AI Field Asset Inspection App with LiDAR & Computer Vision | UK

AI-Powered LiDAR Asset Inspection, Offline Industrial Auditing & ERP Integration — UK

Project Overview

We developed an Enterprise AI Field Asset & Equipment Auditor for a UK-based industrial organization that needed a reliable mobile platform for inspecting, documenting, and auditing physical assets across large facilities and remote industrial environments.

The solution combines LiDAR 3D spatial scanning, augmented reality, computer vision, edge AI, offline synchronization, automated PDF reporting, and enterprise ERP integration into a single iOS application.

Field engineers can use an iPhone or iPad to scan rooms and physical equipment, capture spatial information, inspect asset components using AI, continue working without an internet connection, and synchronize inspection data once connectivity becomes available.

The application was developed using Swift, SwiftUI, ARKit, RoomPlan API, Core ML, and Metal on iOS. The backend uses Python, FastAPI, PyTorch, gRPC, PostgreSQL, and AWS GovCloud, while Realm provides encrypted local storage and synchronization capabilities. Enterprise identity and access management is handled through Okta OAuth 2.0 / SAML SSO.

The platform was designed around a simple operational workflow:

Scan → Identify → Inspect → Validate → Record → Report → Synchronize → Integrate.

 

---

Business Challenge

Enterprise field teams frequently need to inspect physical equipment across:

• Industrial facilities

• Manufacturing plants

• Warehouses

• Utility sites

• Engineering environments

• Remote locations

• Restricted operational areas

Traditional asset auditing can involve paper checklists, photographs, spreadsheets, disconnected inspection applications, and manually generated reports.

This creates several operational problems.

Field engineers may need to:

• Identify equipment manually

• Record asset information

• Photograph components

• Document defects

• Measure physical spaces

• Complete inspection checklists

• Work in areas with poor connectivity

• Upload information later

• Prepare audit documentation

• Synchronize information with enterprise systems

The client wanted to replace this fragmented workflow with a unified mobile inspection platform.

The target workflow was:

Field Engineer
      ↓
LiDAR 3D Scan
      ↓
Room / Asset Understanding
      ↓
AI Component Inspection
      ↓
Inspection Data
      ↓
Offline Storage
      ↓
PDF Audit Report
      ↓
Cloud Synchronization
      ↓
SAP / Oracle ERP

---

Project Objectives

The primary objectives were to:

1. Build an enterprise-grade iOS field inspection application.

2. Use LiDAR to capture spatial information.

3. Create 3D representations of rooms and assets.

4. Use ARKit for spatial understanding.

5. Use RoomPlan API for room scanning.

6. Implement real-time edge AI inspection.

7. Detect equipment components using custom AI models.

8. Allow field engineers to work without internet access.

9. Synchronize inspection records after connectivity returns.

10. Generate professional PDF audit reports automatically.

11. Integrate inspection information with SAP and Oracle ERP systems.

12. Implement enterprise authentication.

13. Support Okta OAuth 2.0 / SAML SSO.

14. Encrypt locally stored field data.

15. Build an architecture suitable for enterprise-scale deployment.

---

The Solution

We developed a native iOS enterprise inspection platform combining spatial computing, computer vision, AI, local data storage, cloud processing, and ERP integration.

The high-level architecture was:

                         FIELD ENGINEER
                                │
                                ▼
                       iOS / iPadOS APP
                                │
        ┌───────────────────────┼───────────────────────┐
        ▼                       ▼                       ▼
      LiDAR                  Edge AI                 Offline
        │                       │                       │
   ARKit / RoomPlan         Core ML                  Realm
        │                       │                       │
        ▼                       ▼                       ▼
  3D Spatial Data        Component Detection      Local Records
        │                       │                       │
        └───────────────┬───────┴───────────────────────┘
                        ▼
                   FastAPI Backend
                        │
                        ▼
                    gRPC Services
                        │
             ┌──────────┼──────────┐
             ▼          ▼          ▼
        PostgreSQL     AI APIs    Reporting
             │                       │
             └──────────┬────────────┘
                        ▼
                 Enterprise Systems
                  ┌──────────────┐
                  │ SAP / Oracle │
                  └──────────────┘

The application was designed so that field engineers could continue working even when cloud connectivity was unavailable.

