IndoAI Technologies is redefining the landscape of Edge AI with its innovative “Appization” model, transforming traditional cameras into modular, AI-powered platforms. By enabling users to dynamically install and swap AI models, much like apps on a smartphone, the company is bridging the gap between cutting-edge computer vision and real-world usability.
At the core of this vision lies a robust ecosystem that combines proprietary edge orchestration, developer tools, and a marketplace for third-party AI solutions, positioning IndoAI as a foundational platform for next-gen intelligent devices.
Co-Founder & Director,
IndoAI Technologies Pvt Ltd
In an exclusive interview with Tech Disruptor, Rashmi Kulkarni, Co-Founder & Director, IndoAI Technologies Pvt Ltd, shares how IndoAI is not just building smarter cameras but shaping the future of decentralized, adaptive AI deployment. She also delves into the technical and strategic decisions driving the company’s mission to become the “Android of Edge AI.” Edited excerpts are below:
Tech Disruptor: Regarding IndoAI’s long-term strategy, are you aiming to establish a licensing model to become a foundational platform like ‘Android’ for edge AI cameras, or will the primary monetization come from your own high-value AI models?
Rashmi Kulkarni Our long-term strategy is to position itself as the foundational OS platform for edge AI cameras, similar to how Android serves smartphones. This approach, what we call Appization, enables developers to deploy AI models like apps on our devices. While we do build and sell some high-performance in-house models (e.g., for face recognition, license plate detection, gesture-based distress signals), our primary focus is on enabling a developer-first, model-agnostic ecosystem. Licensing the platform, APIs, and distribution via our AI model marketplace will form a major monetization channel going forward.
Tech Disruptor: To potentially broaden adoption, has IndoAI considered open-sourcing its edge-AI framework, or is this core IP essential to your competitive advantage?
Rashmi Kulkarni: We’ve carefully considered open-sourcing. While some peripheral tools may be opened up to foster collaboration, our core Appization IP such as the deployment layer, orchestration runtime, and model sandboxing framework remains proprietary for now. It’s the heart of our differentiation and critical to maintaining performance, safety, and data privacy standards. That said, we’re committed to open APIs, SDKs, and developer tools to allow third-party contributions and integrations.
Tech Disruptor: From a UI/UX perspective, what do you see as the most significant design hurdle in making edge AI accessible to individuals without a strong technical background? What are your plans for simplifying the user interface to facilitate the deployment of custom AI models for this audience?
Rashmi Kulkarni: Our biggest UI/UX challenge stems from the fact that the AI camera itself has no screen interface. All interactions—model deployment, performance monitoring, and configuration—must happen remotely through the IndoAI mobile app. This puts an immense design burden on making the app intuitive, powerful, and accessible even for non-technical users.
Key challenges and our approach:
● Model Management: Users can install/uninstall AI models directly on the camera through the app. We’ve built a drag-and-drop style experience and a model preview system so users can see what each model does before deploying it.
● Compute Monitoring: Since cameras have limited edge compute, we’ve added real-time utilization dashboards showing CPU/GPU loads and memory usage, helping users understand model efficiency.
● Notification Configuration: Users can configure custom alert rules and push notifications via app—e.g., alert when a stranger is detected or when motion is spotted at odd hours.
● Personalization for Recognition: Users can upload known face images through the app so that the AI model can flag strangers automatically. This is used heavily in homes, offices, and hostels.
● Voice-Guided Setup: To support non-technical users, we’re adding a voice-guided assistant to help with onboarding, model selection, and problem troubleshooting in regional languages.
Tech Disruptor: Given that your cameras process biometric data, could you elaborate on how IndoAI ensures compliance with India’s Digital Personal Data Protection (DPDP) Act?
Rashmi Kulkarni: We’ve designed IndoAI from the ground up with privacy-by-design principles. Key measures include:
● On-device processing of biometric and visual data to avoid unnecessary cloud transmission.
● User consent workflows for identity and facial recognition features.
● Encryption at rest and in transit, with audit trails and access controls.
● Integration of role-based access controls (RBAC) and anonymized data logs for shared systems.
We are actively aligning our compliance protocols with DPDP, and have retained legal counsel to ensure readiness for any future certification frameworks under Indian law.
Tech Disruptor: Referring ‘AI for Bharat’, how does IndoAI approach the localization of AI models to effectively address India’s diverse needs and contexts?
Rashmi Kulkarni: Localization is essential to our business strategy partly due to compulsion & partly due to gain flexibility. We are in the early stage so our production quantity is limited and cottage at the moment hence our camera hardware assembly is local. We procure most parts locally apart from processors. The casing is manufactured by us.
We develop and fine-tune AI models for contextual relevance. For instance:
● Face recognition models are trained with diverse Indian datasets across lighting, skin tones, and attire variations.
● Our distress gesture models account for non-verbal cues common in Indian contexts.
● Our voice and gesture-based UI supports multi-lingual prompts including Hindi and other regional languages.
We’re also collaborating with local academic institutions and community partners to co-create datasets from Tier 2/3 cities.
Tech Disruptor: Considering your involvement in smart city initiatives, are you currently pursuing integrations with municipal IoT infrastructure, such as traffic management systems or emergency services, to expand beyond the capabilities of standalone cameras?
Rashmi Kulkarni: Yes. We have few contracts from municipalities & we will announce once formalities are done. Also we’re in active discussions with multiple municipal corporations and urban development agencies.
Our roadmap is packed with impactful innovations. We’re integrating AI-driven solutions for critical urban challenges, like congestion control, theft prevention (pickpocketing and chain snatching), and molestation detection. We’re also rolling out plug-ins for garbage monitoring, public safety (including distress gesture alerts), and enhanced women’s safety surveillance.
Looking ahead, we plan to introduce emergency service routing powered by real-time visual intelligence and advanced anomaly detection, ensuring smarter, faster responses to urban safety threats. Our cameras can serve as edge compute nodes in an IoT mesh, communicating insights across city systems without needing high bandwidth or central processing.
Tech Disruptor: IndoAI’s ‘Appization’ model allows users to easily exchange AI models, similar to smartphone applications. What are the future directions for this concept? Are you considering building an open ecosystem for third-party AI developers, or will you primarily focus on vertical-specific partnerships to expand the platform?
Rashmi Kulkarni: We see Appization evolving in both directions. In the short term, we’re building vertical-specific partnerships—e.g., for retail theft detection, school attendance, and industrial safety. This helps us validate the ecosystem and maintain quality control.
The future is open, and we’re building an AI Model Marketplace to accelerate innovation. Third-party developers will be able to upload, test, and publish their models, monetizing them through pay-per-use or licensing models. By leveraging our runtime and evaluation tools, creators can seamlessly deploy their solutions.
In essence, we want IndoAI to become the “Play Store for Vision AI”, enabling rapid AI innovation on edge devices.
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