Developer Platform
Standardize how software is built, exposed and delivered without forcing every team to solve the platform again.
Consulting · Products · Managed operations
Engineering systems for AI Data Inference
Infer Origins helps organizations architect, build, and operate production-grade AI, data, cloud, and developer platforms across the Netherlands and Benelux.
workloadscontrol planeAzureAWSEdge Cloud
Selected organizations from our engineering experience
Engagement Triggers
We are typically brought in when...
A niche, independent engineering practice
We work from the problem outward; selecting, combining and engineering technologies into platforms that fit the system, its constraints and the people who will operate it.
Where proven technologies and reference architectures provide the right foundation, we build on them. Where the same engineering problem repeats, we standardize, automate and package what works into reusable platforms and managed capabilities.
ENGINEERED PLATFORMS
Born from repeated engineering problems.
Repeated problems become reusable engineering. Our platforms package proven architecture, automation and operational patterns into capabilities teams can adopt and own.
Data & AI Platform
Govern data and move AI workloads from experimentation to reliable production infrastructure.
Trust & Security
Establish identity, cryptographic trust and policy as platform capabilities rather than application concerns.
Digital Experience
Separate structured content from applications so teams can evolve digital experiences independently.
Engage where the constraint is
01
Advice
When complex decisions need clarity.
02
Products
When proven patterns should become platforms.
03
Operations
Production demands operational excellence.
A delivery model built for transfer
We do not create permanent dependency.
Every engagement includes explicit architecture decisions, operational runbooks, automation, training, and a path for your team to own the system.
Shape an engagement
01
Diagnose the system/constraint:
Understand the system, failure modes, dependencies, and operational bottlenecks
02
Engineer the paved road
Turn the architecture into a repeatable platform, workflow, or operating pattern
03
Operate for reliability
Run against explicit SLOs, automate the routine, and make failure visible
04
Transfer the capability
Leave behind the platform, runbooks, automation, and knowledge needed to own it
Field notes
Technical writing for leaders and builders
Linux sandboxing
How modern workloads can combine unikernels, MicroVMs, Landlock, and Bubblewrap to achieve strong isolation without the overhead of traditional virtual machines.
Read article →PCI GPU passthrough
A practical VFIO controller for preparing an Ubuntu host for PCI GPU passthrough, with preflight checks, GRUB recovery, NVIDIA isolation, and verification.
Read article →Automating DNS with Confidence
A split origin DNS setup with multi origin and multi subscription creating route via a gateway subnet is complex.
Read article →kyverno policy rules framework beyond K8S
Check kyverno-json, it is natural extension when one already having #kyverno as policy engine for #k8s governance.
Read article →