---

LiDAR 3D Asset Scanning

One of the defining capabilities of the platform is LiDAR-based spatial scanning.

Supported iPhone and iPad devices with LiDAR can capture information about physical environments and help construct a digital representation of the inspected space.

The scanning workflow is:

Open Scanner
     ↓
Detect Environment
     ↓
LiDAR Depth Capture
     ↓
Room Understanding
     ↓
3D Spatial Model
     ↓
Asset Inspection

This gives field engineers a more structured representation of the physical inspection environment.

---

RoomPlan API

Apple's RoomPlan API was used to assist with room scanning and spatial understanding.

The application can identify structural elements and create a structured representation of an inspected environment.

Potentially recognized elements include:

• Walls

• Doors

• Windows

• Openings

• Furniture

• Room dimensions

• Spatial relationships

This spatial information can become part of the audit record.

---

ARKit Integration

ARKit provides the foundation for augmented-reality and spatial-computing functionality.

The application can use ARKit for:

• World tracking

• Spatial understanding

• Surface detection

• Camera positioning

• Object placement

• 3D scene interaction

The combination of ARKit and LiDAR enables the application to understand the physical environment rather than treating the camera as a simple image-capture device.

---

3D Spatial Audit

The captured environment can become part of an asset audit.

For example:

Industrial Room
      │
      ├── Equipment A
      ├── Equipment B
      ├── Control Panel
      ├── Electrical Unit
      └── Safety Equipment

This creates a spatial context for inspection records.

Instead of simply storing:

"Equipment A inspected."

the system can associate the asset with a physical location within the scanned environment.

---

Asset Identification

The platform uses AI to help identify equipment components.

A field engineer can point the device toward equipment while the AI pipeline processes the camera feed.

Camera
  ↓
Frame
  ↓
Core ML
  ↓
Object Detection
  ↓
Equipment / Component
  ↓
Inspection Result

This reduces the need for field engineers to manually identify every component.

---

Real-Time Edge AI

A major architectural requirement was the ability to perform AI inference directly on the mobile device.

The application uses Core ML for on-device machine-learning inference.

This allows the device to analyze supported visual information without requiring every frame to be uploaded to a cloud AI service.

Benefits include:

• Low latency

• Reduced network dependency

• Faster response

• Offline operation

• Lower cloud inference requirements

• Improved field usability

---

Custom PyTorch Object Detection Models

Custom object-detection models were developed using PyTorch.

The models can be trained to identify equipment or specific components relevant to the client's industrial environment.

For example:

Camera Frame
      ↓
Object Detection Model
      ↓
Detected Components
      ├── Control Panel
      ├── Valve
      ├── Gauge
      └── Electrical Unit

The model can then provide detection information to the inspection workflow.

---

Core ML Model Deployment

Models developed using machine-learning frameworks can be converted or prepared for mobile inference through Apple's Core ML ecosystem.

The resulting model can run directly on the iOS device.

The architecture becomes:

PyTorch
   ↓
Trained Model
   ↓
Mobile-Compatible Model
   ↓
Core ML
   ↓
iPhone / iPad

This allows the trained AI capability to operate at the edge.

---

Metal GPU Processing

Metal was used for GPU-accelerated processing where appropriate.

AI and spatial applications can involve computationally intensive operations.

Metal can help utilize Apple GPU hardware for:

• Parallel computation

• Image processing

• Rendering

• Computer vision workloads

• Spatial visualization

This is particularly useful when the application needs to maintain responsiveness while performing AI or 3D operations.

---

AI Component Inspection

The application can use detected components as the starting point for inspection.

