Smarter products
NoWatt gives manufacturers twenty years of high-resolution consumption data across thousands of appliances in hundreds of environments. The result is a real-world dataset that helps designers, service providers, and product teams understand how equipment actually behaves after deployment.
Questions manufacturers bring to us
The conversation usually starts where internal data is weakest: the gap between expected product behaviour and what actually happens across live operating environments.
Why manufacturers care
Spotting a fault isn’t enough. Manufacturers need to understand why it happened in the field, how it affects service economics, and how those insights should inform the next product generation.
Air conditioning, refrigeration, plant, and other long-running infrastructure see more variation in load, settings, intervention, and misuse after deployment than manufacturers can capture on their own.
A call-out confirms that something went wrong, but not whether the issue sits in the product, the installation, or the way equipment is being used.
Settings drift, slow degradation, and poor operating practice can make equipment more expensive to run and support long before anyone can explain why.
When evidence stays fragmented across service notes and hearsay, it is harder to decide which design changes or smart features will improve manufacturing and operating economics.
The data asset
The monitoring history matters because it establishes the dataset’s provenance. For manufacturers, the commercial value lies in that operating history: how equipment behaves across sectors, loads, settings, intervention patterns, and long-term drift in the real world.
Applications
The value is not abstract. The dataset becomes commercially useful when it improves product performance, reduces avoidable service disruption, and lowers cost of ownership.
Train products, diagnostics, and product logic on real operating behaviour rather than idealised assumptions from controlled conditions.
Separate likely product faults from installation and usage issues earlier, so engineering attention goes to the right problem first.
Use field evidence to address the patterns that drive inefficiency, callbacks, and support burden for end users.
Evaluate fit
If NoWatt is relevant to your product category, the commercial case typically comes down to four questions: what the dataset can tell you, what it can improve, why the data is credible, and whether a partnership is practical.
Data Asset
See why breadth, duration, and provenance make the operating history commercially hard to replicate.
Go to Data Asset →Applications
Explore product, diagnostic, service, and ownership-cost use cases built on real operating behaviour.
Go to Applications →Proof
Review the sectors, estates, and live deployment history that created the learning dataset.
Go to Proof →Partnership
See how a first conversation is framed around your product category, field questions, and practical commercial value.
Go to Partnership →Proof
The dataset wasn’t assembled in a lab or a demo. It was built through live deployments across multiple sectors, estates, and operating conditions over two decades. This video shows how incoming data is interpreted against that broader benchmark.
“NoWatt helps us to make educated decisions on which energy reduction projects will provide the greatest financial benefits.”
“Pizza Hut is keen to implement affordable energy-saving tools such as NoWatt, and works with AECOM to achieve this.”
“The information from ECA and NoWatt is spot on, and essential for clubs like us.”
Example environments
Case-study environments
These examples show the kinds of live operating contexts that contributed to the benchmark and make it more useful for manufacturers.
Energy management and operational prioritisation across a large hospitality estate.
Affordable energy-saving interventions supported by actionable monitoring insight.
Monitoring applied to complex sports and entertainment infrastructure.
Operational data and building-performance analysis across an education estate.
Partnership
The first conversation is usually about the equipment category, the installed-base behaviour that is hardest to explain, and whether operating-history data could create a practical commercial advantage.
Working on smarter products, lower service friction, or lower ownership cost across appliance and infrastructure.
Product behaviour in the field, recurring service drag, or the question you cannot answer well enough with internal data.
sales@nowatt.com
+44 7873 124107
