Logistics

Every delayed shipment has a data trail. Start reading it.

Blissy AI builds AI systems for logistics companies — route optimisation, predictive delay detection, risk scoring, and operations automation. Built to cut cost-per-delivery and improve SLA compliance at scale.

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22%
Fuel cost reduction
94%
On-time delivery (from 71%)
62%
Fewer dispatch headcount
Sound Familiar?
Challenges we hear from Logistics teams
We've built for this exact problem set. Here's what we typically hear before we start working together.
SLA misses are costing you clients
You don't know a shipment will be late until it is. By then, the customer has already called. The damage is done.
Route planning is still manual or done with basic tools
Your dispatchers are making 300-stop route decisions in Excel. They're good, but they're not optimal.
No early warning system for disruptions
Weather events, port congestion, highway closures — you find out when the driver calls, not before.
COD reconciliation is a full-time job for 3 people
Cash collected, cash remitted, discrepancies — all happening on WhatsApp threads and spreadsheets.
Live Builds
What we've built for Logistics
Every case below is live in production. IP fully transferred to the client on delivery. We don't retain access or use your data for model training.
Route Optimisation4.1
AI Dispatch & Route Optimisation for a 500-Vehicle Fleet
A mid-size logistics company was planning routes for 500 vehicles using a combination of Google Maps and dispatcher experience. Load assignments were suboptimal and fuel cost was climbing. We built an AI dispatch layer that clusters deliveries by zone, optimises multi-stop routes with real-time traffic, auto-assigns vehicles by load type and capacity, and re-routes around disruptions as they happen.
Outcome: Fuel cost down 22%. On-time delivery: 71% → 94%. Dispatchers reduced from 8 to 3 without volume drop.
22%
Fuel cost reduction
94%
On-time delivery (from 71%)
62%
Fewer dispatch headcount
Route optimisationFleet AIReal-time re-routingLoad matching
Delay Prediction4.2
Delivery Prediction Engine — Know 48 Hours Before the Miss
A logistics provider was finding out about SLA misses from customer complaints. We built a predictive delay engine that ingests shipment data, traffic patterns, carrier reliability scores, and weather signals to flag at-risk shipments 48 hours before their delivery window, allowing proactive interventions.
Outcome: Proactive interventions prevented 67% of flagged delays from becoming SLA misses. Customer escalation calls down 58%.
67%
Flagged delays resolved
58%
Fewer escalations
48hrs
Warning window
Delay predictionCarrier scoringWeather integrationSLA intelligence
Cash Intelligence4.3
COD & Cash Reconciliation AI — Close the Books Daily
A logistics company handling COD deliveries across 15 states had a reconciliation process that took 5 days and employed 4 people full-time. We built an AI reconciliation layer that matches every delivery to cash collected, flags discrepancies in real time, generates daily cash position reports, and escalates anomalies before they compound.
Outcome: Reconciliation cycle: 5 days → same day. Discrepancy detection rate 4×. Finance headcount redeployed to growth work.
Same day
Reconciliation (from 5 days)
4×
Discrepancy detection
100%
Daily cash visibility
COD reconciliationFraud detectionCash intelligenceDaily close

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