About
Out of the lab, into the field.
Elysautus Solutions is an R&D practice focused on one thing: taking promising Edge, AI, and IoT concepts and turning them into prototypes that survive real environments — not just conference demos and slide decks.
The playbook behind that work was first developed at Ryerson in Toronto and replayed with industry partners in Finland. Today, I use it to help teams move from lab rigs and conceptual diagrams to pilot-ready systems you can deploy, observe, and iterate on.
Story
The R&D playbook: learned at Ryerson, replayed in Finland
Same pattern, different environments: prove it in the lab, then make it survive outside.
At Ryerson University in Toronto, I worked on projects that lived between software, hardware, and people. The job was rarely “build a proof of concept and move on”. It was get something fragile working, then make it stable enough that others can use and extend it.
That meant dealing with imperfect data, non-ideal hardware, and real-world constraints — all while keeping the system understandable enough that the next person didn’t have to restart from scratch.
In Finland, I replayed the same playbook with local companies and research groups in harsher conditions and more complex environments. Lab work showed what was possible; pilots and field tests showed what was actually deployable, observable, and maintainable.
Elysautus Solutions exists to focus on that translation layer: turning promising Edge/AI/IoT concepts into systems your team can deploy, monitor, and evolve — without giving up control of the stack.
Timeline
How the lab-to-field focus evolved
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Phase 01
Ryerson — learning the applied R&D craft
Early work bridging computer science, hardware, and human-computer interaction. Focus on turning exploratory research into working rigs and prototypes other people could use, break, and build on.
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Phase 02
Finland — replaying the playbook with industry
Working with Finnish labs and companies on IoT and interactive systems. Same pattern: promising research and concepts, but a lot of work required to make them robust enough for pilots and integrations with real infrastructure.
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Phase 03
Elysautus Solutions — dedicated lab-to-field focus
Turning that experience into a focused offering: helping teams move from lab work, prototypes, or conceptual architecture to field-ready pilots in Edge, AI, and IoT with clear architectures, observability, and handover.
Approach
How I think about R&D work
The goal is actionable evidence — not theatrical demos.
Constraints first
Real projects have budgets, legacy systems, and internal politics. I treat those as design inputs, not inconvenient details to ignore.
Field reality as judge
Lab success is a milestone, not the finish line. We introduce real-world conditions early so problems surface when they’re still cheap to fix.
Open and observable systems
Where practical, I lean on open-source, self-hostable stacks. Everything is instrumented so you can see what’s happening instead of guessing.
Fit
Who Elysautus Solutions is (and isn’t) for
Clarity saves everyone time. Here’s where this R&D model tends to work best.
A good fit if…
- • You have a clear problem and at least a rough idea of what you want to test.
- • You care about owning your stack and avoiding opaque vendor lock-in.
- • You’re willing to treat R&D as a learning process, not a fixed outcome guarantee.
- • You value honest visibility into trade-offs, risks, and technical debt.
Probably not a fit if…
- • You just want a black-box SaaS and don’t care how it works.
- • You want a slide deck more than a working system.
- • You treat infrastructure, security, or observability as one-off tasks.
- • You’re looking for the cheapest possible implementation regardless of risk.
Credibility
Experience and background behind the playbook
This isn’t a generic cloud consultancy bolted onto buzzwords — it’s built on years of applied work across research, industry, and open-source.
Experience (selected)
- • Applied R&D and prototyping in university labs.
- • Work on interactive systems and IoT in both Canadian and Finnish contexts.
- • Translating partner and stakeholder requirements into end-to-end prototypes.
- • Consulting around cloud-native, DevOps, and application security platforms.
Education
- • MSc in a Human–Computer Interaction / interface-focused program (Finland).
- • BSc in Applied Computer Science (Toronto, formerly Ryerson).
- • Thesis work on tele-operative / interactive systems and messaging.
Training & tooling
- • Deep exposure to Linux, containers, and Kubernetes.
- • DevOps / platform engineering and GitOps practices.
- • Security-conscious delivery: DevSecOps, supply-chain integrity, observability.
- • Preference for Linux Foundation and CNCF-aligned tooling where it makes sense.
Research & publications (selected)
- • Emerging application areas and challenges of automatic face analysis , Continuum: Journal of Media & Cultural Studies, 2013.
- • Applying Human—Computer Interaction Practices to IoT Prototyping , in From Internet of Things to Smart Cities, Chapman & Hall/CRC, 2017.
- • MSc thesis work on tele-operative / interactive systems and messaging, informing how prototypes are evaluated and instrumented in real-world use.
This research background shapes how I design and judge prototypes today: evidence over hype, measurable behaviour over anecdotes, and a constant awareness of the human side of complex systems.
Have a prototype or concept that needs to face the real world?
If you’re sitting on lab work, a PoC, or a diagram that needs to become a field-ready pilot, I can help you design the next concrete step.