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Margins in logistics are made in minutes and kilometers. We build the automation, optimization, and visibility layer that gets both back.
The reality on the ground
Logistics businesses run on thin margins and thick WhatsApp groups. Orders arrive as messages and get retyped, routes are planned from memory, customers call to ask where their shipment is, and the month-end compliance paperwork consumes the back office for days.
Every one of those is a solved problem: parsed order intake, algorithmic routing, live tracking links, and self-compiling reports. The operators who adopt them handle twice the volume with the same team.
What we deliver
WhatsApp and email orders parsed, validated against stock and credit, and pushed to your ERP — humans handle exceptions only. Explore this practice →
Algorithmic routing and load planning that cuts kilometers and fits more drops per vehicle. Explore this practice →
Barcode-driven WMS with locations, picking, and dispatch — stock that is right because the process makes it right. Explore this practice →
Tracking links, dispatch notifications, and client portals that end the where-is-my-order call. Explore this practice →
Fuel, maintenance, detention, and per-trip profitability in dashboards, not diesel diaries. Explore this practice →
Excise, GST, and client-SLA reports compiled automatically for review, not assembled at midnight. Explore this practice →
Results we target
Targets based on engagements of this shape — actual goals are agreed per project, upfront, in writing.
Order-to-dispatch automation gave a beverage distributor 22 hours a week back. Read the full sample case study for this industry.
Read case studyRepresentative scenarios
Honesty note: these are illustrative engagement scenarios — problem patterns we solve and the results a well-run engagement targets. They are not real client names or audited figures, and they'll be replaced by documented case studies as projects complete.
Client profile: Licensed beverage distributor, 900+ outlets.
The problem: Three staff re-typed phone and WhatsApp orders into billing all day, producing 6–8 dispatch errors a week and month-end excise crunches.
What we build: n8n pipeline parsing WhatsApp orders, validating stock and credit, pushing clean orders to the ERP, and compiling excise/GST data automatically.
Typical results: Order entry ~85% faster, entry errors near zero, 22 staff hours a week redeployed to sales.
Client profile: Delivery company, 300 vehicles.
The problem: Dispatchers planned routes from memory; drivers averaged 60 drops a day and fuel spend grew faster than volume.
What we build: Route-optimization engine in a driver app with live tracking, sequencing, and proof-of-delivery capture.
Typical results: Drops per driver up ~40%, fuel cost down double digits, customer status calls down ~60%.
Client profile: Third-party logistics warehouse.
The problem: Paper registers and tribal knowledge; stock counts never matched, and client audits were dreaded events.
What we build: Barcode WMS with bin locations, guided picking, cycle counts, and client-wise billing generated from actual activity.
Typical results: Inventory accuracy above 99%, pick times down, client audits passed from the system's own records.
Client profile: Freight brokerage.
The problem: Quotes, carrier assignment, and tracking lived in separate sheets; margins per load were guesses.
What we build: Freight CRM covering quote-to-invoice with carrier rates, dispatch, document handling, and per-load margin visibility.
Typical results: Quote turnaround in minutes, every load's margin known before booking, follow-ups automated.
Client profile: Regional trucking fleet, 120 trucks.
The problem: Fuel, maintenance, and detention costs surfaced only in monthly accounts; loss-making lanes ran for quarters.
What we build: Ops warehouse joining trips, fuel, maintenance, and billing into per-trip and per-lane profitability dashboards.
Typical results: Loss-making lanes identified and re-priced, maintenance anomalies caught early, monthly review from live numbers.
Client profile: Import-export house.
The problem: Every shipment meant re-keying invoices, packing lists, and bills of lading across systems and portals.
What we build: Document-AI extraction reading shipment documents and auto-filling the ERP and compliance filings, with human review on exceptions.
Typical results: Document handling time down ~70%, keying errors eliminated, filings ready when the shipment is.
Client profile: Cold-chain transporter.
The problem: Temperature logs were manual and gap-filled; one disputed excursion could cost a pharma client.
What we build: Sensor-fed monitoring with automated excursion alerts and client-ready compliance reports per trip.
Typical results: Excursions caught in transit not after, disputes settled from data, pharma clients retained on evidence.
Bring us the problem. We'll bring the plan, the build, and the numbers to prove it worked — agreed upfront, reported honestly.
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