Collov Labs Raises $23M Series A to Bet on Visual AI as the Next Interface

Collov Labs has raised a $23 million Series A and launched a new research lab aimed at advancing visual AI systems, signaling a broader shift in how artificial intelligence may evolve beyond text-based interaction.

The round, backed by Brightway Future Capital, Taihill Venture, and Mindworks Capital, will fund the development of systems designed to interpret images and camera input, with the goal of enabling AI to understand and act on the physical world.

A Shift Away From Chat-Based AI

Much of today’s AI adoption has centered around chat interfaces. Collov Labs is building around a different premise: that visual input will become the primary way people interact with AI.

Instead of prompting systems with text, the company is focused on enabling users to point a camera at a scene and have AI interpret context, reason about what it sees, and assist with real-world actions. This reflects a broader industry transition toward multimodal AI, where systems combine vision, language, and reasoning into a unified experience.

The idea is not entirely new, but recent advances in compute, models, and on-device processing are making it increasingly practical.

Building Toward Real-World AI Interaction

Collov Labs is developing systems that combine diffusion models, spatial reasoning, and agentic workflows. The goal is to move beyond static image recognition toward systems that can understand relationships within a scene and execute multi-step actions.

Thisection aligns with a growing push toward AI systems that interact with physical environments, particularly as hardware evolves to support real-time processing and persistent context.

The company’s background reflects this focus. Its team has experience in multimodal AI, large-scale recommendation systems, and applied machine learning across both academia and industry.

From Design Tools to a Broader AI Layer

Collov’s existing products, including its AI-powered design tools, provide a glimpse into how these systems function in practice. The company originally gained traction in areas like interior design and visual content generation, where AI can interpret spatial layouts and generate realistic outputs.

Earlier iterations of the business focused on AI-driven design platforms and automation tools, an approach that has already seen commercial traction across real estate, retail, and e-commerce use cases .

These products now act as a feedback loop, supplying real-world data that helps improve the company’s models and refine how they understand visual environments.

Why Visual AI May Expand Adoption

One of the underlying assumptions behind Collov Labs’ strategy is that text-based interfaces have limited reach. While chatbots have driven awareness, most of the global population has yet to meaningfully engage with AI tools.

Visual interfaces, by contrast, are inherently more intuitive. The shift mirrors earlier transitions in computing, where graphical interfaces made systems accessible to a broader audience beyond technical users.

If successful, this approach could lower the barrier to entry for AI adoption and expand its use across industries where visual context is essential, including retail, design, logistics, and field operations.

The of Hardware and On-Device AI

Advances in hardware are a key enabler behind the rise of visual AI. As processing capabilities improve on smartphones, wearables, and specialized chips, more of the work required to interpret images and video can happen locally in real time. This reduces latency and allows systems to respond instantly to what a user is seeing, rather than relying entirely on cloud-based processing.

This shift also changes how AI is delivered. Instead of existing primarily as standalone applications, visual intelligence can become embedded within devices themselves, operating continuously in the background. That opens the door to more context-aware interactions, but also raises practical concerns around accuracy, reliability, and how these systems behave in unpredictable real-world environments.

Broader Implications for AI Interaction

The move toward visual AI suggests a gradual shift in human-computer interaction. Systems that can interpret scenes and spatial relationships may reduce the need for structured inputs, making AI more accessible to users who are less comfortable with text-based tools.

At the same time, the complexity of real-world environments introduces new challenges. Misinterpreting a scene or missing key context can lead to incorrect outputs, and the consequences of those errors become more significant as AI moves closer to decision-makings.

Rather than replacing existing interfaces, visual AI is more likely to evolve alongside them, adding another layer of interaction. Over time, this could lead to a more integrated experience where AI responds to context as much as it does to explicit instructions.