InfraSense.AI
Working Prototype
AIoT Road Intelligence

Roads are physical.
Their intelligence
shouldn't be.

InfraSense AI transforms road-condition signals into location-aware infrastructure intelligence — enabling more informed, more efficient maintenance decisions.

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3
Sensing Modalities
YOLOv8
Detection Model
Prototype
Current Stage
infrasense://road-intelligence/live
PROTOTYPE VISUALIZATION
Segments
R-01338
R-01492
R-01571
R-01722
R-01944
LAT 8.7312°LON 77.7045°CriticalHigh riskDefect
SIM · PROTOTYPE
Segment Intel
R-014
Main Arterial · Urban
Risk Score
92
/100 · CRITICAL
Events
Surface Defect4
Cracking2
Event StreamSIMULATED DATA
10:41:08IMU anomaly detected — vibration spike registered.
10:41:12Visual confirmation via YOLOv8 — potential surface defect.
10:41:18GPS lock confirmed. Segment: R-014 · LAT 8.7312° · LON 77.7045°
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The Infrastructure Problem

Most road networks are inspected periodically.
Infrastructure doesn't deteriorate periodically.

The gap between inspection cycles is where maintenance budgets are quietly consumed by problems that could have been caught earlier.

Current Reality
Periodic Inspection
Survey
Infrequent manual inspection. Months between cycles.
Snapshot
Point-in-time data. Condition at a single moment.
Deterioration Gap
Defects grow undetected between surveys.
Complaint-Driven
Maintenance triggered by failures, not evidence.
Reactive Response
Emergency repairs. Higher cost. Lower outcomes.
InfraSense AI Direction
Continuous Intelligence
Continuous Sensing
Camera + IMU + GPS streaming while vehicles traverse the network.
AI Detection
YOLOv8 inference identifies surface anomalies in real time.
Location-Aware Evidence
Events tied to precise GPS coordinates and road segments.
Network Visibility
Condition data aggregated across multiple passes over time.
Prioritized Intelligence
Ranked intervention list. Evidence-based budget allocation.

“When maintenance starts with fragmented information, constrained budgets are forced into reactive decisions.”

The InfraSense AI Pipeline

From road surface to maintenance intelligence.

Every stage in our pipeline transforms raw physical signal into actionable insight — making individual detections meaningful at network scale.

01
Physical Road
Asphalt, surface, geometry
02
Sensing
Camera · IMU · GPS
03
AI Detection
YOLOv8 edge inference
04
Geolocation
GPS + road segment binding
05
Reconciliation
Multi-pass event consensus
06
Network Map
Segment-level condition state
07
Risk Scoring
Severity + frequency + priority
08
Decision Support
Maintenance intelligence output
Not just detection
Detecting defects is the starting point — not the destination. Value lies in what happens after detection.
Network-scale understanding
Aggregating events across multiple passes and segments reveals the true condition of a road network.
Prioritization as the product
The final output is a prioritized action list — making constrained maintenance budgets more effective.
Prototype Command Interface

Road intelligence, visualized.

PROTOTYPE INTERFACE · SIMULATED DATA
INFRASENSE ROAD INTELLIGENCE CONSOLE · v0.9 PROTOTYPE
Network Segments
LAT 8.7312°LON 77.7045°
PROTOTYPE · SIM DATA
Event Stream
10:41:31Segment R-014 priority → CRITICAL
10:41:24Event confirmed. Severity: HIGH
10:41:18GPS lock: SEG-A4-01 · R-014
Segment Intelligence
SEG-A4-01
Main Arterial·N
Risk Score
92
/100 · Critical
Observations
Event count7
ConfidencePrototype
Status
PROTOTYPE · NOT VALIDATED

Data shown is simulated for prototype demonstration. Not from live field operation.

Technical Architecture

Sensor to intelligence, end to end.

Every layer is purpose-built for real road environments — connecting physical signal all the way to decision-ready output.

Layer 01
Physical Sensing
ACTIVE · PROTOTYPE
Camera
RGB · 30fps
active
IMU
Vibration · 100Hz
active
GPS
Location lock
active
ESP32
Edge MCU
active
Layer 02
AI Perception
ACTIVE · PROTOTYPE
YOLOv8
Object detection
active
OpenCV
Frame pipeline
active
Edge Inf.
Real-time
active
Layer 03
Intelligence Layer
ACTIVE · PROTOTYPE
FastAPI
Data ingestion
active
PostgreSQL
Spatial storage
active
Docker
Containerized
active
Layer 04
Intelligence Interface
ACTIVE · PROTOTYPE
React/Vite
Dashboard UI
active
Node.js
API gateway
active
Supabase
Auth + DB
prototype

Technology stack reflects current working prototype state. Architecture may evolve during field validation.

