Designing a Scalable Energy Intelligence Platform.
The Problem.
Innowatts helps energy providers to forecast demand and manage risk using AI model trained on over 52 million smart meters. But behind the scenes, their product experience was fragmented, custom-built for every client, slow to deploy, difficult to use, and hard to scale. Additionally, the company had a strong reliance on spreadsheets in order to visualise outputs from their systems.
They needed a new, unified product experience that could work out of the box for a variety of user types, from data scientists to energy brokers.
My role.
Worked closely with product managers, architects, and leading/mentoring a fellow junior designer. My responsibilities included: UX strategy & discovery, User research, Interviews, Design system, Prototyping & handoff.
Understanding our users and their Problems.
Early conversations with the product team made one thing clear: the platform had powerful capabilities, but the user experience was getting in the way. It was slow to set up, hard to scale, and different for every client.
To understand where things were breaking down, we started by mapping out the internal tools, their use cases, and the variety of user roles, forecasters, schedulers, energy traders, and key stakeholders. We also looked at how long it took to get new clients up and running, and where users were hitting friction when trying to extract meaningful insights.
A clear pattern of similar frustrations started to emerge:
“We spend more time gathering and cleaning data than actually analysing it.”
“We’re still relying on Excel and PDFs to share reports.”
Thanks to the insights from our initial explorations, we now have a better picture of which or Ideal Persona might be, and from this moment on, we designed for them…
Innowatts User Personas
The Proposal.
To fix the fragmented experience, we had to rethink the product from the ground up.
The main issue was that the outputs from the AI models were very, very big, making it impossible to be used by any web application, this is why we created the concept of InnoPods.
InnoPods are a modular and standardised data units that could plug into different forecasting tools and dashboards.
Each InnoPod would represent a complete, clean dataset, making it reusable across features and clients.
With InnoPods in mind, now we need to create a platform with an engine in which each the very big outputs form the forecasting models can be sliced in a way that ban be manageable by Web application, you can see below the representation howl the ecosystem will behave as a well oiled machine to ingest, prepare the data, generate the insights from the AI models and then using an InnoPod generator engine, slice it and create more manageable and smaller data sets.
Information Architecture.
With InnoPods as the foundation, we rebuilt the information architecture to support clarity and reuse.
Now, instead of dozens of overlapping tools, each user role would see the features and data relevant to them, and nothing more.
We have segmented the application on individual modules based on the target user and their needs, within each module, users will be able to produce the relevant InnoPod that can be used later on any of the available tools for further visualisation and analysis such as Alerting, Scenario analysis, dashboards or even allowing access from third party tools such as Tableau or Salesforce.
Designing the Experience.
We moved quickly from whiteboards → user flows → mockups → interactive prototypes in Figma. The product team agreed on working using Kamban, which made the development cycle very agressive, but it gave us the opportunity to iterate very fast with ideas and solutions. Very often we were designing in parallel with development as feedback was coming constantly.
Interactive Prototype.
Outcomes.
• 50% reduction in client implementation time.
• Onboarding time dropped from 1 week to just few hours.
• New features could be deployed in hours, not weeks.
• Upselling became easier thanks to a modular, plug-and-play platform.
This project wasn’t just a redesign. It was a reframing of the product strategy, with design leading the charge.
InnoPods gave the company a new language for thinking about its offering, and it showed how UX can create value beyond the screen.
Some Learnings.
UX can drive platform strategy, not just screens.
This project showed that design can go far beyond UI, InnoPods wasn’t just a design idea, it became the foundation for a scalable business model. We learned how to influence product thinking at a strategic level through UX.
Standardisation unlocks scale.
Custom solutions might feel flexible, but they don’t scale. By pushing for modular, reusable systems, has proven how a strong design foundation (like InnoPods + the Innowatts design system) can cut costs, speed up delivery, and make future growth easier.
Deep collaboration across roles is essential.
Working side-by-side with PMs, POs, and architects showed me how design fits into the bigger technical and business picture. I learned to speak everyone’s language and use prototypes as a tool to align and unblock.
Prototypes are communication tools, not just testing tools.
Instead of relying on static files, I’d embraced interactive prototypes to present, validate, and refine ideas with stakeholders, helping everyone see the vision early and avoid misunderstandings.
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