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Every Vehicle. Every Stop. Every Service. Accounted For.
Introduction
Loretto AI Labs developed ASAES, an advanced AI-powered field operations platform designed to transform how businesses monitor, manage and verify their daily transport and customer-service activities.
Developed around the operational requirements of a commercial service fleet in Melbourne, Australia, ASAES integrates real-time vehicle telemetry, customer geofencing, route intelligence, automated service verification, video evidence, artificial intelligence and predictive operational analytics into a unified management environment.
Unlike conventional GPS tracking solutions that merely display vehicle locations on a map, ASAES goes significantly further. The platform establishes an intelligent relationship between scheduled customer visits, actual vehicle movements, time spent at customer premises, service evidence, operational exceptions and customer complaints.
By continuously correlating these information sources, ASAES enables businesses to move from reactive fleet supervision to proactive, evidence-based operational management — a scalable architecture designed to improve service accountability, reduce missed customer visits, strengthen operational transparency and give management actionable intelligence across the entire field-service network.
Project Overview
The initial ASAES implementation was designed around a commercial service operation, with an architecture built to expand across additional vehicles, service territories, depots and customer locations.
| Project component | Scope |
|---|---|
| Initial operating fleet | 2 commercial vehicles |
| Daily customer visits | Approx. 30–45 stops per route |
| Operating schedule | Monday–Friday |
| Typical daily operation | 9–10 hours |
| GPS hardware | Teltonika FMC003 |
| Telemetry architecture | Direct device-to-platform ingestion |
| Location intelligence | Customer-specific geofencing |
| Video infrastructure | Four-camera MDVR architecture |
| Management platform | Centralised web dashboard |
| AI architecture | Multi-agent operational intelligence |
| Notifications | Automated alerts and WhatsApp integration |
| Core objective | Independent verification of field-service execution |
From vehicle-mounted hardware to the operations command centre, with business rules and management approval governing high-impact decisions.
Fleet Vehicles
Teltonika FMC003 GPS devices and four-camera MDVR units
Secure Telemetry Ingestion
Direct device-to-platform data with timestamp correlation
Geofencing & Route Engine
Customer geofences, stop detection and route reconciliation
Evidence Correlation & AI Agents
Service verification, complaints, prediction and anomaly detection
Business Rules & Approval
High-impact decisions governed by rules and management approval
Operations Command Centre
Dashboard, WhatsApp alerts, investigations and executive reporting
Direct Vehicle Telemetry & Live GPS Intelligence
Continuous operational telemetry straight from the vehicle — without depending on manual driver reporting.
ASAES integrates directly with vehicle-mounted Teltonika FMC003 GPS tracking devices. The platform receives, processes and stores vehicle-generated information, creating a historical record of fleet activity.
The result is an independent operational information source that can be correlated against planned customer-service activities.
Commercial valueImproved fleet visibility, stronger operational accountability and a reliable foundation for advanced route and service intelligence.
Intelligent Customer Geofencing
Actual vehicle movement analysed against verified customer coordinates — not a driver tapping "arrived".
Customer-specific geofences determine when a service vehicle reaches, enters, remains at or departs from a customer location. For businesses operating dozens or hundreds of daily stops, independently confirming every visit is a major administrative challenge — ASAES automates much of it.
Managers can see which customer locations were visited, when vehicles arrived, how long they remained and whether each visit matched the planned route. Geofence events establish location-based evidence, which is then combined with other operational records to determine whether the required service was actually performed.
AI-Powered Route Intelligence
Raw GPS telemetry turned into route-performance intelligence.
ASAES compares scheduled routes against actual vehicle movements and identifies deviations, irregularities and service exceptions across every route and every day.
Predictive route optimisationAI analyses historical service patterns — customer service times, repeated delays, route sequencing, operating patterns and the geography between customers — to help operators improve fleet utilisation, reduce unnecessary travel and allocate resources more effectively.
Automated Pickup & Delivery Verification
A truck being near a customer is not proof the service was done. ASAES correlates multiple evidence sources to find out.
