Pipelines, serving, monitoring, and the infrastructure underneath so the model that worked in a notebook keeps working in production.
We design the platform your models run on: compute, storage, feature access, orchestration, and the environments between a laptop and production. Sized for the team you have and the workloads you're actually running, with the cost envelope agreed up front rather than discovered on the invoice.
Getting a model out of a notebook and into a service your product can call, reliably, at the latency your users will tolerate. Reproducible training runs, versioned artefacts, containerised inference, and automated release paths so a new model version is a routine deployment instead of an event.
Models degrade quietly. We instrument yours so drift, data quality breaks, latency creep, and quality regressions surface as alerts with an owner, not as a customer complaint months later. Every prediction traceable back to the data and the model version that produced it.
The operational layer that generative workloads need on top of classic MLOps: prompt and model versioning, offline and online evals, guardrails, caching, and routing across providers. Plus the access controls, audit trail, and per-feature cost visibility your finance and security teams will ask for.
2.5×
uplift in video processing speed
Autodesk Flow Studio. An AI-driven production platform that enables the creation of Hollywood-level VFX and studio content on an indie budget.
75%
less manual document review
Document intelligence and AI workflows that keep clinical trials inspection-ready across 65,000+ research sites in 90+ countries.
$630M+
in annual revenue powered
The direct-to-consumer platform behind the world's best-known medical apparel brand. Search, product, and checkout clearing orders in 85 markets, for 3.1M+ clinicians.
450K
concurrent streams during the Summer Olympics
Germany's answer to Netflix. Our team architected the platform that carried the Summer Olympics live to hundreds of thousands of concurrent viewers, without missing a frame.
The feedback for Nortik engineers is truly outstanding, and we are very satisfied with the collaboration. I’m eagerly awaiting a moment to bring on additional people and expand this partnership going forward.
Nortik’s Impact
Engineering across Florence's trial operations platform: eBinders, SiteLink, eTMF, eConsent and Site Feasibility, for a network of 65,000+ research sites in 90+ countries.
Everything teams usually want to know before putting a model in front of customers. Not here? Ask us directly.
Everything between a model that works on someone's laptop and a model your product depends on. Training and deployment pipelines, the serving layer, monitoring, and the governance wrapped around it.
One team owns that whole path, so nothing gets stranded in the gap between your data scientists and whoever runs your infrastructure.
Both, and most clients run both. Classic models want training pipelines, feature consistency, and drift monitoring. LLM features want prompt versioning, evaluation suites, guardrails, and cost control.
The platform underneath is the same either way, so we build it once and let both kinds of workload sit on it.
AWS, GCP, and Azure, with Kubernetes, Terraform, Docker, MLflow, Airflow or Dagster, and whichever serving stack fits your latency and budget.
We work in the tooling you already run instead of migrating you onto ours. If there's no platform yet, we'll recommend the smallest setup that covers you now and grows later.
Yes, and a lot of our work starts exactly there. We audit your pipelines, environments, monitoring, and release process first, then tell you what's worth keeping, what needs hardening, and what should be replaced outright.
Improvements ship incrementally alongside your roadmap, so model delivery never stops while the foundation gets fixed.
First we instrument cost per request, per feature, and per model version, because nobody optimises what they can't see. Then it's the usual levers. Right-sized compute, autoscaling, batching, caching, quantisation, and routing the cheap requests to cheaper models.
The biggest win usually lands in the first month, and it usually comes from a workload nobody remembered was still running.
We stay on for support and iteration as long as you need, and you get runbooks, infrastructure as code, dashboards, and a platform your own engineers can run.
Nothing we build is designed to keep you on the hook.
Discover more ways we can help you scope, build, and scale AI in your business.
