Wave of light particles flowing through faint circuit traces on a dark background

Services

MLOps / DevOps Services

Ship AI reliably, at scale.

Track record

Creators of RustyRAG

Realtime RAG, built in Rust · Sub-200ms end-to-end

10+ years delivering AI solutionsIgnas Vaitukaitis · Founder & CEO

Read RustyRAG’s source before you sign.

Shipped for

VersarWashington, DC
GynisusNew York
CampusReelNew York
LuniqGermany
HospitalityFlowSingapore

Overview

AI That Stays Running

Getting a model to work once is easy. Keeping it running reliably in production is the hard part. We build the pipelines, monitoring, and infrastructure so your AI systems deploy smoothly, scale on demand, and alert you before things break.

CI/CD for ML

Automated pipelines for training, testing, and deploying models — from code push to production in minutes.

Model Monitoring & Alerts

Track accuracy, drift, latency, and throughput in real time with alerts when performance degrades.

Infrastructure Automation

Terraform, Docker, Kubernetes — reproducible environments that scale up and down with demand.

Cost Optimization

Right-sized compute, spot instances, caching strategies, and auto-scaling to keep your cloud bill under control.

Process

How It Works

We treat your ML infrastructure like production software — version-controlled, tested, and automated end to end.

Infrastructure Audit

We review your current setup — deployment flow, monitoring gaps, cost inefficiencies — and map out what needs to change.

Pipeline & Automation

We build CI/CD pipelines, containerize workloads, and wire up monitoring and alerting across your stack.

Handoff & Runbooks

Your team gets full documentation, runbooks, and training so they can operate and extend the infrastructure independently.

Updated May 30, 2026