CI/CD pipelines
Automated build, test and deploy pipelines so releases are boring, frequent and reversible.
Services
Cloud, DevOps & Managed Operations
We build and run the platform your software lives on — CI/CD, containers, monitoring and long-term managed operations, with GPU and AI infrastructure as our named specialty.
Evidence
What this domain delivers
What we have shipped in this domain
- GPU sizing, deployment, model serving and monitoring on dedicated GPU hardware running production AI workloads, with the capacity numbers to show for it (up to 6× concurrent transcription capacity per GPU).
- CI/CD, containerization and monitoring as standing practice across our engagements — we operate the platforms we build, including our long-term partnership with Sonar in the German banking sector.
What we offer as capability
- Taking over an existing platform after a structured onboarding and audit: monitoring and deployment automation put in place first, then run as a managed engagement.
- Response and availability commitments sized per engagement and written into the contract, so what you are buying is stated rather than implied.
- Capacity and cost engineering for AI workloads: measured benchmarking on your own traffic shape, not datasheet arithmetic.
Capabilities
What we do
Containerization
Packaging applications and AI model servers into containers for reproducible deployment across environments.
Monitoring and observability
Metrics, logging and alerting for both application health and infrastructure utilization, so problems are seen before users see them.
Managed operations
Long-term operation of the platforms we build: deployment ownership, monitoring, incident response, routine maintenance and capacity planning, by the same team that wrote the code.
Linux server and cloud operations
Hardening, patching, backup and restore procedures, and the unglamorous discipline that keeps a platform boring.
Performance and capacity engineering
Measured benchmarking to size infrastructure against real load; our transcription tuning work (a measured six-fold capacity gain per GPU) came from exactly this discipline.
Tech stack
- Containerization
- CI/CD tooling
- monitoring & alerting stacks
- Linux server operations
- dedicated GPU servers
- AI inference serving (Whisper ASR, translation, TTS and vision models)
- backup and restore procedures
Spotlight
GPU and AI infrastructure is the specialty
Most platform teams have never had to size a GPU. We have — repeatedly, with a benchmark in front of us.
We run production AI stacks (speech recognition, translation, TTS, vision) on dedicated GPU hardware and handle the whole lifecycle: sizing, deployment, model serving, monitoring, tuning and the capacity conversation that follows. The economics of self-hosting live or die on how much work one card actually does, and that is a measurement, not a datasheet claim. On our own stack the measured answer moved from the original baseline to up to six times the baseline live-transcription capacity per GPU.
That specialty sits on top of ordinary, careful platform work — pipelines, containers, monitoring, backups — because a GPU with no deployment discipline around it is just an expensive server.
Method
How we work
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Automate the path to production.
If a deployment step is manual, it is a future outage — everything repeatable goes into the pipeline.
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Measure before scaling.
Benchmarks on real workloads decide hardware, not guesses. That is how we found a 4–6x throughput gain on hardware that was already paid for.
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Self-hosted where it matters.
For data-sensitive workloads (especially AI), we favor dedicated, self-managed infrastructure over third-party APIs — sovereignty and cost control together.
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Operations as partnership.
We stay accountable after go-live: monitoring, incident response and continuous improvement as an ongoing engagement, with every commitment written down.
FAQ
Frequently asked questions
Can you set up GPU infrastructure for our AI workloads?
Yes. We run production AI stacks (speech recognition, translation, TTS, vision) on dedicated GPU hardware, and we handle the full lifecycle: sizing, deployment, model serving, monitoring and tuning.
Do you do plain DevOps, without the AI angle?
Yes. CI/CD, containerization, monitoring and managed operations are standing services — they underpin every platform we build, AI or not. GPU work is the named specialty because that is where our published capacity numbers come from, not because it is the whole domain.
What are your SLAs and support hours?
Support and response commitments are agreed per engagement and written into the contract, sized to what the platform actually needs — the same team that built and runs the system is the team answering. Tell us your availability requirement early and we will tell you straight away what we can commit to and how it would be staffed.
Is self-hosting AI cheaper than using cloud AI APIs?
Often, at sustained volume — but the real answer comes from benchmarking. We measure your workload's throughput per GPU first; our own tuning work has shown that the same hardware can deliver several times more — up to a six-fold gain in live-transcription capacity — which changes the economics substantially.
Can you take over operations of an existing system?
Yes, after a structured onboarding: we audit the current setup, put monitoring and deployment automation in place, and then run it as a managed engagement. The onboarding phase is real work rather than a formality — it is what makes the operations that follow boring.
What does "managed operations" include?
Deployment pipeline ownership, monitoring and alerting, incident response, routine maintenance and capacity planning — an ongoing engagement with the same team, not a ticket queue.