Commercial service businesses frequently face reports of missed collections, incomplete deliveries or services allegedly performed at the wrong time. Traditional systems can establish that a truck was near a customer's premises, but that alone does not prove the required service was completed.
ASAES evaluates eight evidence sources together and produces an evidence-supported operational assessment rather than relying on a single GPS event — giving management a defensible, traceable record of daily service execution.
Each visit is assessed against what should have happened, where the vehicle was, how long it stayed and what evidence exists.
Step 1: Scheduled Visit
What should happen
Step 2: GPS & Geofence
Where the vehicle was
Step 3: Stop Duration
How long it stayed
Step 4: Records & Video
What evidence exists
Step 5: Service Verification Agent
Correlates everything
Step 6: Classified Outcome
Traceable assessment
Four-Camera Intelligent Video Evidence System
A four-camera Mobile Digital Video Recorder (MDVR) architecture, time-aligned with GPS and service events.
The camera infrastructure captures relevant vehicle and operational activity while maintaining a relationship with GPS timestamps and service events — strengthening operational visibility and providing additional evidence for investigating service-related incidents.
When a customer reports a missed pickup, the Computer Vision Agent can examine available footage alongside the scheduled visit, GPS location, geofence activity and stop duration, creating a stronger basis for investigating the incident and determining the right corrective action.
| Camera | Primary purpose |
|---|---|
| Camera 1 | Forward-facing road and travel recording |
| Camera 2 | Driver cabin and operational safety monitoring |
| Camera 3 | Vehicle loading and service activity |
| Camera 4 | Rear or additional vehicle-area coverage |
Video processing and access operate under applicable workplace surveillance, privacy and data-retention requirements.
Multi-Agent Artificial Intelligence
At the centre of ASAES is a coordinated network of specialised AI agents. Rather than relying on a single general-purpose model, the platform distributes operational responsibilities across agents that analyse information, identify risks and generate recommendations — all coordinated by a central Master Operations AI Supervisor.
The supervisor evaluates agent findings and prioritises operational exceptions. High-impact decisions remain subject to defined business rules and appropriate management approval, and the architecture keeps an audit history of relevant AI recommendations and operational actions.
Specialist agent network · 17 agents
Operating principle
Agents recommend. High-impact decisions are checked against defined business rules and remain with accountable managers to approve.
Selected agent
High-impact decisions remain subject to defined business rules and appropriate management approval.
Business Rules & Management Approval
High-impact decisions checked against defined business rules and approved by management
Operational Actions & Notifications
Alerts, WhatsApp notifications, investigation summaries and reports
Audit Trail
Recommendations, approvals and actions recorded for traceability
| Agent | Responsibility |
|---|---|
| Master Operations AI SupervisorSupervisory | Coordinates all agents and operational priorities |
| GPS Intelligence AgentLocation & Route | Analyses vehicle telemetry and movement |
| Geofencing AgentLocation & Route | Monitors customer arrivals and departures |
| Route Intelligence AgentLocation & Route | Evaluates planned-versus-actual routes |
| Service Verification AgentService Assurance | Correlates evidence to assess service execution |
| Missed-Service Detection AgentService Assurance | Identifies potentially missed customer visits |
| Late-Service Prediction AgentService Assurance | Detects and forecasts service delays |
| Computer Vision AgentService Assurance | Analyses available operational video |
| Customer Intelligence AgentCustomer | Evaluates customer-specific service history |
| Complaint Analysis AgentCustomer | Investigates complaints using operational evidence |
| Fleet Performance AgentFleet & Prediction | Analyses vehicle and route productivity |
| Driver Operations AgentFleet & Prediction | Evaluates relevant operational patterns |
| Anomaly Detection AgentFleet & Prediction | Identifies unusual activity and conflicting information |
| Predictive Operations AgentFleet & Prediction | Anticipates delays and service risks |
| Notification AgentGovernance & Reporting | Coordinates alerts and communication |
| Data Integrity AgentGovernance & Reporting | Monitors telemetry and data consistency |
| Audit & Compliance AgentGovernance & Reporting | Maintains operational traceability |
| Reporting Intelligence AgentGovernance & Reporting | Generates management insights and reports |
Agents analyse and recommend; business rules and management approval govern high-impact decisions; every step is recorded.
