sensitive data within specific geographical boundaries.
Second, many organizations have existing cloud investments they need to leverage rather than being forced into new providers. Third, highly regulated industries often require air-gapped deployments for their most sensitive workloads. Beyond infrastructure, we offer LLM flexibility, allowing enterprises to use any inference engines like fireworks, Cerebras, Groq, GCP Vertex, AWS or privately hosted public AI models; whether it’s Azure-hosted GPT-4o, self-hosted Claude, Google Vertex-hosted Claude, or other options.
ET: How does AI Studio achieve the claimed 40–60% reduction in computational resource usage? Are there specific optimization strategies at play?
VS: The resource reduction comes from several practical optimization strategies we’ve implemented based on real-world enterprise usage patterns. First, we enable right-sizing of LLMs for specific tasks - not every operation requires the most powerful model, and we help enterprises match the appropriate model to each workload. Second, our platform uses LLMs intelligently during development (like for TextToSQL conversions) but then deploys with the generated SQL or Python code for repeated production runs in BI or automation workflows. Third, we’ve designed our architecture to use LLMs only where AI thinking is truly in the critical path - many operations can be handled more efficiently through optimized traditional computing. Finally, our L RAG technology implements intelligent chunking and embedding methods that minimize redundant processing of enterprise data.
ET: Who would be the typical customer for AI Studio? Can small businesses and SMBs also benefit from it? What would the costs be?
VS: Our primary focus is mid to large enterprises with complex data environments and mission-critical use cases across industries. That said, we’ve designed AI Studio with modular licensing that makes it accessible to growing companies. Our entry-level configurations start at $25,000 annually, with enterprise implementations typically ranging from $100,000 to $500,000 depending on scale and specific requirements.
For smaller businesses, we’re exploring partnership models with industry-specific solution providers who can leverage our platform to create tailored offerings at more accessible price points. Our goal is to make enterprise-grade AI accessible to organizations of all sizes while ensuring the level of support and security that production deployments require.
ET: What’s on the horizon for AI Studio and ParamNetwork—are there new capabilities or industries you’re targeting next?
VS: We have several key initiatives on our roadmap that build on our current foundation while addressing emerging enterprise needs. We are strengthening our structured data analysis capabilities, particularly for time series databases which are critical for operational technology and IoT use cases.
We are extending Quantum Minds with MindChats and MindAutomation extensions to make it even easier to build GenAI chatbots and next-generation RPA applications—imagine systems that can review bids, apply selection logic, auto-negotiate, and create purchase orders automatically. We are also developing high-performance AI ETL capabilities to address the growing need for intelligent data transformation at enterprise scale. Multi-enterprise collaboration is our unique strength, and we’ll be deepening integration with the ⦃param⦄ Network blockchain platform to enable secure, verifiable AI workflows across organizational boundaries.
The Economic Times (ET): What gap in the enterprise AI market did you identify that led to the creation of AI Studio?
Vaideeswaran Sethuraman (VS): As the founder of Divum, with 15 years of experience and over 1500 projects delivered across mobile, web, and cloud, and of ParamNetwork, with six years of deep work alongside large enterprises and unicorn startups, we recognized the need for an accelerator to build and deploy production-ready AI workflows. AI Studio started as an internal tool to fast-track our GenAI-driven development. Over the past 12 months, it has evolved into a robust, standalone platform; now ready to empower enterprises across the globe.
We identified that enterprise AI solutions need to work seamlessly with existing systems like ERP, SCM, and internal data lakes, going far beyond the general knowledge of trained LLMs. There was a clear gap for a native data-integrated GenAI platform that could connect to domain-specific and enterprise-specific data across SQL, MongoDB, Influx, APIs, and other sources.
At the same time, enterprises still needed consumer AI capabilities to source information from the web and real-time data streams. The market lacked truly integrated platforms - most solutions addressed only one aspect of the enterprise AI puzzle rather than providing a comprehensive approach
We saw a critical need for enterprise-class data decentralization that ensures AI has the right access, at the right time, for the right user - maintaining security while enabling powerful insights
These gaps led us to create AI Studio as an integrated platform that connects enterprise data systems and real-time streams while maintaining strict security through decentralized data access controls.
ET: How does AI Studio differentiate itself from other GenAI platforms currently available to enterprises?
VS: First and foremost, AI Studio is designed from first principles for enterprise deployment patterns, with built-in governance, security, and integration capabilities that many platforms only add as afterthoughts.
It’s genuinely model-agnostic, supporting any LLM rather than locking customers into specific providers, giving enterprises both flexibility and cost control. Our platform integrates three critical technologies—L RAG, Quantum Minds, and MCP Streams—creating a complete solution rather than requiring multiple disconnected tools.
Finally, it’s built on our proprietary blockchain foundation, creating unprecedented data security through a decentralized data lake architecture that ensures AI agents only access appropriate data collections
ET: Let’s talk about the proprietary stack—L RAG (Lightning RAG), Quantum Minds, and MCP Streams. Could you elaborate on the unique role each one plays in enabling agentic AI workflows?
