residential buildings across Italy
Smartphone-first structural screening
Know where tolook closer.
Resoniqa turns guided smartphone vibration measurements into building-specific screening signals—helping organisations prioritise professional inspection across large portfolios.
Designed to support prioritisation—not to diagnose damage, certify safety or replace structural engineers.
Session qualityAccepted
Duration22.8s
Samples1,376
Rate~60 Hz
Building baselineGrowing with every validated session
Technology readinessTRL 3
Working productEnd-to-end MVP
External validationInitial industry LOI
RecognitionDigithON 2026 Top 100
The opportunity
Italy does not lack expertise. It lacks a scalable first screen.
Professional inspections and installed sensor systems are essential when a structure needs detailed assessment. But they require equipment, site visits, time and specialist capacity. Resoniqa is being built for the decision that comes one step earlier: which buildings should we inspect first?
built before 1976
publicly owned real-estate properties
Source: ENEA, 2024. Figures reflect the September 2026 business plan.
The platform
From a phone on a stable surface to a clearer portfolio decision.
Each session is linked to a building and device, checked for quality, processed and compared with previous measurements.
Measure
A guided session captures vibration data using the accelerometer already inside a smartphone.
Validate
Sensor availability, sampling consistency and completeness are checked before data are accepted.
Process
The platform extracts vibration intensity, frequency characteristics and quality indicators.
Compare
New measurements are compared with the building’s historical baseline to reveal change over time.
Screen
Frequency, intensity, baseline deviation and confidence feed a prototype screening indicator.
Prioritise
Clear results help teams decide where professional attention and deeper inspection should go first.
Working MVP
From guided capture to portfolio-level screening.
Real product screens from the current mobile workflow. Every view is shown in its original proportion without cropping.
Research foundation
Built on established scientific and operational evidence.
Peer-reviewed studies and applied research projects have established the basis for smartphone- and crowd-enabled vibration sensing in structural monitoring. Resoniqa builds on this foundation while validating its own screening workflow.
01
MyShake
02
Monitoring Building Structural Health Using Smartphones and Ambient Vibrations
03
Assessing the Structural Health of Buildings Using Smartphones and Ambient Vibration
04
Smartphone Application for Structural Health Monitoring of Bridges
05
Citizen Sensors for SHM
06
CrowdLIM
Who it serves
One sensing layer. Three high-value decisions.
Primary beachhead
Engineering & construction
Screen broader portfolios and focus site visits, detailed assessment and monitoring resources on the buildings that need attention first.
Public resilience
Municipalities & public bodies
Build a clearer first-pass view across schools, offices and other public assets—before and after disruptive events.
Risk intelligence
Insurance portfolios
Add validated historical structural indicators as one more information layer for portfolio screening and prevention.
Institutional customers pay for processed structural information and decision support. Citizens and building residents contribute guided measurements as users and data providers.
Why Resoniqa
The advantage is not the phone sensor.
It comes from combining distributed data collection with quality controls, signal processing, AI-supported analysis and building-specific historical baselines.
01
Deployment-light screening before dedicated instrumentation.
02
Longitudinal building records that become more useful over time.
03
Portfolio-level intelligence designed around professional decisions.
Validation roadmap
Milestone-driven from TRL 3 to first contracts.
Every transition has a gate: technical reliability first, then field value, then willingness to pay.
01
Phase 0Jun–Sep 2026
Proof of concept & MVP
TRL 3 reached, end-to-end sensing workflow live and early-adopter segments identified.
02
Phase 1Oct 2026–Feb 2027
Technical validation
Repeatability, device variation and reference-sensor agreement across controlled tests.
03
Phase 2Mar–Sep 2027
Field validation
3+ pilot partners, 20 buildings and 1,000 validated measurement sessions.
04
Phase 3Oct 2027–Dec 2028
Market launch
Convert successful pilots into paid contracts and refine recurring portfolio pricing.
Built across disciplines
Engineering, AI, software and business in one team.
A multidisciplinary team combining engineering, AI, software, business, and structural science.
MZ
Maziar Zahed
Founder & CEO
PhD candidate in Mechanical Engineering, Faculty of Civil and Architecture Engineering, University of Catania × University of Campania Luigi Vanvitelli.
Expertise
- Computational Mechanics & Structural Dynamics
- Innovation & Project Management
- Product Strategy
Key experience
Led the launch of a 2,800 m² Technology Center & Innovation Factory (SnowTec), managing infrastructure, budgeting, procurement, HR, contractors, and operations.
ND
Nima Dolatabadi
AI Lead
PhD candidate, University of Catania × University of Campania Luigi Vanvitelli.
Expertise
- Computer Vision
- Python & Linux Development
- AI Algorithm Development
Key experience
Designed and optimised algorithms for automated pattern recognition in geological data.
MG
Maryam Ghasemi
Business Strategy & Growth Lead
BSc student in Business Administration, Parthenope University of Naples.
Expertise
- Business Strategy
- Market Validation
- Go-to-Market
Key experience
Contributed to growth strategy, market analysis, and product development at an AI startup, supporting KPI-driven decisions.
DD
Daniel Dolatabadi
Technology Lead
MBA · M.Arch.
Expertise
- Full-Stack Development
- UI/UX Design
- AI Systems Integration
Key experience
Built a production-ready AI-enabled full-stack platform from concept to deployment, integrating multiple LLM providers.
MM
Mahdi Miri
Data Science Lead
MSc student in Data Science, University of Naples Federico II.
Expertise
- Deep Learning
- Natural Language Processing
- RAG & AI Agents
Key experience
Collaborated in the development of RAG-based and AI agent systems.
MF
Maziar Fakoor
Structural Engineering Lead
MSc in Civil Engineering, University of Mazandaran.
Expertise
- Structural Assessment & Rehabilitation
- Reinforced Concrete Structures
- CFRP Strengthening
Key experience
Researcher in post-damage assessment and strengthening of reinforced concrete structures.
Build the evidence with us
Bring Resoniqa into the real world.
We are looking for pilot buildings, reference-sensor validation, research partners and €50,000 in pre-seed funding for an 18-month validation and market-entry programme.