As artificial intelligence moves rapidly from experimentation to enterprise adoption, businesses are increasingly looking beyond AI models and chatbots toward technologies that can integrate with their existing systems, data and workflows. Think AI (OPC) Private Limited is positioning itself in this emerging space with an AI-as-a-Service and agentic AI platform designed to help organizations turn enterprise data into insights, decisions and automated actions.
In this interview, B. Rohini, Managing Director of Think AI, discusses the company’s vision, the challenges enterprises face in operationalizing AI, and its ambition to build an infrastructure layer connecting enterprise systems with artificial intelligence. She also outlines Think AI’s approach to security, governance, deployment flexibility and its long-term roadmap toward intelligent, AI-powered business processes.
1. Please introduce yourself and tell us about your role at Think AI and the vision behind the company.
I’m B. Rohini, Managing Director of Think AI (OPC) Private Limited. I lead the company from the business and organizational side, with a focus on building Think AI as an enterprise technology company and taking our platform to businesses and organizations across different industries.
The idea behind Think AI originated from a simple but important observation: AI should not remain limited to organizations that have large AI teams, specialized infrastructure, and significant technical resources. Businesses should be able to access AI capabilities and integrate intelligence into their existing systems in a practical, secure, and scalable way.
Think AI is being developed as an AI-as-a-Service and agentic AI technology platform, designed to help organizations transform their data into insights, decisions, and automated actions. The platform is intended to support multiple industries rather than being restricted to a single domain.
The underlying technology and concept have also been filed and published as a patent, which represents an important milestone in our journey and our effort to build proprietary technology rather than simply package existing AI services.
On the technology side, Boddu Rammurty Naidu leads the technical vision and product development of Think AI. He is responsible for the technology architecture, AI platform engineering, product development, integrations, and the evolution of the Think AI technology stack.
Together, our vision is to make AI accessible, deployable, and actionable for organizations of different sizes and across industries moving beyond AI experimentation toward AI that can actually support business decisions and execution.
2. What inspired Think AI, and what gap in the enterprise AI market were you looking to address?
The inspiration came from observing that building an AI demonstration is becoming easier, but taking AI into a real enterprise environment is still difficult.
Businesses already have databases, ERP systems, CRM platforms, APIs, documents, applications and legacy systems. The challenge is connecting AI with all of these systems securely and turning AI capabilities into something that can actually operate inside a business workflow.
Enterprise AI adoption continues to face challenges around data readiness, governance, security, ROI and workflow integration.
That is the gap Think AI is trying to address.
Instead of asking a business to rebuild its technology environment around AI, our objective is:
Existing systems + existing data + Think AI + AI capabilities = intelligent business workflows.
So we see Think AI as an AI infrastructure and orchestration layer between enterprise technology and AI capabilities.
3. Think AI helps businesses across healthcare, finance, manufacturing, retail, banking, pharmaceuticals, and government. What are some of the most important business challenges you aim to solve through AI?
We don’t approach the market by saying that every industry needs the same AI solution.
Instead, we provide a common technology foundation that can support different business problems.
For example:
- Healthcare: patient intelligence, document processing, prediction and decision support.
- Finance and banking: risk analysis, fraud-related analytics, recommendations and automation.
- Retail: customer intelligence, recommendations, demand analysis and forecasting.
- Manufacturing: predictive analytics, operational intelligence and process optimization.
- Pharmaceuticals: document intelligence, research-related analysis and workflow automation.
- Government: secure analytics, document processing and decision-support workflows.
The larger problem we want to solve is the gap between AI capability and business execution.
AI should not stop at generating an answer. It should be able to understand business data, perform the appropriate analysis, provide a useful output and, where authorized, participate in the workflow.
That is why our platform architecture combines capabilities such as AI processing, APIs and connectors, enterprise RAG, agentic orchestration, governance and deployment flexibility.
4. With growing concerns around data privacy, security, explainability, and compliance, how does Think AI help enterprises adopt AI with greater confidence?
Trust has to be designed into the platform rather than added after deployment.
Think AI is designed around several principles:
Privacy: Our platform is designed around a no-business-data-storage approach for processing, where applicable to the deployment architecture.
Security: Authentication, authorization, encryption, API controls and observability are part of the platform architecture.
Deployment control: Organizations can choose different deployment approaches depending on their requirements, including SaaS, private cloud, on-premises, hybrid and air-gapped environments.
Governance: Enterprise AI increasingly involves models, agents, data, applications and automated decisions. Current industry thinking is moving toward continuous technical controls and monitoring rather than governance being treated only as documentation.
Explainability: Our objective is to provide useful confidence and reasoning information with AI outputs wherever the underlying capability supports it, so that businesses can better understand and validate AI-assisted decisions.
Ultimately, we don’t want enterprises to simply use AI. We want them to be able to control how AI is used.
5. What makes Think AI different from other AI platforms, and how do you want businesses to perceive the value of your AI-as-a-Service and agentic AI platform?
I would describe our differentiation in one sentence:
Think AI is designed to connect AI with the real enterprise not just provide another AI model or chatbot.
There are many excellent AI models and AI platforms in the market. We don’t believe the future is about competing only on the model.
The enterprise challenge is increasingly around what happens around the model:
Data → Integration → AI → Orchestration → Decision → Action → Governance
Think AI brings these layers together through an API-first architecture, connectors, SDKs, AI services, enterprise RAG, agentic orchestration, governance and multiple deployment options.
We want customers to perceive Think AI as:
An AI technology layer they can plug into their existing technology environment.
They shouldn’t have to replace their existing applications simply to introduce AI.
And our long-term objective is to move from:
AI that answers → AI that understands → AI that decides → AI that executes under appropriate human and enterprise controls.
6. As businesses move from experimenting with AI to using it for real-world decision-making, what is your vision for Think AI over the next five years?
Over the next five years, I see Think AI evolving from an AI-as-a-Service platform into a broader enterprise AI operating layer.
Today, our focus is on building the foundation:
APIs → AI services → connectors → SDKs → orchestration → security → deployment → governance.
Then we want to progressively expand toward:
Enterprise AI → Agentic workflows → Autonomous business processes → AI-powered decision systems.
Our long-term vision is that a business should be able to connect its existing systems to Think AI, define its business objectives and policies, and allow AI agents to perform appropriate tasks while maintaining security, governance, monitoring and human oversight.
We also want Think AI to remain industry-agnostic at the technology level, while allowing domain-specific intelligence to be built on top of the platform.
The five-year vision is therefore not simply to become another AI application.
It is to build a scalable enterprise AI infrastructure layer that helps organizations move from AI experimentation to production, from production to automation, and ultimately from automation to intelligent decision-making.
The market is already moving in this direction: enterprises are increasingly focused on integration, governance, security and operational readiness as they scale AI beyond pilots.
“Think AI is building the infrastructure layer between enterprise systems and artificial intelligence making AI easier to integrate, secure, deploy, govern and ultimately use for real business decisions.”