Unlocking Immersive Realities Through AI

Using AI to Reimagine the Physical World

Pranab Sinha doesn’t talk about AI as an abstract or distant technology. For the CIO of Matterport, artificial intelligence is the engine powering a fundamental shift: the movement from photographing spaces to understanding them. As he describes it, Matterport is not simply creating immersive models; it is building a spatial intelligence layer capable of interpreting, transforming, and eventually optimizing the physical world.

Sinha frames the company’s mission through a deceptively simple lens: take any real environment and turn it into a high-fidelity digital twin. Matterport is a spatial data company that creates photorealistic, digitally accurate digital twins from raw imagery. Behind that clarity is a deep conviction that AI is not seasoning added to a mature platform. It is the platform, and it is reshaping how industries interact with the spaces they operate.

It’s a shift that mirrors broader AI transformations but plays out in a dimension many enterprises still struggle to digitize: three-dimensional reality.

Why Legacy Approaches to Space Don’t Scale

For decades, businesses treated physical environments as static assets that required manual documentation, in-person inspections, and specialized hardware to analyze. Early Matterport systems reflected that reality. As Sinha puts it, they began as a hardware company, reliant on dedicated cameras to capture complex 3D data.

But this model strained under its own limitations. Specialized capture meant limited accessibility. Manual interpretation meant slow workflows. And the world simply had too much space to digitize using conventional means. “We have more than 10 million spaces, 30 billion square feet of space,” Sinha noted, a scale that only reinforced the need for automation. AI became the inflection point.

Instead of forcing customers into rigid, equipment-heavy processes, Sinha pushed the organization toward a software-first approach. But allowing any device to feed into the system demanded a more intelligent backend. AI had to handle variability in image quality, angles, lighting, and user skill, reconstructing spaces with the same dimensional accuracy once achieved only through specialized lenses.

This wasn’t just a technical upgrade; it reframed the company’s identity from being seen as a hardware company to a software company. The message was clear: legacy playbooks for documenting the physical world were insufficient, and AI was the only path to global scale.

The Case for AI-Driven Spatial Understanding

Once capture became more flexible, the next challenge was interpretation. Digitization without intelligence simply produced high-resolution replicas of physical space, but Sinha wanted more. Businesses needed context, meaning, and insights drawn directly from the environment.

That is where Matterport’s AI engine comes in. The company built models capable of identifying structural elements, recognizing objects, and translating images into measurements and metadata. The digital twin became not just a visualization, but a source of operational truth.

Sinha sees this integration of AI and spatial data as a breakthrough moment across industries: “This kind of photorealistic but immensely detailed space with dimensional accuracy. That’s what allows many of our customers to make business decisions.”

The AI is no longer just reconstructing environments; it is interpreting them. That shift parallels how security leaders view AI in SOC operations: not as a bolt-on detection layer, but as a way to rethink the entire workflow. For Matterport, AI changes the foundation of how space itself is captured, consumed, and controlled.

Reimagining Space With Generative AI

The most transformative development, in Sinha’s view, is what comes after digitization. Matterport is now introducing tools that allow customers to experiment with alternative versions of their environments using generative AI.

This capability fundamentally alters how organizations plan renovations, redesign operational layouts, and model scenarios. Instead of needing CAD tools, architects, or physical mockups, users can prompt the system to propose changes and see them rendered directly into the digital twin.

It’s the spatial equivalent of moving from keyword-based SIEM queries to conversational security operations. Instead of wrestling with technical tools, customers interact with the environment intuitively. Whether it’s a warehouse reconfiguration, a retail redesign, or an insurance assessment, AI becomes the interface between human imagination and spatial possibility.

From Real Estate to the Factory Floor

Matterport’s AI evolution has carried the company far beyond its real estate origins. Sinha described the breadth of today’s footprint: “We work across commercial real estate, facilities, construction, insurance… it’s really broad.”

One area he spoke about with particular energy was manufacturing and industrial operations. These environments are costly to analyze, complex to modify, and often dangerous to navigate. Digitizing them unlocks a new paradigm for training, layout planning, maintenance, and remote oversight.

Sinha explained how customers want to “take the digital representation of the factory floor” and use it to explore configurations or spot issues without walking the floor physically. The digital twin becomes the risk-free sandbox, and the enterprise gains eyes everywhere.

This is the same pattern seen across many industries adopting AI: legacy workflows collapse into simpler, more powerful ones when intelligence is fused into the system. Manual inspections become virtual walkthroughs. Paper floor plans become interactive environments. And decisions that once required teams on the ground can be made from anywhere.

AI as an Internal Force Multiplier

Just as Dover’s Naroola views AI as essential to reducing SOC toil, Sinha sees similar internal benefits for Matterport’s own teams. He describes AI as an augmentation layer: a way to free people from repetitive tasks and allow frontline workers, whether in IT, customer service, or engineering, to focus on higher-impact work.

“AI brings this augmentation capability,” he said. It “removes toil” and accelerates processes that previously slowed teams down.

The company is already using AI to enhance engineering productivity and improve support operations. It’s a recognition that AI isn’t only a product feature, but an organizational capability, one that affects every function.

From Images to Intelligence

The future Sinha outlines is not dominated by hardware advances, but by AI’s ability to understand intent and abstract meaning from physical space.

Matterport’s AI roadmap mirrors the broader shift occurring across enterprise technology: from manual inputs to conversational interaction, from static models to adaptive intelligence, from visual replicas to decision-support systems.

Across every domain he explored, AI emerged not as an enhancement, but as a redefinition. It transforms how organizations perceive the physical world and how they shape it.

Just as Naroola argues that SOCs are becoming analytics engines, Sinha is signaling that enterprises must begin treating their spaces the same way: as data-rich systems waiting to be analyzed, optimized, and transformed.