How Digipal partnered with BigData Boutique to go from IoT-enabled pallets to a production-ready, hardware-agnostic asset tracking platform on AWS — fast enough to move customer deals forward.
Digipal, a UK-based supply chain technology company, partnered with BigData Boutique to build an end-to-end IoT asset tracking and analytics platform on AWS — cloud-native from day one, hardware-agnostic, and production-ready fast enough to move enterprise sales conversations forward.
Most IoT products don't fail at the sensor. They fail at the gap between raw telemetry and something a customer is willing to pay for. That gap is where Digipal needed to move fastest. The hardware story — reusable plastic pallets embedded with IoT sensors — was clear. What it needed alongside it was an AWS-native platform that could turn streams of location, movement, and environmental data into answers customers actually ask: where are my assets, where have they been, and which ones are missing or idle?
Why a Platform, Not Just Telemetry
Digipal's circular service model — durable, trackable pallets that replace single-use alternatives — depends on a continuous flow of data from assets in the wild. Plenty of vendors can ship a tracker and a feed of GPS pings. None of that alone answers an operations director's questions or closes a deal with a retailer.
The problem looks small until you list the constraints:
- Multiple IoT hardware and data providers had to plug into the same backend, with no lock-in to any single vendor.
- The customer-facing portal had to answer practical, operational questions — current location, history, idle and missing assets, utilisation patterns.
- The whole thing had to be demonstrable on a sales call, because the ability to show a working system was directly tied to closing commercial agreements.
That's a platform problem, not a sensor problem. And it's the kind of problem a startup doesn't want to solve by stitching together three vendors and a dashboard tool.
What "End-to-End on AWS" Actually Means Here
BigData Boutique came in as Digipal's primary engineering and delivery partner from the beginning — not advisory, not architectural sketches handed off to someone else to build. The deliverable was a production system.
The platform on AWS covers the full path from device to customer:
- Ingestion pipelines that normalise IoT data coming in from different hardware providers, each with their own formats and quirks.
- Post-processing and analytics layers that turn raw signals into business context: trips, dwell time, utilisation, anomalies.
- A scalable backend built to serve both real-time queries (where is this pallet right now?) and historical ones (where has it been over the past 90 days?).
- A customer-facing portal with dashboards and reports focused on the questions operations teams actually have — location, movement, and utilisation.
The architectural call that mattered most was making the platform hardware-agnostic. Locking Digipal's product to one sensor vendor would have been an easier first build and a much harder commercial future. Designing for multiple providers up front meant the platform could absorb new hardware, new geographies, and new deployment models without a rewrite.
Moving at the Speed of the Business
Early-stage product teams have to spend their time on the things only they can do — product vision, customer conversations, commercial traction. Digipal chose to do exactly that, and to put the engineering build in BigData Boutique's hands as a close delivery partner rather than as a hands-off contractor.
The practical effect: changes that came out of customer calls could go into production without waiting on hiring cycles, and the platform was always demo-ready when it mattered. As Digipal made its first technical hires, BigData Boutique continued operating as an extension of the team, helping evolve and stabilise the platform rather than handing over a black box.
The Outcome
The collaboration moved Digipal from concept to a deployable, customer-facing product in a timeframe that would have been very difficult to hit independently. In practice that meant:
- A fully operational asset tracking and analytics platform on AWS.
- Real-time asset visibility that could be shown to prospective customers as part of the sales process.
- Support for multiple IoT technologies and deployment scenarios, without re-architecting per deal.
- Faster deal cycles, with the platform directly enabling new commercial agreements.
The bigger result is positioning. Digipal got to market with a modern, cloud-native product while still building out its in-house engineering capability — instead of trading one off against the other.
Takeaways for IoT and Supply Chain Builders
A few things that are easy to underestimate when you're staring at a hardware roadmap:
- The product is the platform, not the device. Customers don't buy sensors; they buy answers to operational questions. Budget engineering effort accordingly.
- Hardware-agnostic is a commercial decision, not a technical one. Designing ingestion and modelling to absorb multiple vendors keeps your sales pipeline open and your pricing leverage intact.
- Speed-to-demo is part of the architecture. If your platform can't be shown working on a call, your deals stall. Make "always demo-ready" a non-negotiable.
- Partner where you don't yet have hires. A focused engineering partner who can build production systems lets you delay org-building until you actually know what you're hiring for.
Read the Full Case Study
The full story — including more on Digipal's circular model, the architecture choices behind the platform, and how the partnership continues to evolve — is in our customer story:
Read the full Digipal case study →
If you're building an IoT or supply chain product and need a partner that can take a platform from vision to production on AWS, get in touch. We work with teams running large-scale data platforms on AWS every day.
