CRANE · 0.97

WORKER · PPE ✓

REBAR STACK · 0.91

SPATIAL INTELLIGENCE SYSTEM

RoVis

Spatial Intelligence for India’s Construction Sites

Creating real-time digital twins of Indian construction sites, from metro corridors to high-rise towers, through drone vision, semantic scene understanding and spatial AI.

Try the live demo → Request a demo See how it works

SCANNING · 4 DRONES POINTS 1.42M SLAM LOCK ✓ TWIN SYNCED

01 / THE PROBLEM

Construction sites remain blind environments.

Sites generate massive visual information, yet critical decisions are still made on incomplete, out-of-date evidence.

HOW SITES ARE MONITORED TODAY

Manual inspection

Sparse, subjective, walked once a day.

Delayed reports

Insight arrives after the decision window closes.

Fragmented data

Photos, logs and drawings never fuse into one view.

THE CHALLENGE

The environment changes every day structures evolve, migrant crews rotate, material stacks shift. On a large Indian site, 300+ workers and dozens of subcontractors turn over weekly, and no legacy system keeps a live understanding of it.

MANUAL INSPECTION · DAILY WALK

CONSEQUENCES

Fatal safety risk

~38 fatal site accidents a day (India)

CRITICAL

Cost & schedule overruns

~40% of major infra projects delayed (MoSPI)

CRITICAL

Rework, pilferage & waste

5–10% of total contract value

HIGH

Field–office data lag

Daily progress lag = weeks of delay

HIGH

DELAYED INFRA PROJECTS · INDIA
₹4.8 lakh cr in cost overruns across
1,800+ monitored projects.

02 / OUR SOLUTION

A continuously evolving digital twin of the site.

RoVis fuses visual data into a structured spatial model, then reasons over it converting raw pixels into actionable site intelligence, built for the density and pace of Indian sites.

NOT A MAP - A MODEL

Objects, not pixels

Every element is a semantically labelled entity with state & history.

TEMPORAL

Persistent memory

The twin remembers yesterday, so it can reason about change.

LIVE

Always current

Continuous ingestion keeps the model synced to the real site.

ACTIONABLE

Reasons & recommends

Goes past observation to decision support & next actions.

INGESTS

DRONE CAPTURE · LIVE INPUT

◈ Drone imagery

◈ CCTV & mobile cameras

◈ Multi-view frames

RoVis ENGINE

Spatial
Reasoning

CONVERTS TO

Computer vision

Semantic scene understanding

Spatial mapping & SLAM

AI reasoning & planning

03 / SYSTEM ARCHITECTURE

From pixels to decisions , a five-layer pipeline.

STREAMING · 30 FPS
EDGE + CLOUD HYBRID

Input

DronesCCTV camerasMobile cameras

Perception

Object detectionSemantic segmentationInstance tracking

L3 · CORE

Spatial Intelligence

Semantic SLAMScene reconstructionLocalization

Digital Twin Engine

Persistent 3D stateTemporal memoryObject registry

Decision Intelligence

Hazard reasoningResource trackingPlanning & actions

◦ CLOSED-LOOP · TWIN STATE FEEDS PERCEPTION PRIORS ◦ LATENCY < 200 ms EDGE PATH ◦ RESILIENT ON 4G / INTERMITTENT SITE LINKS

04 / DIGITAL TWIN

A living model of the entire site.

Multi-view reconstruction fuses every frame into one persistent, semantically labelled 3D twin.

RECONSTRUCTION PIPELINE

Structure-from-motion

Camera poses recovered from overlapping drone frames.

Dense point-cloud fusion

Depth maps merged into a unified spatial frame.

Semantic labelling

Each cluster classified & registered as a twin object.

MULTI-VIEW RECONSTRUCTION · SEMANTIC TWIN

STRUCTURE · CORE-A

EQUIPMENT · EXCAVATOR

TWIN OBJECT REGISTRY

Structures128

Workers46

Equipment12

Material stacks31

Site zones9

Reconstruction1.42M pts · 2.1cm RMSE

05 / CAPABILITIES

The twin, put to work.

Safety, resources and progress every capability reasons over the same live spatial model. Every worker is continuously checked against BOCW safety norms: helmets and hi-vis, crane swing radius, edge and excavation exclusion zones.

