New study: The Agentic AI gap
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Industrial and Embedded Engineering
Devices grow more complex while skilled staff get scarcer. We bring AI directly onto the machine, so troubleshooting and guided operation happen on the spot. No cloud required, and data stays on the device.
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first-level support at every device
0
cloud required
100%
hardware-agnostic
Qualification
These are the situations where adding features stops being enough, and the intelligence has to move onto the device itself.
A machine can be reverse-engineered. The software that reads its sensors, understands context, and guides the operator cannot.
01.Most calls turn out to be operating mistakes. A four-hour drive to correct a setting is not a service model.
02.More sensors, more interfaces, more variants. The staff who can run them confidently are getting harder to find.
03.The demo worked. Turning it into a shippable product needs data, software, and UX engineering you cannot staff internally.
04.Connected devices must comply by the end of 2027. Start late and a compliance task becomes liability exposure.
05.Capabilities
We bring intelligence onto the device itself, so the machine understands its own state and the person operating it works at the level of your most experienced user.
A design nobody can build is not a design.
Designers and embedded developers sit in the same team from day one. Hardware limits are a design input, not a discovery in month six.
Build it once, ship it ten times.
The operating concept is engineered for the portfolio, not for one device. Every product after the first inherits instead of starting over.
Your stack has to survive two decades.
Technology choices run against an explicit criteria catalogue: licensing, developer availability, maintainability. The question is not what works now, it is what you can still staff in 2045.
The lab is a bottleneck you can delete.
Hardware abstracted in software takes development off the physical rig. Distributed teams contribute, and fault scenarios nobody can safely reproduce run as automated tests overnight.
An agent, not an AI connection.
Situation recognition, decision, and action run as one system on the device. The agent reasons over device data and service history, not generic knowledge.
The device explains its own faults.
The agent diagnoses malfunctions against device data, manuals, and past service tickets, and absorbs first- and second-level support without a technician on site. It runs networked or fully local.
Guidance before the question.
The agent anticipates the user's next step and highlights the controls it needs, without a chat window. Operators without specialist training no longer need to know the answer in advance.
Use an agent only when rules run out.
Where an outcome can be expressed as rules, a rule-based system is cheaper and verifiable, and we recommend it. Before building anything, your data shows whether the intended outcome is reachable.
We spoke with several providers. Some wanted to sell us a product. Cloudflight wanted to understand our mission. That made all the difference.
Christian Steigelmann
Managing Director (OpenTechCloud)
Reference
DEBAG is one of the leading manufacturers of professional baking ovens in the bakery industry. In a rapidly changing market, the company aims to simplify the usability of all appliances for various user groups. Together, we developed a platform for DEBAG's products that enables both automatic and manual control of the processes. The design of our solution was recognized with an iF DESIGN AWARD.
Collaboration
Every engagement starts small and earns the next step. You test us on real work before the project runs at full scale.
Architecture review
A workshop clarifies requirements for a new product, an architecture review assesses an existing one. Both end with a clear recommendation.
Proof of concept
A proof of concept, an MVP, or a single module. You check whether the fit holds on content, team, stakeholders, and delivery reliability.
Iterative development
Feature by feature up to market launch, usually agile, always iterative.
Maintenance and cyber resilience
After launch, we maintain the software, scan libraries for known vulnerabilities, keep the SBOM current, and ship fixes under a service level agreement. That supports your obligations under the Cyber Resilience Act.
Trust
Book a meeting
30 minutes with one of our embedded engineering experts. We look at your device, your data, and where on-device AI cuts service costs first.
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