The Difference

Not another pothole detector. An intelligence layer.

Traditional Road Inspection
Periodic inspection cycles
Manual, labor-intensive surveys
Fragmented, spreadsheet-based data
No precise geospatial context
Reactive to failures and complaints
Difficult to scale across networks
InfraSense AI · Working Prototype
Continuous sensing while traversing
AI-assisted automated detection
Location-aware evidence at GPS precision
Road segment-level condition mapping
Prioritized by evidence and severity
Network-scale intelligence architecture

InfraSense AI capabilities described reflect the design direction of the working prototype. Field validation is the next milestone.

Infrastructure Resilience

Earlier intelligence.
More resilient roads.

Earlier detection can support more resource-efficient maintenance pathways. When infrastructure receives attention before advanced deterioration, less material, energy, and cost are involved.

Important Note

InfraSense AI makes no specific claims about CO₂ savings, cost reductions, or other quantified environmental outcomes. Any such impact would depend on implementation at scale and validated field data. These are directional observations only.

Detect Earlier
Identify deterioration at early stages when intervention is smaller.
Assess Accurately
Evidence-based severity and location — not estimates from surveys.
Prioritize Efficiently
Direct constrained maintenance budgets to highest-impact segments.
Maintain Proactively
Targeted repair before escalation to costly reconstruction.
Extend Asset Life
Longer-lived infrastructure means reduced resource consumption over time.
Preferred pathway
Early Intervention
Small defect detected
Targeted patch repair
Surface restored
Asset life extended
Avoidable scenario
Late Reconstruction
Defect grows undetected
Alligator cracking
Base layer failure
Full reconstruction required
Designed For India

Built for the complexity of India's road infrastructure.

InfraSense AI is being developed in India, with Indian road conditions, Indian road authorities, and Indian infrastructure constraints at the center of the design process — not as an afterthought.

Heterogeneous road networks
Urban expressways, district roads, rural paths — often in the same jurisdiction.
Distributed road authorities
PWDs, Urban Local Bodies, NHAI, district authorities — each with different mandates.
Constrained maintenance budgets
Prioritization matters more when resources are limited.
Monsoon and weather exposure
Seasonal deterioration cycles create predictable maintenance demand.
Large geographic scale
Network-level intelligence is necessary — spot inspections cannot cover the whole picture.
Conceptual · No real deployment shown
Designed For
State PWDs
State public works departments managing arterial networks.
Urban Local Bodies
Municipalities overseeing city road maintenance.
District Road Authorities
District-level networks with mixed condition profiles.
Highway Operators
Operators needing continuous condition awareness.

These are target user categories — not current customers or partners.

Development Roadmap

From prototype to platform.

Each stage requires genuine milestones — not just time. We move forward when the evidence justifies it.

now
Working Prototype
Camera + IMU + GPS + AI detection system built and functional.
CURRENT
next
Field Validation
Real-world testing on public roads. Accuracy and coverage validation.
then
Institutional Pilot
Partnered pilot with a road authority or municipality.
later
Scalable Implementation
Multi-vehicle, multi-segment deployment. Network-level intelligence.
future
Platform Scale
Infrastructure Intelligence Platform at city/state network scale.
0
Paying Customers
Pre-revenue
0
External Pilots
Pre-pilot
₹0
Revenue
Bootstrapped
₹15 Lakh
Funding Ask
Seed target

Transparent current status. We believe honesty about stage is a sign of technical seriousness, not weakness.

The Builders

Building something infrastructure-grade.

InfraSense AI is being built by engineers who understand that infrastructure intelligence is not a product — it is a discipline. Our work is grounded in real technical constraints, not theoretical capability.

Institutional Foundation
Government College of Engineering, Tirunelveli
Faculty Mentor: Prof. G. Sona
Working Prototype · Academic Origin
S
Saqib Ahmad Bhat
Co-Founder
System architecture, AI pipeline, sensor integration
A
Aqib Majeed
Co-Founder
Product development, data systems, infrastructure intelligence
Legal Entity
UDYAM-JK-04-0054839
Micro Enterprise · 2026–27 · Registered
Read the full team story

Let's make every road
more observable.

We are looking for institutional partners, pilot opportunities, research collaborators, and infrastructure stakeholders who want to help build the intelligence layer for physical roads.

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InfraSense AI · Working Prototype · AIoT Road Infrastructure Intelligence · infrasenseai.online