VS: Each component of our proprietary stack serves a specific purpose in the agentic AI workflow. L RAG (Lightning RAG) is our advanced retrieval augmented generation engine that processes structured, semi-structured, and unstructured data simultaneously. Unlike conventional RAG implementations that struggle with mixed data types, L RAG optimizes processing across your entire data landscape, making it ideal for enterprises with diverse data sources.
Quantum Minds is our low-code workflow builder that democratizes AI development. It provides pre-built agentic operators like textToSQL, pandasAIdf, and MCPClients that business users can assemble visually without deep technical expertise, dramatically accelerating deployment. Eliminating the need for review any agentic Python code, unlike other consumer agentic AI platforms.
MCP Streams is our implementation of Model Connect Protocol that connects to various data streams and enterprise systems. It functions like ‘Zapier++ for GenAI,’ creating seamless connections between AI workflows and business systems while maintaining model flexibility.
ET: What specific security and governance features are built into AI Studio to meet regulatory standards like GDPR?
VS: Security and governance are foundational to AI Studio, not add-ons. Our blockchain-based decentralized data lake ensures that AI agents can only access appropriate data collections, creating granular access control.
We have implemented comprehensive audit logs that track every AI interaction, decision, and data access, creating complete traceability throughout the AI lifecycle. For GDPR compliance, our Quantum Minds workflow tool enables “AI ETL” processes that can mask personal identifiers and implement data minimization before the information is ever used for insights or automation. Our platform features robust explainability tools that help compliance teams understand how AI recommendations are generated, addressing the “black box” concern that often blocks enterprise AI adoption in regulated industries.
ET: Can you talk about your cloud-agnostic deployment capabilities and why that’s important for enterprises today?
VS: Our cloud-agnostic approach gives enterprises complete deployment flexibility; public cloud, virtual private cloud, or fully on-premises. This is crucial for three reasons. First, data sovereignty requirements vary by region and industry, and many enterprises need to keep sensitive data within specific geographical boundaries.
Second, many organizations have existing cloud investments they need to leverage rather than being forced into new providers. Third, highly regulated industries often require air-gapped deployments for their most sensitive workloads. Beyond infrastructure, we offer LLM flexibility, allowing enterprises to use any inference engines like fireworks, Cerebras, Groq, GCP Vertex, AWS or privately hosted public AI models; whether it’s Azure-hosted GPT-4o, self-hosted Claude, Google Vertex-hosted Claude, or other options.
ET: How does AI Studio achieve the claimed 40–60% reduction in computational resource usage? Are there specific optimization strategies at play?
VS: The resource reduction comes from several practical optimization strategies we’ve implemented based on real-world enterprise usage patterns. First, we enable right-sizing of LLMs for specific tasks - not every operation requires the most powerful model, and we help enterprises match the appropriate model to each workload. Second, our platform uses LLMs intelligently during development (like for TextToSQL conversions) but then deploys with the generated SQL or Python code for repeated production runs in BI or automation workflows. Third, we’ve designed our architecture to use LLMs only where AI thinking is truly in the critical path - many operations can be handled more efficiently through optimized traditional computing. Finally, our L RAG technology implements intelligent chunking and embedding methods that minimize redundant processing of enterprise data.
ET: Who would be the typical customer for AI Studio? Can small businesses and SMBs also benefit from it? What would the costs be?
VS: Our primary focus is mid to large enterprises with complex data environments and mission-critical use cases across industries. That said, we’ve designed AI Studio with modular licensing that makes it accessible to growing companies. Our entry-level configurations start at $25,000 annually, with enterprise implementations typically ranging from $100,000 to $500,000 depending on scale and specific requirements.
For smaller businesses, we’re exploring partnership models with industry-specific solution providers who can leverage our platform to create tailored offerings at more accessible price points. Our goal is to make enterprise-grade AI accessible to organizations of all sizes while ensuring the level of support and security that production deployments require.
ET: What’s on the horizon for AI Studio and ParamNetwork—are there new capabilities or industries you’re targeting next?
VS: We have several key initiatives on our roadmap that build on our current foundation while addressing emerging enterprise needs. We are strengthening our structured data analysis capabilities, particularly for time series databases which are critical for operational technology and IoT use cases.
We are extending Quantum Minds with MindChats and MindAutomation extensions to make it even easier to build GenAI chatbots and next-generation RPA applications—imagine systems that can review bids, apply selection logic, auto-negotiate, and create purchase orders automatically. We are also developing high-performance AI ETL capabilities to address the growing need for intelligent data transformation at enterprise scale. Multi-enterprise collaboration is our unique strength, and we’ll be deepening integration with the ⦃param⦄ Network blockchain platform to enable secure, verifiable AI workflows across organizational boundaries.