For example:

Detected:
Electrical Control Panel

       ↓

Inspection

       ↓

Condition:
Normal

Required:
Routine Maintenance

Or:

Detected:
Valve

       ↓

AI Inspection

       ↓

Potential Issue Detected

       ↓

Engineer Review

AI findings are treated as inspection assistance rather than an unquestionable replacement for qualified field personnel.

---

Human-in-the-Loop Inspection

Enterprise inspection systems should not rely entirely on automated predictions.

The application therefore supports human verification.

The workflow is:

AI Detection
     ↓
Suggested Result
     ↓
Field Engineer Review
     ↓
Accept / Correct
     ↓
Final Inspection Record

This is particularly important for safety-critical or compliance-related environments.

---

Inspection Checklist

The application can provide structured inspection checklists.

For example:

Equipment: Control Panel

☑ Physical Condition
☑ Labels Present
☑ Enclosure Condition
☑ Wiring Visibility
☐ Maintenance Required
☐ Safety Issue

This provides a consistent inspection methodology across field teams.

---

Asset Condition Recording

Field engineers can record conditions such as:

• Normal

• Requires Maintenance

• Damaged

• Critical

• Not Accessible

• Requires Review

The exact status model can be configured according to the client's operational procedures.

---

Inspection Evidence

The platform can associate evidence with an asset inspection.

Evidence may include:

• Camera images

• AI detection results

• 3D spatial information

• Engineer notes

• Inspection checklist

• Timestamp

• Location

• Asset identifier

This creates a stronger audit trail than a simple text-based checklist.

---

Offline-First Field Operation

Remote industrial sites may have:

• Weak cellular coverage

• No Wi-Fi

• Restricted networks

• Temporary connectivity interruptions

The application was therefore designed with offline operation as a core requirement.

A field engineer should not lose the ability to perform an inspection simply because the device temporarily loses internet access.

The workflow becomes:

No Internet
    ↓
Continue Inspection
    ↓
Save Locally
    ↓
Complete Audit
    ↓
Connectivity Returns
    ↓
Synchronize

---

Realm Encrypted Local Storage

Realm was used for local data persistence and synchronization.

It can store:

• Inspection records

• Asset information

• AI results

• User actions

• Notes

• Pending synchronization operations

Encryption provides an additional layer of protection for sensitive enterprise field data.

---

Offline Synchronization

The application maintains local records while offline.

When connectivity returns:

Local Realm
    ↓
Detect Connection
    ↓
Sync Queue
    ↓
Backend
    ↓
PostgreSQL

The synchronization layer must account for conflicts and partially completed operations.

---

Synchronization Queue

A queue can track records awaiting synchronization.

Example:

Inspection #1024 — Pending
Inspection #1025 — Pending
Inspection #1026 — Synced
Inspection #1027 — Pending

When connectivity becomes available, pending records can be processed.

---

Conflict Handling

Offline systems can create situations where the same asset is updated in multiple locations.

A synchronization strategy can use:

• Record IDs

• Version numbers

• Timestamps

• Update metadata

• Server-side validation

This allows the backend to determine how conflicting changes should be handled.

---

FastAPI Backend

The backend was developed using Python and FastAPI.

FastAPI provides a modern API layer between the mobile application and enterprise services.

Potential responsibilities include:

• Authentication validation

• Asset APIs

• Inspection APIs

• Synchronization

• Report generation

• AI service integration

• ERP integration

• Audit data processing

---

gRPC Communication

gRPC was used for efficient communication between backend services where appropriate.

A possible architecture is:

iOS Application
      ↓
FastAPI
      ↓
gRPC
 ┌────┼─────────┐
 ▼    ▼         ▼
AI   Reports   ERP
Service Service Integration

gRPC is particularly useful for structured, high-performance communication between internal services.

---

PostgreSQL

PostgreSQL provides persistent cloud-side storage.

Potential entities include:

Users
Assets
Facilities
Rooms
Inspections
Inspection Items
AI Findings
Audit Reports
Synchronization Records
ERP References

The database can maintain the authoritative enterprise inspection record after synchronization.

---

Asset Database

Each physical asset can have a structured record.