Master Operations AI Supervisor
Coordinates all agents and sets operational priorities
Specialised AI Agent Network
17 agents across location, service assurance, customer, fleet and governance
Business Rules & Management Approval
High-impact decisions checked against rules and approved by management
Operational Actions & Notifications
Alerts, investigation summaries, reports and workflow actions
Audit Trail
Recommendations, approvals and actions recorded for traceability
Intelligent Customer Complaint Management
When a customer reports a missed pickup, late delivery or incomplete service, the evidence is already assembled.
The Complaint Analysis Agent correlates each complaint against historical operational records and prepares an evidence-supported investigation summary for management — reducing the time spent manually reviewing GPS systems, spreadsheets, service schedules and video recordings.
Because every complaint is linked to its evidence, recurring service problems become visible, and businesses can address their underlying operational causes rather than handling each dispute in isolation.
Complaints are matched to the operational record automatically, so investigations start with evidence rather than a search.
Step 1: Complaint Received
Missed, late or incomplete
Step 2: Evidence Assembled
Schedule, GPS, geofence, video
Step 3: Complaint Analysis Agent
Correlates and assesses
Step 4: Investigation Summary
Prepared for management
Step 5: Resolution & Root Cause
Tracked and analysed
Automated Alerts & WhatsApp Integration
An event-driven notification architecture so managers act on exceptions without watching a live dashboard all day.
The platform supports integration with WhatsApp Business messaging services to deliver relevant operational notifications to authorised personnel. Notification rules, escalation levels and recipient permissions are configurable according to business requirements.
Centralised Operations Command Centre
Vehicle telemetry, customer geofencing, route intelligence, service verification and AI-generated insights in one management platform. The views below are illustrative mock-ups of the ten dashboard modules — not live data.
Vehicle locations, connectivity and current movement.
Route North
Route South
Illustrative mock-up. Customers, sites, vehicles and values are generic examples to show the interface — not records from a live deployment.
Predictive Operations & Continuous Improvement
ASAES is designed not only to record past events but to help businesses anticipate what comes next.
By analysing historical GPS telemetry, customer service durations, route patterns and operational exceptions, the AI architecture identifies recurring inefficiencies and helps management improve route planning, resource allocation and customer-service reliability.
Data Security, Auditability & Operational Resilience
Secure data-management principles appropriate for business-critical operational infrastructure.
ASAES is designed to preserve the integrity of operational records and provide traceable evidence for service investigations and management reporting.
Commercial Applications
The ASAES architecture can be adapted to multiple industries.
Verify recurring customer pickups and deliveries while improving route accountability.
Monitor vehicle activity, delivery schedules and operational exceptions.
Verify scheduled collections and investigate missed-service complaints.
Improve route visibility and customer delivery performance.
Monitor field-service teams and verify scheduled customer visits.
Correlate technician or vehicle visits against planned service activities.
Consolidate fleet intelligence and operational reporting across multiple service territories.
Why Loretto AI Labs?
Conventional GPS tracking tells businesses where their vehicles are. Loretto AI Labs takes the next step by connecting vehicle movement with customer requirements, service execution, operational evidence and artificial intelligence.
ASAES demonstrates how these capabilities combine to solve complex operational challenges in distributed field-service networks — and our solutions can be customised to existing fleets, operational procedures, customer networks and enterprise software environments.
Managing a fleet should not require constant manual supervision, disconnected spreadsheets and time-consuming investigations. Loretto AI Labs helps businesses transform traditional field operations into intelligent, connected and accountable service networks.
Whether you manage five vehicles or a large multi-depot fleet, our AI and IoT engineering capabilities can be adapted to your operational requirements. Contact Loretto AI Labs to discuss a customised implementation.