Services
AI & LLM Platform Engineering
Private AI infrastructure you own — clusters, models, and automation, run with production discipline.
What we do
Private AI infrastructure
On-prem and hybrid GPU clusters on Kubernetes, deployed through GitOps with full observability. We designed and operate our own production 4-node NVIDIA DGX Spark cluster.
Private LLM serving
Open-weight and custom models served inside your network, with access control, usage visibility, and predictable cost. Your prompts and documents never leave your infrastructure.
Custom model work
We built, quantized, and serve our own Mixture-of-Experts model lineage — Vinicius-35B-A3B-v1. We apply the same skills to adapt, compress, and serve models for your workload.
RAG & knowledge systems
Retrieval-augmented generation over your documents and data, so answers come from your sources — grounded and citable, not guessed.
AI agents & automation
Agentic workflows built on MCP that connect models to the systems you already run — with clear permissions and human control where it matters.
AI you own, run like production infrastructure
Most companies experimenting with AI hit the same wall: the demo works, but nobody can say where the data goes, what it will cost at scale, or who keeps it running at 2am. We remove that wall by treating AI as what it actually is — production infrastructure.
Mavits designs, builds, and operates private AI platforms: GPU clusters in your rack or in a hybrid setup, running on Kubernetes, deployed through GitOps, and watched by real observability. Models are served inside your network, behind your access control. Your documents, prompts, and customer data stay yours — provably.
We run what we recommend
This is not a slideware practice. We designed and operate our own production 4-node NVIDIA DGX Spark GPU cluster — Kubernetes, GitOps deployment, monitoring, and disciplined change management, end to end. On that cluster we serve our own Mixture-of-Experts model lineage, Vinicius-35B-A3B-v1, which we built and quantized ourselves.
That matters for one practical reason: every recommendation we make has already been tested on our own hardware and our own budget. When we tell you a model will fit on a given machine, or that a serving setup will hold under load, it is because we have measured it — not because a datasheet said so.
From infrastructure to outcomes
Hardware is the floor, not the goal. On top of the platform, we build the things your business actually feels:
- Private model serving — open-weight or custom models behind a single stable endpoint, with per-team access control and usage visibility.
- Custom model work — adaptation, quantization, and serving tuned to your workload, so you get the quality you need at a cost you can defend.
- RAG and knowledge systems — assistants that answer from your documents and databases, with sources attached, instead of improvising.
- Agents and automation — workflows built on MCP that let models operate your existing tools safely, with humans approving the steps that matter.
How an engagement runs
The path is the same every time: understand your use case and constraints, architect the smallest platform that serves it, build it in visible increments, then operate it — or hand it over with a runbook your team can actually use. You see success criteria in writing before the first server is racked, and evidence that they were met before we call it done.
What you get
- Architecture and hardware plan sized from measured requirements
- Production Kubernetes GPU platform with GitOps and observability
- Private model-serving endpoint with access control and usage metrics
- Working pilot with success criteria agreed up front
- Operations runbook and a clean handover — or we keep running it for you
Tools we work with
- Kubernetes
- NVIDIA
- vLLM
- GitOps / Argo
- MCP
- Grafana
Common questions
Why run private AI instead of using an API?
Three reasons come up in every engagement: data control, cost, and independence. Your prompts, documents, and customer data never leave your network. At sustained volume, hardware you own beats per-token pricing. And you are never one vendor decision away from a broken product. APIs are still the right call for some workloads — and we will tell you when they are.
What hardware do we need?
Usually less than people expect. Requirements depend on the models you serve and the load you put on them, so we size from measurements, not guesses. Many teams start with a single GPU server and grow from there. We run a 4-node NVIDIA cluster ourselves, so we know where the real scaling steps are — and where the money is wasted.
How long until a working pilot?
A focused pilot — one model, one use case, served privately, with success criteria agreed up front — typically takes weeks, not months. We define what "working" means before we start, and everything built during the pilot carries forward into production.
Do we need an in-house AI team?
No. We design, build, and operate the platform, and we document everything in plain language. If you have engineers, we hand over cleanly and train them. If you do not, we keep running it for you under a care plan.
Ready to talk it through?
Tell us where you are today and where you want to be. We will map the shortest sound path between the two.
Book a consultation