Advancing How Humans

capture sites  reconstruct in 3D verify progress detect defects reduce rework compare to plans build digital twins turn video into BIM coordinate crews improve safety document compliance deliver faster

with Spatial AI




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About SpatioSense

Spatial intelligence for the field, built by the people who study and ship it.

We combine Robotics PhDs, applied computer vision, and startup grit to remove the friction between site reality and project delivery.

SpatioSense is a spatial AI software platform for construction teams. We help owners, general contractors, and consultants capture job sites using smartphones and turn that data into 3D models, defect detection, and progress verification—reducing rework, site visits, and close-out time.

Photogrammetry interior capture
Photogrammetry 3D SLAM Computer Vision QA Field-ready

Trusted spatial AI for construction

Document, detect, and deliver with a single site capture.

SpatioSense turns quick smartphone captures into actionable, spatially-aware intelligence for owners, GCs, and consultants.

Video-to-3D in hours AI defect detection Spatial tagging Progress verification Connected to your stack

Field-first capture

Walk the site once. Our capture guidance reduces reshoots and ensures coverage without specialized scanners.

0 Faster site walks vs. laser scans

Defect intelligence

Automatically flag defects, code issues, and mismatches against plans. Each finding is pinned in 3D space.

0 Detection precision on pilot sites

Workflows that ship

Generate close-out lists, sequence site visits, and push updates into Procore, BIM 360, or your preferred tools.

0 More issues closed per visit

How it works

Complete Site Oversight in Three Simple Steps

No lidar vans or week-long turnarounds. Just fast, high-fidelity spatial data that is ready to act.

1

Capture

Record with your phone. Forget expensive laser scanners. Walk your site, upload to SpatioSense, and we handle the rest.

2

Analyze

Our engine transforms video into a high-fidelity 3D map. AI scans the environment to detect, log, and spatially tag defects.

3

Resolve

Track close-out progress, create site-visit plans, and view analytics. Data pushes directly into your construction stack.

Use case

How SpatioSense fits into real construction workflows.

A concise, plain-language snapshot for evaluators and decision makers.

Where we fit

Construction tech for AEC teams.

Spatial AI SaaS that turns site captures into usable 3D evidence.

Who uses it

Owners, GCs, and consultants.

Teams that need fast, shared visibility across active job sites.

Why it matters

Less rework, fewer site walks.

Replace slow QA/QC with actionable 3D proof tied to real issues.

Current stage

Private beta with pilot customers.

Onboarding new teams with guided demos and active projects.

High-fidelity 3D spatial view interface
High-fidelity 3D spatial view
Detected defects overview dashboard
Detected defects overview
Detail defect views synced to external system
Defect details synced to external systems
Auto task list prioritization and management
Auto task list prioritization
Auto routing for 3D spatial reinspection
Auto routing for 3D reinspection
GenAI-powered agentic chat
GenAI-powered agentic chat

Product video

See SpatioSense in action.

A short walkthrough of the capture-to-resolution workflow.

Reality-grade visuals

See every corner in 3D before you step onsite.

From exterior progress to interior finishes, our capture pipeline delivers spatially anchored visuals you can trust.

Platform depth

Everything you need to turn reality capture into resolved work.

Reality-grade 3D

Photogrammetry tuned for dynamic job sites delivers dense meshes and point clouds with export options for BIM workflows.

Spatial issue tracking

Every observation is geo-anchored, timestamped, and linked to responsible parties for faster accountability.

Plan comparison

Compare as-built scans to design intent, highlighting deviations before they become change orders.

Safety & compliance

Flag missing guards, blocked egress paths, and PPE gaps with automated checks tailored to your standards.

Collaboration

Share views, annotations, and assignments with subs and owners without forcing them into new software.

Integrations

Push findings into Procore, Autodesk Construction Cloud, or CSV/Docx exports to keep documentation consistent.

Procore Autodesk Construction Cloud planned BIM 360 planned Primavera P6 planned Custom APIs

Impact you can measure

Spatial AI that pays for itself before turnover.

0

Reduction in defect re-walks across pilot projects.

0

From capture to actionable, shareable 3D deliverables.

0

Faster close-out when paired with existing project tools.

Team

The people building SpatioSense.

Robotics PhDs shipping field-ready spatial AI for construction teams.

Fred Sukkar

Fred Sukkar, PhD

Co-founder

Fred is a robotics expert with a passion for solving real-world problems through visual intelligence. Holding a PhD in Robotics, he has made significant research contributions to the fields of active perception, planning and mapping.

Fred's career is defined by bridging the gap between academia and industry. Having played pivotal roles in high-growth startups, he understands the challenge of deploying complex algorithms in unstructured environments. At SpatioSense, he is continuing his passion for translating bleeding-edge research into tangible value for the construction industry.

Tin Lai

Tin Lai, PhD

Co-founder

Tin loves CS, ML, robotics, and everything in between. Over his research career, he's worked with international institutions on machine vision for search-and-rescue operation, contributed to defense projects like Autonomous Underwater Vehicles on sonar sensing for mine countermeasure, aerial flight under GPS-denied scenario, and served as CTO for an eVTOL company focusing on urban autonomous flight. At SpatioSense, he turns complex ideas into practical, scalable tech—all with a healthy sense of curiosity and fun.

Raphael Falque

Raphael Falque, PhD

Co-founder

Raphael Falque is a robotics and 3D perception expert focused on enabling robust spatial understanding for real-world autonomous systems. With a PhD in Robotics, his work centres on geometric perception, point-cloud processing, and sensor fusion, tackling challenges such as deformation, motion, and incomplete data in unstructured environments.

Raphael specialises in translating advanced perception research into systems that work reliably outside controlled environments. His work emphasises deployable 3D perception pipelines, combining geometric reasoning and multi-sensor data, to support robust decision-making in dynamic, real-world conditions. He brings a practical mindset to complex perception challenges, ensuring solutions are not only innovative but operational at scale.

Collectively, we bring decades of research in computer vision and AI plus experience shipping production software into active construction environments. We combine academic rigor with operator pragmatism to deliver spatial intelligence that works on real job sites.

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