The vehicle.
We build the systems that make the strategy work in the real world.
The Data Duck's Technology and AI practice designs and builds platforms, AI tools, data systems, CRM workflows, commerce infrastructure, and internal products that are aligned to the business they are meant to serve.
The work begins before the first line of code, with the operating question the system has to answer, the people who will use it, the constraints it must survive, and the business outcome it has to support.
Where the work starts
Clients usually come to us when they need to launch a new platform, rebuild a fragile website, introduce AI into a workflow, connect CRM and commerce, create an internal operating system, automate a manual process, or turn a strong business idea into a product that can actually scale.
The common issue is rarely just code. It is the gap between business intent and technical specification. Our job is to close that gap before the build begins, and then build the system properly.
Where cost is decided
The cost, speed, and reliability of a technology project are largely shaped in the first few weeks. That is when the architecture, scope, integration boundaries, data flows, and failure modes are decided.
Many failed builds are not badly coded. They are correctly executed against the wrong specification. The specification reflects a business question that was not properly resolved.
We begin with the operational question the system has to answer. Who will use it? What does it need to do under pressure? What data does it depend on? What happens when the input is messy? What should be automated, and what should stay human? What must the internal team be able to govern, extend, or change later? Only then do we build.
The standard
A proof of concept that works on clean data in a controlled environment is not the same as a system that has to run every day inside a real business. Production-grade means the system can tolerate messy inputs, changing workflows, third-party dependencies, regulatory constraints, user error, traffic spikes, and the operational expectations of people who depend on it.
Our reference point is not a demo. It is working infrastructure. Gridfuse, built for TrailStone and now part of Engelhart Commodities, manages real-time imbalance risk across European power markets and Japan. The same build standard informs the consumer platforms, AI tools, safety infrastructure, and internal systems we design for clients.
Partner · Technology & AI
He has built consumer apps, enterprise systems, and trading software, along with an unhealthy number of Shopify sites. That range matters, because production-grade means something different for a platform managing imbalance risk across power markets than it does for a store that has to take orders every day, and he has had to make both survive contact with the real world. When a client hands over a production system, they are handing it to a named owner who stays accountable for how it behaves once it is live and under load.
What we build
Custom AI systems designed around the business's actual data, workflows, users, and constraints. We build systems that operational owners can interrogate, govern, and improve, rather than opaque tools that no one fully understands.
Websites, apps, dashboards, internal tools, commerce systems, and enterprise platforms built across architecture, backend, frontend, and integrations. The goal is not just launch. The goal is a system that can absorb the next stage of growth without needing to be rebuilt.
The systems layer beneath acquisition, retention, sales, and service: CRM workflows, attribution logic, dashboards, targeting infrastructure, spend intelligence, and data flows. At a certain level of sophistication, growth is no longer only a creative problem. It becomes an infrastructure problem.
Advisory work before the build begins: what to build, what to buy, what to defer, what to integrate, and what the system must be ready for over the next three years. The output is a build plan with the architectural decisions and trade-offs clearly recorded.
On AI
Most boards are asking some version of the same question: where can AI create genuine operating leverage, and where is it a distraction dressed in current language? We answer that question for the specific business, not for the trend. In some cases, AI is the right layer to build. In others, the real problem is poor data quality, unclear workflows, weak integration, or a process that has not been defined well enough to automate.
When the case for AI does hold up, we cover the architecture, training and evaluation discipline, integration model, governance, and ongoing maintenance cost before the build begins. AI becomes uneconomic in predictable ways. We prefer to identify those risks before money is spent.
How it runs
We begin with the operational question the system has to answer.
Only then do we build.
From there, we map users, workflows, data sources, integrations, edge cases, governance requirements, and scale expectations. We then define the build plan: what is essential for version one, what should be deferred, what can be bought, what must be custom, and what the internal team will need to manage later.
The build is then executed across architecture, design, engineering, QA, deployment, documentation, and handover. The deliverable is working software with the decisions behind it on record.
Selected work
Problem
Renewable energy trading requires real-time management of imbalance risk across volatile power markets.
Build
A virtual power plant platform handling renewable energy trading, imbalance risk optimisation, and ancillary services market access across European power markets and Japan.
Why it matters
The platform now operates inside one of the larger commodities trading houses globally.
Problem
An AI-native consumer product needed a full platform, not just a front-end experience.
Build
Platform architecture and end-to-end build, including the data and model layer beneath the user-facing experience.
Problem
Safety reporting infrastructure requires reliability, sensitive data handling, and national-scale usability.
Build
Technology infrastructure for a safety reporting platform operating across multiple countries.
Problem
A large financial services brand produces a continuous volume of content across products, campaigns, and communications, which is hard to manage, govern, and publish consistently through scattered tools.
Build
An end-to-end content management and publishing system, covering content creation, review and approval workflows, governance, and publishing across the brand's channels.
Problem
A high-output content and communications operation needs a single system to manage and publish across its properties without engineering becoming the bottleneck.
Build
A content management and publishing system built to handle the volume, structure, and editorial workflows the organisation runs on.
The vehicle has to survive the road. Technology is only useful when it works inside the business, not just inside the demo.
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