Industries
Every sector. The same full strength.
AI infrastructure, physical GPU hardware, and cloud and DevOps engineering carry every engagement at the same standard. Here is how that lands in three sectors we serve often; the platform, the guardrails, and the team behind them are identical wherever you work.
Owned GPU capacity for teams that compute all day.
University labs, research groups, and AI startups burn through cloud credits doing work that never stops. We design shared GPU pools with schedulers and quotas, build the racks, and stand up an OpenAI compatible serving layer, so a dozen teams share owned capacity instead of fighting over spend.
- Shared clusters with per team quotas and fair scheduling
- From a single Mac Studio to a multi rack H100 installation
- Cloud bursting for deadline weeks, owned capacity for the daily load
AI where the data is not allowed to leave.
Regulated organizations want the capability without the exposure. We run models inside your cloud account or on hardware in your building, with guardrails on every layer, approvals where they matter, and an audit trail your compliance team can actually read.
- On premises and private cloud inference with full data residency
- Input and output guards, PII redaction, human approval thresholds
- GDPR and CCPA ready controls designed in from the first review
Production infrastructure for people who ship content.
Studios and creators need compute next to the work: local model hosting for video, audio, and image pipelines with no upload wait, and streaming setups that are built, tuned, and supported rather than improvised.
- Local GPU workstations and model hosting beside the edit suite
- Complete streaming studios: encode PCs, cameras, audio, OBS tuned
- Hardware specced, sourced, racked, and kept running
Your sector is covered.
Everything here, from the platform standard to the hardware expertise, travels to every industry that builds with AI, cloud, or data. Tell us what is stuck.