Smarter products
NoWatt owns twenty years of high-resolution operating data — how thousands of appliances actually behave across hundreds of live environments. Manufacturers licence it, and partner with us to turn it into product intelligence: diagnostics, warranty evidence, and embedded monitoring trained on real field behaviour rather than idealised lab conditions.
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.
Factory instrumentation is cheap, but it produces no operating history. Real field behaviour is expensive and slow to acquire — and a product you haven't shipped yet has none. Embedded intelligence needs field ground truth at launch, not years later. That gap is where most “smart product” roadmaps stall.
Why this is hard to build in-house
A new generation of equipment is expected to monitor itself, predict its own faults, and lower its cost of ownership. Doing that well requires models trained on how equipment actually behaves in the field — across sectors, loads, settings, misuse, and slow degradation.
A single manufacturer can only learn this from its own installed base, slowly, after products ship, through expensive post-installation monitoring. That is the cold-start problem: the intelligence is most valuable at launch, but the data to train it only accumulates years afterwards.
NoWatt closes that gap. Twenty years of real cross-sector operating history lets a manufacturer ship embedded monitoring trained on genuine field behaviour from day one — instead of waiting for its own fleet to generate it.
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.
What we provide
NoWatt owns this operating-data asset and licenses access to it on a non-exclusive basis. The data is raw and supports many applications, so we work alongside your team — typically a short-to-medium-term partnership — to shape it into the capability you need, whether that is fault and diagnostic models, warranty evidence, or product-design insight. The data remains NoWatt’s; you gain an applied, evolving capability built on it.
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 genuine product faults from installation and usage issues with field evidence — directing engineering attention to the right problem and cutting avoidable warranty drag and call-outs.
Use field evidence to address the patterns that drive inefficiency, callbacks, and support burden for end users.
Embedded monitoring trained on real field behaviour keeps you present across the operating life of the product — predictive service, efficiency assurance, parts, and renewal — converting a one-time equipment sale into a recurring service relationship with your client.
Evidence of how equipment actually consumes energy after deployment supports efficiency claims, design decisions, and compliance as energy-performance and refrigerant regulation tighten.
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 a partnership built on our operating history could create a practical commercial advantage. Because the data is raw and multi-purpose, early work is collaborative: we apply the asset to your product category alongside your team, and prove value before scaling.
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