Example:

Asset ID: EQ-2048
Type: Electrical Panel
Location: Facility A
Room: Control Room 3
Status: Operational
Last Inspection: 12 Aug 2026
Next Inspection: 12 Nov 2026

This allows the mobile inspection process to connect directly with enterprise asset information.

---

Asset Lifecycle

The platform can support a complete asset lifecycle:

Asset Registered
      ↓
Asset Located
      ↓
Inspection
      ↓
Condition Recorded
      ↓
Maintenance
      ↓
Re-Inspection
      ↓
Historical Record

This creates a longitudinal asset history.

---

Dynamic PDF Audit Report Generator

One of the major outputs of the platform is an automatically generated PDF audit report.

Instead of manually compiling photographs, inspection notes, and asset information, the system can assemble them automatically.

The workflow is:

Inspection Completed
       ↓
Collect Evidence
       ↓
Collect AI Findings
       ↓
Collect Asset Data
       ↓
Collect Engineer Notes
       ↓
Generate PDF
       ↓
Audit Report

---

Audit Report Contents

A generated report can contain:

• Company information

• Facility information

• Inspection date

• Engineer information

• Asset information

• 3D/spatial context

• Inspection checklist

• AI findings

• Photographic evidence

• Detected issues

• Engineer comments

• Recommendations

• Audit status

• Report identifier

---

Dynamic Report Generation

The report engine can use templates based on the inspection type.

For example:

Inspection Type
      ↓
Select Template
      ↓
Populate Asset Data
      ↓
Insert Images
      ↓
Insert Findings
      ↓
Insert Sign-Off
      ↓
Generate PDF

This provides consistency across enterprise reports.

---

Automated Evidence Organization

Inspection photographs and AI findings can be automatically associated with the correct asset.

For example:

Asset EQ-2048
 ├── Front Image
 ├── Control Panel Image
 ├── AI Detection
 ├── Inspection Checklist
 └── Engineer Notes

The report generator can then place these items in the appropriate report sections.

---

SAP Integration

The platform was designed to integrate inspection information with SAP enterprise systems where required.

Potential integration areas include:

• Asset master data

• Equipment IDs

• Maintenance records

• Work orders

• Inspection results

• Maintenance status

A typical workflow is:

Field Inspection
      ↓
Validated Result
      ↓
FastAPI
      ↓
Integration Layer
      ↓
SAP
      ↓
Enterprise Asset Record

---

Oracle ERP Integration

The same architecture can support Oracle ERP environments.

The integration layer can map inspection information to the appropriate Oracle records and workflows.

Inspection
    ↓
Data Transformation
    ↓
Oracle Integration
    ↓
ERP Record

The integration architecture was designed to keep ERP-specific logic separate from the mobile application.

---

ERP Data Mapping

Different enterprise systems use different schemas.

The integration layer therefore maps:

Mobile Asset
      ↓
Internal Asset Model
      ↓
SAP / Oracle Mapping
      ↓
Enterprise Object

This avoids tightly coupling the iOS application to a specific ERP data model.

---

Enterprise Authentication

Enterprise users require centralized identity management.

The platform uses Okta for identity and access management.

Supported mechanisms include:

• OAuth 2.0

• SAML SSO

This allows employees to use organizational authentication policies rather than creating a completely independent authentication system.

---

Okta OAuth 2.0

OAuth 2.0 can provide secure authorization between the application and backend services.

A simplified flow is:

User
 ↓
iOS App
 ↓
Okta
 ↓
Authentication
 ↓
Access Token
 ↓
FastAPI
 ↓
Authorized Request

---

SAML Single Sign-On

For enterprise environments using federated identity, SAML SSO can allow employees to authenticate through their organization's identity provider.

This provides centralized control over:

• User access

• Authentication policies

• Employee lifecycle

• Enterprise identity

---

Role-Based Access

The platform can support different enterprise roles.

For example:

Field Engineer

Can perform inspections.

Supervisor

Can review inspections.

Auditor

Can access audit reports.

