AI + engineering / The physical world
Intelligence.
Built for
the real world.
Plixo helps manufacturers turn AI into useful everyday work. We diagnose the bottleneck, build around the systems you use, and test the result where it matters.
Discuss a manufacturing workflow
01 / Where we help
The bottleneck is where
the work gets interesting.
Repeated runs. Manual handoffs. Results that are hard to trace. Start where an engineering improvement could change the cost, time or reliability of a real job.
Useful AI.
Working in operations.
Find the right application, connect it to the workflow, and improve reliability and cost. Start with a paid diagnostic or a defined implementation.
Explore manufacturing consultingDiagnostics + implementationBetter measurement.
More reliable biology.
Research into tools and methods for biological production, sample handling and repeatability. An enduring area of exploration.
Explore digital biologyResearch + engineering feasibility02 / Start with the work
Less friction.
More useful capacity.
The goal is a more capable physical world.
That starts with specific work: an engineering handoff, a quality investigation, a supplier response, or a production exception. AI earns its place when the result is reliable and worth the cost.
We work from the actual process, its constraints and the people responsible for it. A smaller improvement can be the right answer.
See how an engagement works03 / A practical first engagement
Know what is
worth building.
A scope-led diagnostic
A paid diagnostic of one recurring manufacturing workflow. Get a decision you can act on: a testable implementation scope, a smaller improvement, or a clear reason to stop.
Discuss a diagnosticScope, fee and timing agreed before work begins.
What you take away
-
01
A baseline of the bottleneck
Map the steps, responsible people and cost of rework, waiting or manual handling.
-
02
The engineering constraints
Review representative inputs, system interfaces, permissions and failure modes.
-
03
A test plan and a build decision
Define acceptance criteria, the smallest useful test and a scoped implementation brief. Include a no-build recommendation when warranted.
Implementation follows an agreed scope, fee and acceptance criteria. We can start with a defined build when the problem and requirements are already clear.
Portrait enhanced with AI
04 / The person doing the work
Engineering experience.
Curiosity about the work.
Plixo is led by Michael (Mike) Ochs. His background spans AI implementation, enterprise technology and work with manufacturers, including the semiconductor industry.
I bring the engineering perspective: how systems connect, where work gets stuck, and what it takes to make an improvement usable. That means understanding the existing process and defining a useful result together.
The aim is useful tools and methods that make physical work more reliable, with results people can measure and trust.
Michael's background on LinkedInA useful place to start
Bring the bottleneck.
Let's find the next step.
Tell Michael what repeats, what it costs,
and what a better
result would look like.