Can you run open models on our own infrastructure?

Yes. We deploy open-weight models on your cloud or on-premises GPUs when data privacy or cost make hosted APIs a poor fit.

Do you build AI agents?

Yes, focused on engineering and operations: incident triage, runbook automation and change summaries, always with scoped permissions and human approval for risky actions.

AI & MLOps

AI Infrastructure & MLOps

From notebook experiments to reliable AI in production. We build the platforms that run machine learning and LLM workloads: GPU scheduling on Kubernetes, model serving, pipelines, evaluation and cost controls, plus safe AI agents for operations.

Talk to an engineer Problems we solve

Sound familiar?

Problems we fix every week

If two or more of these describe your team, it is time to talk.

Models never leave the notebook

Data scientists build promising models, but there is no path to production.

GPUs are expensive and idle

Machines are reserved per team and sit unused most of the day.

LLM features have no guardrails

No limits on cost, latency, data leakage or prompt changes.

You cannot reproduce last month’s model

Data, code, prompts and model versions are not tracked together.

AI agents in operations feel risky

You want automation for triage and runbooks, but not unsupervised access to production.

Recognize your team here?

A 30-minute call is enough to tell whether we can help.

Book a discovery call

What changes

Before and after we work together

Today

Manual model hand-offs
Dedicated, idle GPU servers
Untracked prompts and models
Unmonitored LLM usage

With FlickOps

Automated training and deployment pipelines
Shared GPU pools with scheduling and quotas
Versioned data, models and prompts
Serving with cost, latency and safety monitoring

Faster path from experiment to productionHigher GPU utilization for the same spendReproducible, auditable modelsAI assistants and agents with human approval built in

Deliverables