DeepKlarity Labs

Where we go deep.

Labs isn't R&D in the corporate sense. It's us tinkering with things that fascinate us — exploring hard problems before they become client requirements. What we learn here shapes the systems we build.

Physical intelligence

Robotics × AI

Teaching machines to understand and act in physical environments. Edge inference, real-time adaptation, safety-critical systems.

Edge inference and sensor fusionVision-language control loopsAdaptive motion through LLM reasoningReal-time safety monitoring
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Infrastructure for autonomous agents

Agent OS

Building the orchestration layer for AI agents — browser control, desktop automation, inter-agent communication, and reliable tool use.

Browser automation primitivesComputer use (keyboard, mouse, screen)Inter-agent communication protocolsMCP server implementations
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Spatial computing meets intelligence

VR / XR × AI

Exploring AI-native interfaces for immersive environments. Generative 3D, spatial understanding, voice-first interaction, and intelligent virtual worlds.

Generative 3D from text and imagesSpatial reasoning and scene understandingVoice and gesture-first interfacesAI-driven procedural environments
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How we think about research

01

Learn by building

We don't just read papers. We implement, break things, and figure out what actually works. The failures are as valuable as the successes.

02

Share what we learn

We learned from others who shared. Insights, code, and lessons get documented and published. No point hoarding knowledge.

03

No timelines

Labs work moves at its own pace. Some experiments take weeks, some take months. The goal is understanding, not shipping.

Interested in what we're building?

We're always happy to talk about the problems we're exploring — whether you have a related challenge or just find this stuff interesting.

Get in touch →