Administrator

Can manage enterprise settings.

Integration Service

Can communicate with ERP systems.

This prevents every user from receiving unrestricted access.

---

Security Architecture

Because the application handles enterprise asset and inspection data, security was considered throughout the architecture.

Key areas include:

• Okta authentication

• OAuth 2.0

• SAML SSO

• HTTPS

• Encrypted Realm storage

• API authorization

• Role-based access

• PostgreSQL access controls

• Secure cloud infrastructure

• Audit logging

• Input validation

---

AWS GovCloud Infrastructure

The backend infrastructure was designed around AWS GovCloud as specified for the project.

The cloud architecture can provide isolated infrastructure for backend APIs, data services, processing workloads, and enterprise integrations.

A conceptual architecture is:

                         AWS CLOUD
                            │
                      Load / API Layer
                            │
                         FastAPI
                            │
              ┌─────────────┼─────────────┐
              ▼             ▼             ▼
          AI Services    PostgreSQL    Reporting
              │                           │
              └─────────────┬─────────────┘
                            ▼
                     ERP Integrations

Note: AWS GovCloud is a US-region cloud offering. For a UK client, the final production-region and data-residency configuration would need to be selected according to the client's regulatory, contractual, and residency requirements.

---

Edge AI + Cloud AI Architecture

The system separates workloads between the device and cloud.

On Device

• Core ML

• Fast component detection

• Basic image analysis

• Offline inference

Cloud

• PyTorch processing

• Advanced AI workloads

• Centralized processing

• Model management

• Enterprise analytics

The architecture becomes:

                    AI PROCESSING
                         │
             ┌───────────┴───────────┐
             ▼                       ▼
        EDGE DEVICE               CLOUD
        Core ML                  PyTorch
        Metal                    FastAPI
        Offline AI               gRPC
             │                       │
             └───────────┬───────────┘
                         ▼
                 Inspection Results

---

Why Edge AI Was Important

Industrial field environments cannot always depend on continuous cloud connectivity.

Edge AI allows the field engineer to receive immediate feedback from the device.

For example:

Camera
 ↓
Core ML
 ↓
Component Detected
 ↓
Immediate UI Feedback

There is no requirement to upload every camera frame to a remote server before providing a response.

---

AI Model Lifecycle

The AI architecture can support an ongoing model improvement process.

Field Data
   ↓
Validated Findings
   ↓
Training Dataset
   ↓
PyTorch Training
   ↓
Model Evaluation
   ↓
Mobile Model
   ↓
Core ML
   ↓
Field Deployment

This creates a foundation for continuously improving component detection.

---

AI Confidence Scores

AI predictions can be accompanied by confidence information.

For example:

Component:
Control Panel

Confidence:
94%

Status:
Detected

Low-confidence predictions can be flagged for human verification.

---

Human Verification Threshold

A configurable threshold can determine when human review is required.

For example:

Confidence > Threshold
       ↓
Suggested Detection

Confidence < Threshold
       ↓
Manual Review Required

This is particularly useful in enterprise inspection workflows.

---

Asset Location Intelligence

Because the application uses LiDAR and spatial scanning, asset records can contain contextual location information.

For example:

Facility A
   ↓
Floor 2
   ↓
Control Room
   ↓
Equipment EQ-2048

This creates a richer asset database than a simple text address.

---

3D Spatial Documentation

The application can associate a scanned environment with an inspection report.

The audit record may therefore contain:

• Room dimensions

• Spatial model

• Asset positions

• Inspection images

• Component detections

This creates a digital representation of the physical inspection environment.