TWIN VIEWPORT · SAFETY LAYER

⚠ Missing helmet

⚠ Crane radius

⚠ Unsafe proximity

● PPE OK ● WARNING ● CRITICAL

HAZARD DETECTION · LIVE FEED ● 3 ACTIVE

CRITICAL0.96

Missing hard-hat detected

ZONE POUR-B · WKR-041 · 12s ago

WARNING0.88

Worker–excavator proximity

2.1 m · below 5 m threshold

CRITICAL0.93

Restricted-zone entry

CRANE SWING RADIUS · WKR-019

RESOURCE INTELLIGENCE

Every material, machine & crew spatially tracked.

Tracks steel and cement stock, machinery idle-time and crew density, and flags overnight material pilferage, a persistent drain on Indian sites.

LOW HIGH · WORKFORCE DENSITY

MACHINERY

71%

12 units · 3 idle

WORKFORCE

across 9 zones

MATERIALS

stacks tracked

PROGRESS → AI PLANNING

From monitoring to decision support.

4D PROGRESS · SAME SITE OVER TIME

COMPLETION

78%+14%/30d

T-30d28%

T-20d47%

T-10d64%

TODAY78%

RECOMMEND · WORKFORCEconf 0.91

Move 6 crew to Level-4 slab to recover the 3-day delay.

RECOMMEND · PROCUREMENTconf 0.87

Reorder cement now , stock hits zero in 4 days.

RECOMMEND · NEXT TASKconf 0.84

Pour the east-wing slab before Thursday’s monsoon window closes.

06 / AGENTIC AI

Ask your site anything.

A natural-language agent sits on top of the twin. Ask in plain English, it navigates the 3D model, analyses it, and answers with located, actionable results.

RoVis AGENT reasoning over 1.42M-pt twin

Where are all workers without hard-hats right now?

3 workers flagged. WKR-041 in Zone Pour-B, WKR-017 & WKR-052 near the North yard, all inside an active crane radius.

Dispatch alertShow on map

Which equipment has been idle over 2 hours?

2 excavators idle 3h 12m in the North yard , est. ₹48k in lost machine-hours today. Reassign to Level-4 excavation?

Ask about the site… ↑

AGENT VIEW · ANSWERS LOCATED ON TWIN

Understands

Parses plain English.

Queries

Pulls twin objects.

Reasons

Spatial + temporal.

Acts

One-click action.

07 / EXECUTION LAYER

The map is the beginning not the end.

Once the twin exists, RoVis turns every insight into a concrete action on the ground , routed to the right person, automatically.

EXECUTION PIPELINE DETECT→ DECIDE→ DISPATCH→ VERIFY↻

Safety triggers

FIRES ON VIOLATION

Push + SMS alert (Hindi / regional) to supervisor with location & snapshot

Auto stop-work order for critical breaches

On-site siren / beacon in the affected zone

Escalates up the chain if unacknowledged in 60s

Site management

RUNS THE DAILY OP

Auto daily progress report to office , no manual walks required

Work orders dispatched to crews with exact location + task

Crew & equipment reallocation pushed to foremen

Auto-procurement when stock crosses a threshold

As-built reality synced back to BIM & schedule

Attendance & productivity from on-site presence

Autonomous loop

CLOSES & VERIFIES

Drone re-tasked to re-scan the changed zone

Verification pass confirms the action was completed

Twin updates → next recommendation issued

08 / EXPECTED IMPACT

Four outcomes, one spatial brain.

Safety

Faster hazard response

Located, prioritized alerts the moment a risk appears — cutting exposure to the ~38 site deaths India records each day.

Efficiency

Less idle & rework

Resources routed to where they create value , cutting the 5–10% lost to rework and pilferage.

Visibility

Continuous awareness

Daily reality capture cuts survey time ~60% vs manual walks.

Planning

Data-driven decisions

AI scheduling grounded in real site state , recovering weeks lost to labour gaps and the monsoon.

CLOSING VISION

Every construction site deserves a digital brain.

RoVis transforms visual information into spatial intelligence — starting on the site, scaling to the city.

Try the live demo → Request a demo Back to top

Autonomous drone patrols Infrastructure monitoring Smart factories Industrial digital twins Smart-city integration