Kivo is an AI-native technology company that redefines business automation and real-world computer vision. Through its flagship platform, Kivo.ai, the company deploys intelligent AI agents that orchestrate workflows across HR, CRM, finance, and project management. These agents eliminate repetitive manual processes; in their place, they deliver predictive intelligence, contextual insights, and unified data visibility. As a result, enterprises move from fragmented operations to integrated, data-driven execution.
At the same time, Kivo extends its capabilities into the physical world through its Kivo Eye initiative. The company converts standard CCTV and IP cameras into advanced AI vision systems. These systems perform real-time object counting, motion tracking, defect detection, and heatmap analytics with precision and scale. Consequently, industries such as manufacturing, logistics, retail, and healthcare gain continuous operational intelligence from existing infrastructure. This shift reduces errors, lowers operational overhead, and strengthens safety and situational awareness across environments.
In an exclusive conversation with The Interview World at the India AI Impact Expo 2026, Ishank Gupta, Founder and CEO of Kivo AI, articulates the company’s strategic direction. He details the advanced capabilities engineered beyond the core AI platform and explains how Kivo designs and optimizes model training pipelines to achieve cost efficiency without compromising performance. Furthermore, he outlines the company’s data security architecture, presents a five-year strategic roadmap, and shares insights into its enterprise-grade clientele. The following are the key takeaways from this in-depth discussion.
Q: What differentiated, breakthrough capabilities have you engineered beyond the base AI platform, and what measurable impact do they deliver?
A: Currently, we are advancing our computer vision capabilities. Specifically, we deploy AI-driven models that overlay an intelligence layer onto existing CCTV infrastructure. Rather than replacing hardware, we augment it. In doing so, we convert conventional surveillance systems into real-time decision engines.
Consider a standard IP-based CCTV camera already installed at your facility. We integrate that camera with our edge device, which operates as a CPU-powered computing unit. We then install our proprietary software directly on the edge layer. This architecture enables on-site processing, minimizes latency, and eliminates the need for disruptive infrastructure changes. Consequently, organizations unlock advanced capabilities using their existing setup.
For example, we can enable automated attendance management. The system identifies employees as they enter and exit the premises and records attendance in real time. Similarly, we can enforce standard operating procedures (SOPs). The system continuously monitors whether each procedural step is executed as mandated and flags deviations instantly.
In addition, we support automated quality assessment. The system evaluates outputs against predefined benchmarks and determines whether they meet the required standards. Finally, we design ROI-driven implementations. In such cases, you define the business objective, and we architect a tailored AI solution around that specific use case. This ensures that every deployment aligns directly with measurable business outcomes.
Q: Given the high cost of AI model training and inference, how do you design and optimize your training pipelines to ensure cost-efficiency without compromising model performance?
A: We maintain a highly skilled in-house R&D team that continuously designs, trains, and refines our AI models. The team manages the entire model development lifecycle internally, thereby ensuring technical depth and quality control at every stage.
We conduct model training on our on-premises CPU-based infrastructure. Once the training phase concludes and the model meets our performance benchmarks, we extract the optimized model artifact and deploy it directly to our edge devices. This structured pipeline, from controlled training to edge deployment, creates a disciplined, well-defined process. As a result, we ensure consistency, operational efficiency, and seamless production rollout.
Q: What data security and governance mechanisms are embedded within your AI platform to ensure confidentiality, integrity, and regulatory compliance?
A: This architecture defines the strength of our technology. We do not rely on cloud-based software installations. Instead, we deploy and operate the entire system on your premises.
In practical terms, this means we do not transmit your data over the internet or move it outside your infrastructure, unless you explicitly authorize us to do so. All processing occurs locally through our on-premise devices. Consequently, sensitive information remains within your controlled environment at all times.
By eliminating external data dependencies, we significantly reduce exposure risks. Ultimately, this on-premise deployment model reinforces data sovereignty and ensures the highest standards of client data security.
Q: Over the next five years, what milestones define your strategic success, and how are Indian clients leveraging your platform?
A: We believe data acquisition remains the most critical bottleneck in AI. Without reliable, high-quality data, intelligent decision-making becomes constrained. Therefore, we focus on simplifying and systematizing the data collection process.
Cameras provide a practical and scalable solution. Most organizations already operate CCTV infrastructure. By activating these existing assets, we transform them into continuous data-generation systems. This approach removes complexity, reduces incremental investment, and accelerates deployment.
Accordingly, we will continue to refine and expand this strategy over the next four to five years. We view it as a long-term growth vector. Moreover, we are already executing at scale. We have deployed our solution across multiple industries, including large, publicly listed enterprises.
Data privacy consistently emerges as a primary concern for these organizations. However, our on-premise deployment model addresses this directly. Once clients understand that their data never leaves their controlled environment, confidence increases significantly.
In parallel, we are witnessing strong traction at this AI event. Our live demonstration reinforces credibility by showcasing real-time capabilities rather than theoretical claims. As a result, the market response has been both immediate and substantive.
Q: Can you provide data points that reflect the depth and growth of your enterprise-class customer portfolio?
A: As noted earlier, we have already deployed our solution across more than ten large enterprises. These organizations are publicly listed companies, each with a market valuation exceeding ₹10,000 crore. In other words, they operate at significant scale and under rigorous governance standards.
Our successful implementations within such enterprises underscore both the robustness of our technology and the confidence that large, high-value clients place in our platform.