---

Field Inspection Workflow

The complete field workflow is:

1. Login with Enterprise SSO
          ↓
2. Select Facility
          ↓
3. Select Room / Asset
          ↓
4. Start LiDAR Scan
          ↓
5. Build Spatial Model
          ↓
6. Scan Equipment
          ↓
7. Edge AI Detection
          ↓
8. Review Components
          ↓
9. Complete Inspection Checklist
          ↓
10. Add Notes / Evidence
          ↓
11. Save Offline
          ↓
12. Generate Audit Report
          ↓
13. Synchronize
          ↓
14. Update ERP

---

Offline Audit Workflow

When there is no network:

Enterprise Login
      ↓
Cached Authorized Session
      ↓
Local Asset Data
      ↓
LiDAR Scan
      ↓
Edge AI
      ↓
Inspection
      ↓
Realm
      ↓
Pending Sync

Once connectivity returns:

Network Restored
      ↓
Sync Queue
      ↓
FastAPI
      ↓
PostgreSQL
      ↓
ERP

---

Development Process

Phase 1 — Enterprise Requirements Analysis

We analyzed:

• Asset inspection workflows

• Field engineer requirements

• Existing enterprise systems

• Offline requirements

• AI inspection requirements

• Reporting requirements

• ERP integration

• Identity management

---

Phase 2 — iOS Architecture

The mobile architecture was established using:

• Swift

• SwiftUI

• ARKit

• RoomPlan

• Core ML

• Metal

The application was structured to separate UI, spatial processing, AI, local storage, and backend communication.

---

Phase 3 — LiDAR & Spatial Scanning

ARKit and RoomPlan were integrated to provide 3D environmental understanding.

---

Phase 4 — AI Model Development

Custom object-detection models were developed using PyTorch.

The models were designed around the industrial components relevant to the client's environment.

---

Phase 5 — Edge AI

Compatible AI models were integrated into Core ML for on-device inference.

---

Phase 6 — Offline Architecture

Realm was implemented for encrypted local persistence and synchronization.

---

Phase 7 — Backend Development

Python and FastAPI were used to create the backend API layer.

---

Phase 8 — gRPC Services

gRPC was introduced for efficient internal communication between appropriate backend services.

---

Phase 9 — Reporting

Dynamic PDF audit-report generation was implemented.

---

Phase 10 — ERP Integration

SAP and Oracle integration layers were developed around the internal inspection data model.

---

Phase 11 — Enterprise Authentication

Okta OAuth 2.0 and SAML SSO were incorporated into the enterprise authentication architecture.

---

Phase 12 — Security & Performance

Security, offline operation, AI inference speed, spatial processing, synchronization, and backend performance were optimized.

---

Phase 13 — Testing

Testing covered:

• LiDAR scanning

• RoomPlan

• ARKit

• AI detection

• Core ML

• Metal processing

• Offline inspections

• Synchronization

• PDF reports

• SAP integration

• Oracle integration

• OAuth

• SAML SSO

• Role permissions

• API security

• Database operations

---

Key Technical Challenges

Challenge 1 — Combining LiDAR, AR & AI

Spatial scanning and AI image processing are computationally intensive.

The architecture therefore needed to coordinate:

ARKit + RoomPlan + Core ML + Metal

without compromising the mobile user experience.

Challenge 2 — Remote Industrial Environments

Field engineers cannot always rely on network connectivity.

Offline-first architecture was therefore treated as a core feature rather than an optional enhancement.

Challenge 3 — Real-Time Edge AI

AI inspection needed to provide useful feedback without requiring every image to travel to the cloud.

Core ML provided the edge inference layer.

Challenge 4 — Enterprise ERP Integration

SAP and Oracle have complex enterprise data structures.

An integration layer was therefore used to isolate ERP-specific mappings from the mobile application.

Challenge 5 — Enterprise Identity

The platform needed to work with organizational identity infrastructure.

Okta OAuth 2.0 and SAML SSO provided a foundation for centralized enterprise authentication.

---

Business Impact

The Enterprise AI Field Asset & Equipment Auditor provides the UK-based organization with a unified digital workflow for field inspection and asset auditing.

The solution can help organizations:

• Digitize field inspections

• Reduce manual documentation

• Identify equipment components faster

• Capture 3D spatial information

• Perform AI-assisted inspections

• Continue working offline

• Synchronize remote inspection data

• Generate audit reports automatically

• Connect inspection records with ERP systems

• Improve asset visibility

• Standardize inspection workflows

• Maintain structured inspection histories

The central transformation was:

Manual field auditing → AI-assisted spatial inspection → automated enterprise asset intelligence.

