Buildroid secures $2m to target Gulf construction boom

AI robotics startup launches block-laying system

Nov 24, 2025

#UAE #funding– UAE-based AI robotics startup Buildroid has emerged from stealth with a $2 million pre-seed investment led by US venture investor Tim Draper, positioning the company to accelerate automation across the region’s buoyant construction sector. The new capital will fund pilot expansion, enhance autonomous robotic capabilities and support early commercial deployments. Founded in 2025, the company has launched its first block-laying robot built on a BIM-to-BUILD simulation pipeline to address labour shortages and improve productivity in a market valued at $42.75 billion.

SO WHAT? – Buildroid is one of a growing number of AI-powered startups getting funded to target a lucrative Middle East’s construction sector. AI procurement platform Builtop and construction management platform TruBuild also announced successful funding rounds this year. Investors are looking to capitalise on the sector’s demand for automation and labour-saving technology. With its large greenfield developments and iconic big budget projects, the Middle East provides an interesting testing ground to prove construction automation concepts. Buildroid’s integration with BIM (Building Information Modeling) systems means that once developed, its robotics systems and use cases could be replicated anywhere else in the world.

Here are some key points about this fund raise:

Buildroid AI, a UAE-based AI robotics startup founded in 2025 by industry entrepreneurs Slava Solonitsyn and Anton Glance, raised $2 million in pre-seed funding led by Tim Draper, a Silicon Valley-based investor known for early investments in major global technology firms.
The company emerged from stealth by unveiling its first block-laying robot, created using a BIM-to-BUILD simulation pipeline designed to connect digital design models with on-site robotic execution through AI-driven Digital Twin environments.
Commercial deployments are scheduled to begin in Q2 2026, with Buildroid’s business model positioning itself to share in cost savings realised from contractor partnerships
Buildroid’s platform integrates specialised and general-purpose robots directly into contractor workflows, aiming to automate the labour-intensive partition wall segment before expanding into interior fit-out and broader construction tasks.
Current pilots are underway on live job sites in the UAE, where the robot is supported by Autonomous Mobile Robots designed to deliver materials and remove the need for manual handling.
The company reports productivity gains of up to 10x and cost savings of up to 4x compared with traditional manual blockwork, based on early simulation and field tests.
Buildroid plans to open its platform to third-party robot vendors, operators and contractors, creating a construction-specific orchestration environment similar to an ERP for robotics.
The startup’s simulation-first methodology aims to de-risk deployment for developers and general contractors by validating robotic workflows before they are implemented on active job sites.
The MENA construction sector is expected to reach $401.2 billion by 2030, increasing demand for automation platforms capable of coordinating multiple types of construction robots.

ZOOM OUT - The GCC (Guf Cooperation Council) construction sector is in a sustained multi-year expansion, underpinned by a multi-trillion-dollar project pipeline and steady mid-single-digit growth forecasts through 2030 and beyond. Market estimates place GCC construction activity at between $170–180 billion in 2025, with projections rising to $214–219 billion by 2030–2032. Saudi Arabia remains the main engine, driven by a #2.1 trillion Vision 2030 programme and a $1.5 trillion pipeline of unawarded projects, while the UAE continues to deliver strong contract awards concentrated in Abu Dhabi and Dubai. This scale, combined with rising labour needs and a shift towards BIM-led and modular methods, is accelerating demand for robotics and automation across GCC building sites.

[Written and edited with the assistance of AI]

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