Case studies
AI-driven automation and measurable impact, delivered across freight management, transportation and warehousing worldwide. 250+ engagements, 12 countries, 4 continents.
Quotes were built by hand from rates scattered across carriers, email and spreadsheets — slow turnaround lost deals to faster competitors. We built an AI rate management & instant quoting engine.
Carrier choice ran on habit, not performance data, inflating cost. We built an AI carrier selection & load-matching engine scoring carriers on cost, capacity and reliability.
Shipping and customs documents were keyed by hand from BOLs, invoices and packing lists, triggering clearance delays. We built an AI document & customs automation layer with HS-code suggestions.
Customer insights were assembled by hand in Excel, so decisions lagged the operation. We layered an AI-enabled customer intelligence engine over live operational data.
Automation stalled on data fragmented across systems and formats. We normalized the data foundation first, then automated the highest-effort manual tasks with AI/ML workflows.
Visibility was fragmented across carriers and modes, so delays were discovered by the customer first. We built a predictive ETA & exception-management layer across every leg.
Support stayed fully manual as parcel volumes scaled, tying headcount directly to volume. We built an autonomous AI customer-operations layer that triages, extracts and responds automatically.
Customers had no reliable self-serve view of their shipment, driving a constant stream of status enquiries. We built an ML-powered predictive ETA & tracking layer with proactive alerts.
Static routes rarely reflected real road conditions, inflating cost per drop. We built an AI route optimization & dispatch engine that continuously re-sequences against live traffic.
Carrier invoices were checked manually, if at all, letting overcharges slip through. We built an AI freight-invoice audit & reconciliation layer matched against contracted rates.
Maintenance was reactive, so roadside breakdowns caused unplanned downtime. We built a telematics-driven predictive maintenance model that services components before they fail.
Data was fragmented across TMS, carrier and customer systems, blocking automation. We built a unified control-tower layer integrating every source into one real-time view.
SKUs were stored with little regard for velocity, so pickers walked excessive distances. We built an AI slotting & storage optimization engine with continuous re-slotting.
Inventory levels were set by rules of thumb, tying up working capital and space. We built an AI demand forecasting & inventory optimization layer tuning reorder points per SKU.
A legacy, single-tenant platform throttled onboarding and couldn't scale to new clients. We rebuilt it as a scalable, multi-tenant WMS and packaged it as a sellable SaaS product.
Shift staffing was set by guesswork, driving idle time and overtime. We built an AI labour forecasting & task-allocation engine matching headcount to forecast workload by zone.
Inbound orders and documents were keyed in manually from Excel, PDFs and email, creating peak-season backlogs. We built an AI document & order automation layer feeding straight into the WMS.
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