AI Exception Management in Logistics — How Intugine Detects, Tickets, Calls and Escalates Automatically

The Exception Lifecycle in Logistics — Where Time Is Lost

Every logistics exception — a vehicle that halts unexpectedly, an ETA that is about to breach, a route deviation, a tracking gap — has a lifecycle. Detection. Triage. Action. Escalation. Resolution. In a manual control tower, each step in this lifecycle requires a human decision. Time is lost between each step: the exception happens, a human eventually notices it, decides what to do, makes a call, waits for a response, decides to escalate, calls the transporter, waits again.

In a network running 2,000 daily trips, the accumulated time lost between exception occurrence and resolution — across hundreds of daily exceptions — directly translates to SLA failures, cascading delays, and client escalations. Intugine's AI exception management layer compresses the full exception lifecycle to minutes, not hours.

Step 1: Detection — Continuous Monitoring Against the Exception Matrix

Every active trip in Intugine is monitored against a configured exception matrix. The matrix defines, per lane and per movement type:

• Halt threshold: How long a vehicle can stop before it is flagged (e.g., 45 minutes on national linehaul, 20 minutes on city distribution)
• ETA breach trigger: When projected arrival crosses into SLA risk zone (e.g., ETA within 2 hours of the static corridor window)
• Deviation tolerance: How far off-route a vehicle can go before flagging (e.g., 5 km on highway, 1 km on city route)
• Tracking gap threshold: How long a vehicle can go without a location ping before it is flagged as not-tracked
• Hub dwell threshold: How long a vehicle can sit inside a facility before flagging

When any threshold is crossed, the system creates an exception ticket immediately — no human monitoring required.

Step 2: Ticket Creation — Classification and Prioritisation

The ticket is not just an alert. It is a structured record containing:

• Exception type and sub-type (e.g., Halt → Extended / Halt → Suspicious Location)
• Priority level (P1–P5 based on SLA risk, cargo type, and time to delivery window)
• Full vehicle context: registration, transporter, GPS vendor, last known location, speed
• Trip context: origin, destination, scheduled arrival, current ETA, SLA window, time remaining
• Historical context: this vehicle's last 5 exceptions, this transporter's exception rate on this lane

A P1 ticket (SLA breach imminent, high-value cargo) triggers an immediate automated call. A P3 ticket (ETA at risk but 4+ hours to window) surfaces in the exception queue for human review. Priority-based routing ensures executive attention is focused where it matters most.

Step 3: AI-Initiated Driver Call

For P1 and P2 exceptions, Intugine's AI initiates an automated outbound call to the driver on the registered mobile number. The call is:

• Exception-specific: A halt call sounds different from an ETA breach call. The script is calibrated to the exception type.
• Response-capturing: The driver's verbal response is captured, transcribed, and logged into the ticket as a remark automatically.
• Action-branching: Driver says "tyre change, 30 minutes" → ticket updated, ETA recalculated, no further escalation unless 30 minutes passes without movement. Driver says "accident" → P1 escalation to human executive immediately.

Step 4: Escalation Workflow

If the driver does not respond after two call attempts, or if the response indicates an issue beyond driver-level resolution, the system escalates automatically:

1. Driver (T+0): AI initiates call, captures response
2. Transporter manager (T+15 min, no response): Automated call to transporter with exception details and driver no-response status
3. Client control tower (T+30 min, transporter no response): Escalation notification to client-side operations with full context trail
4. Human executive (complex resolution needed): Ticket escalated with full history — executive picks up a pre-diagnosed case, not a cold call

Every escalation step is timestamped. The audit trail is complete regardless of whether the exception was resolved by AI or human.

Step 5: Resolution and Closure

When an exception is resolved — vehicle resumes movement, ETA returns to SLA window, hub dwell ends — the system logs the closure automatically with resolution time and resolution method (AI-resolved vs human-resolved). This data feeds the analytics layer: which exception types are resolved by AI, which require humans, resolution time by exception type and transporter, and shift-level performance metrics.

Exception Types Intugine Manages Automatically

Exception Type	Detection Method	AI Action	Escalation Trigger
Extended halt	No movement > threshold	Driver call — capture reason	No response in 15 min
ETA breach risk	ETA crosses SLA risk window	Alert + driver call if P1	ETA confirmed breach
Route deviation	Position off expected corridor	Driver call — verify cause	Deviation continues 30+ min
Tracking gap	No ping > threshold	Driver + transporter call	No response in 20 min
Hub dwell breach	Vehicle in facility > threshold	Hub ops alert	Dwell exceeds 2x threshold
Suspicious halt	Halt in red zone location	Immediate P1 escalation	Human review required
Two-driver non-compliance	Single SIM pattern detected	Transporter alert	Pattern continues 2+ hours
Document expiry	E-way bill / RC / fitness alert	Ops team notification	Expiry within 4 hours