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Ori

AI-powered robotic interiors for space-on-demand living

Architectural robotics platform that integrates transformable robotic furniture systems into residential buildings to expand functional space. Uses smart controls and automated mechanisms to convert single rooms between multiple uses (bedroom, living room, office, closet).

$26.7M raisedEstablished
Founded 2015 · 2,000+ systems installed in 1,750+ apartments across 64 US cities and 140 projects; primary customers include Greystar, Lincoln Property Company, Related Companies
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WHY IT EXISTS

Why This Tool Exists

The Problem

Urban housing shortage and high costs force residents into cramped micro-units (300-400 sq ft) that fail to accommodate modern living activities. A 300 sq ft studio cannot serve as bedroom, living room, home office, and dining space simultaneously, limiting quality of life and driving demand for expensive larger units.

The Solution

Ori integrates computer-controlled robotic furniture systems that transform single rooms on demand. Using simple mechatronics (low-power motors, counterweights, guidance tracks), residents switch a room from bedroom (integrated bed-sofa) to workspace (Pocket Office) to closet (Pocket Closet) via mobile app, voice command, or physical interface. This delivers 500+ sq ft of functional space in 300 sq ft, enabling developers to rent micro-units 10% below market at higher occupancy rates.

USAGE

How You Use It

Delivery Method
Mobile AppSaaS
Integrations
Amazon AlexaGoogle AssistantIKEA (ROGNAN line)
Disciplines
Architecture, Facility Management
Workflows
Data & Automation, Operations & Facility Management
Project Phases
Design Development, Construction Documentation, Construction Administration, Operations/FM
Project Types
Residential, Mixed-Use
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Architectural floor plans, Space dimensions, Desired transformations (bedroom/office/closet)
Optional:User schedule preferences, Custom automation rules
Formats:Physical measurement on-site, Mobile app configuration, BIM data (IFC)
Output
What you get
Format:Deployed robotic furniture system with integrated controls
Fields:Transformed space configurations, Functional area availability, System performance logs, Maintenance alerts, Energy consumption data
Algorithm
How it works
Model:Proprietary hardware control system with embedded sensors and machine learning (ROGNAN line with IKEA uses machine learning to map room boundaries on first few movements)
Accuracy:UL-listed (UL962) safety certification; Cycle-tested for 15+ years durability; Not publicly specified for room mapping accuracy (ROGNAN)
Privacy
How your data is protected
Retention:Project-based retention during occupancy; post-occupancy data handling not publicly specified
Training:Machine learning from usage patterns to predict resident needs; training data handling not publicly detailed
Compliance:UL-listed furniture systems (UL962), SOC 2 status not publicly disclosed, GDPR compliance not documented for ROGNAN international expansion
API
Integration
Endpoint:Not publicly documented
Method:Contact vendor for B2B integration options
USE CASES

Use Cases

  • ·Converting single 300 sq ft studio to multi-functional space (bedroom + living room + office)
  • ·Adding home office workspace during pandemic-era remote work transitions
  • ·Maximizing closet and storage functionality in space-constrained units
  • ·Flexible senior living environments (Pocket Office for healthcare oversight)
  • ·Enabling affordability through smaller unit sizes at competitive rent points
PRICING

Pricing

Free
Not available
Pro
B2B: Contact vendor for pricing per installation; Estimated $5,000-$18,990 per unit based on system complexity
Enterprise
Developer partnerships with co-GP revenue sharing and volume discounts negotiated on per-project basis
Sources & Research NotesResearch date: 2026-02-01
Data Sources (7)
Fields Checked (20)
vendor, website, fundingTotal, yearFounded, maturityStage, customerBase, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, problem, solution, input-required, input-formats, output-format, output-fields, control-features, use-cases, pricing-pro
Not Found (9)
api-documentation, api-endpoint, privacy-policy-detailed, soc2-certification, gdpr-compliance, algorithm-accuracy-metrics, data-retention-exact-period, training-data-policy, webhook-support
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.
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