Case Study Decklar

A supply chain that decides for itself, in real time.

Decklar is a real-time decision AI platform for supply chains. It unifies visibility across the network and turns millions of live data points a day into automated, predictive calls. We contributed engineering on the platform, the part that reads the whole network at once and acts on it.

Client
Decklar
Sector
Supply chain · AI
Engagement
Engineering
Stage
Live · enterprise
Platform
Web · Cloud
The Decklar supply-chain decision platform showing unified network visibility and real-time AI decisions across shipments and reusable assets
One platform: live visibility across the supply-chain network, with AI decisions running on top.
Decklar reports
$1 → $11Reported ROI
25%Lower supply-chain cost
80%Faster decisions
≤90 daysTo first value

Supply-chain teams see their network in fragments.

A global supply chain runs across dozens of systems that do not talk to each other. The planning tool holds one version of the truth, the carriers hold another, the warehouse holds a third.

So teams stitch the picture together by hand, in spreadsheets, after the fact. A shipment slips, an asset goes missing, a port backs up, and the people who could act find out late. Decisions arrive after the moment to make them has passed, and late decisions cost money.

The harder part is volume. A network this size produces millions of data points a day, and they keep changing. Any view that wants to be trusted has to read all of it, in real time, and stay correct while it moves.

One live view of the network, with AI making the call.

We contributed engineering on Decklar's platform across the full stack. The product pulls shipment and asset data from the systems that hold it, normalizes it into one model, and runs decision AI on top of the live stream. Visibility spans modes, from ocean and air to road and rail, and reusable assets like bins, totes, containers and railcars. On that unified feed, predictive models forecast and act: dynamic replenishment, detention and demurrage reduction, automated quality release, security-risk monitoring.

What that meant in practice

  • Integration that draws shipment and reusable-asset data from many enterprise systems into one model
  • Unified, real-time visibility across ocean, air, road and rail and across reusable assets
  • ML services for predictive analytics, forecasting and automated decisions on the live feed
  • Scalable streaming infrastructure built to hold millions of data points a day
The Decklar product showing unified supply-chain visibility: shipment tracking, temperature and humidity charts, and a live map
Unified visibility

Track a shipment and its condition on one screen.

A shipment carries more than a location. Decklar reads its route, temperature and humidity together, so a planner sees the cargo and its risk in one place. We wired the live map, the condition charts and the tracking feed into a single view that updates as events arrive.

One view · location, condition and route
Inside the platform
The hardest part

Dozens of enterprise systems describe the same shipment in different shapes, at different times, and the events never stop arriving. We had to fold all of that into one model that stays correct under millions of updates a day, so the AI reads a network that is true right now rather than true an hour ago.

Ego Eimi Engineering note · the build

Live in enterprise, with the numbers the vendor stands behind.

Decklar runs in production for enterprise supply chains. Decklar reports a $1 to $11 ROI for a Global 2000 customer, 25% lower supply-chain cost, 80% faster decisions, and first value in 90 days or less.

Decklar states adoption among Global Top 10 enterprises across seven sectors, from pharma and CPG to chemicals, electronics, automotive, logistics and food and beverage. These figures are Decklar's stated results, not outcomes we measured. What we own is the engineering: a platform that reads the whole network in real time and acts on it.

AI Real-time analytics Data integration Cloud Web app
Our role
Engineering
Team
Lead + specialists
Model
Product build
Status
Live, enterprise
Project partner Jimit Raval

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