---

Why Swift & SwiftUI Were Used

Swift provides a native foundation for iOS development, while SwiftUI enables modern declarative user-interface development.

For an application involving:

• Camera interaction

• LiDAR

• AR

• AI

• Offline data

• Enterprise workflows

native iOS development provides direct access to Apple's hardware and platform capabilities.

---

Why ARKit & RoomPlan Were Used

ARKit provides spatial understanding and augmented-reality capabilities, while RoomPlan provides higher-level room-scanning functionality.

Together they allow the application to move beyond conventional 2D photographs and create a structured understanding of physical spaces.

---

Why Core ML Was Used

Core ML allows machine-learning models to execute directly on supported Apple devices.

For field inspection, this provides:

• Low-latency inference

• Offline AI capability

• Reduced cloud dependency

• Better responsiveness

• Potentially improved data minimization

---

Why Metal Was Used

Metal provides access to Apple's GPU architecture.

For computationally intensive spatial and AI-related workloads, GPU acceleration can improve processing performance and responsiveness.

---

Why FastAPI Was Used

FastAPI provides a lightweight, high-performance Python API framework.

It was suitable for exposing:

• Inspection APIs

• Synchronization endpoints

• Report generation

• AI services

• ERP integration services

---

Why PyTorch Was Used

PyTorch provides a flexible framework for custom object-detection model development.

It was used to build AI models capable of recognizing equipment components relevant to the field inspection environment.

---

Why Realm Was Used

Realm provides a local database architecture suitable for mobile applications requiring persistent data access.

For this project it was used for:

• Encrypted local storage

• Offline inspection records

• Pending synchronization

• Local application state

• Auto-sync capabilities

---

Why PostgreSQL Was Used

PostgreSQL provides reliable persistent storage for the centralized enterprise inspection platform.

It is suitable for:

• Asset records

• Inspection histories

• AI findings

• Audit reports

• Users

• Facilities

• Synchronization metadata

---

Why Okta Was Used

Enterprise applications frequently need centralized identity management.

Okta provides a foundation for:

• OAuth 2.0

• SAML SSO

• Enterprise identity

• Access control

• Centralized authentication policies

---

Future Expansion

The platform can be expanded with:

• AI predictive maintenance

• Automated defect severity detection

• Digital twin generation

• 3D asset visualization

• AR maintenance instructions

• AI maintenance recommendations

• Voice-based inspection notes

• Automated work-order creation

• Predictive asset failure models

• Computer vision anomaly detection

• Drone inspection integration

• IoT sensor integration

• Digital maintenance history

• Multi-site asset intelligence

• AI-generated compliance summaries

• Enterprise analytics dashboards

A future predictive-maintenance workflow could be:

Asset
  ↓
Historical Inspections
  ↓
AI Analysis
  ↓
Maintenance Patterns
  ↓
Risk Prediction
  ↓
Recommended Maintenance
  ↓
SAP / Oracle Work Order

---

Technology Stack

iOS / Frontend

• Swift

• SwiftUI

• ARKit

• RoomPlan API

• Core ML

• Metal

AI / Computer Vision

• PyTorch

• Custom Object Detection Models

• Core ML

• Edge AI

• Image Analysis

Backend

• Python

• FastAPI

• gRPC

• REST APIs

Databases

• Realm

• PostgreSQL

Cloud

• AWS GovCloud

Enterprise Identity

• Okta

• OAuth 2.0

• SAML SSO

Enterprise Integration

• SAP ERP

• Oracle ERP

Core Features

• LiDAR 3D Scanning

• Room Scanning

• Asset Identification

• AI Component Inspection

• Offline Field Auditing

• Offline Synchronization

• Dynamic PDF Reports

• Enterprise Authentication

• ERP Integration

• Asset History

• Inspection Management

---