Enterprise AI platform build and platform-layer operations
Once several AI systems run in a company, reliability is decided by the layer that carries deployment, releases, and monitoring. We build and operate multi-tenant AI platforms with GitOps deployment and blue-green releases, on-premise and in private cloud environments as well. Delivered by a Budapest-based team.
The next step is the AI opportunity assessment: from HUF 500,000 + VAT.
Updated:
The first AI use case usually starts as a standalone application, and it works fine that way. By the third, the questions repeat: how does a new version go out, how do you roll back, who can access which data, and where do you see that the system is healthy. The platform answers these once, and every workload uses the same answer from then on.
We operate production-grade, multi-tenant AI platforms: deployment runs on GitOps — every change starts from version control, traceably — and new versions go out through blue-green releases, without downtime. We build RAG and agent systems at production quality: retrieval pipelines, prompt-injection defence, per-component failure policies. Our founder led a multinational corporation's global automation and AI centre of excellence for five years, with a 20+ person international team and over $6M in annual savings; our team has been building production systems since 2013 and has closed 46+ projects.
Adding generative AI to a single process is covered on our Generative AI Integration page; this page is about the layer beneath it: running many AI workloads reliably. The platform connects to your existing ERP, CRM, and SharePoint systems, and runs on-premise or on your private cloud when needed, with open-source models. We run the EU AI Act risk classification per workload at the start of the project. For monthly operations of a single existing system, see our Managed AI Operations page.
What the platform unifies
GitOps deployment
Every change starts from version control: the configuration of the platform and of the workloads running on it lives as code. Deployments are repeatable and traceable; a rollback is the restoration of a previous, working state.
Blue-green releases
The new version starts alongside the old one and only receives traffic after it passes the checks. The switch happens without downtime; on failure the rollback is immediate, because the old version is still running.
Multi-tenant operations
Several business units and several workloads run on the same platform, with separated access and resources. Launching a new use case needs no new infrastructure — it lands on the existing platform.
Monitoring and logging
One place shows what runs, what it costs, and where it fails. Logging and human oversight points are designed in per workload — the same records form the basis of the EU AI Act documentation.
How the build works
- 1
AI opportunity assessment — from HUF 500,000 + VAT
We list your running and planned AI workloads and your existing infrastructure: what runs today, where, and what is missing for reliable operations. The output is a written platform plan with cost estimates you can use even if you continue without us.
- 2
Platform foundation
We set up the GitOps pipeline, the environments, the monitoring, and the access model — on your infrastructure: on-premise, in private cloud, or on Azure AI Foundry.
- 3
Pilot on the first workload — 2-3 weeks
We take the first workload — typically a RAG assistant or an agent process — to production on the platform. A small proof of concept is ready in 2-3 weeks and shows how releases, rollbacks, and monitoring work in your environment.
- 4
Implementation and operations — 6-12 weeks
Most projects go from kickoff to production in 6-12 weeks; further workloads land on the finished platform. Source code, models, and data remain yours; monitoring and support are part of how we work.
Why Leventech
Multi-tenant platforms in production
The team operates production AI platforms serving multiple workloads today, with GitOps deployment and blue-green releases. What we build, we also operate and maintain.
Enterprise operations background
Our founder led a multinational corporation's global automation and AI centre of excellence for five years — a 20+ person international team, over $6M in annual savings. 46+ completed projects, 96% client satisfaction.
EU AI Act and GDPR built into development
Risk classification at project start, logging and human oversight in the design, a go-live checklist before launch. We have experience implementing high-risk AI systems in regulated environments.
Your data stays with you
On-premise or private cloud deployment when needed, with open-source models (Meta Muse, Qwen3.8, DeepSeek V4, Mistral Large 3). Your data and your logs never leave your environment.
Common questions
One or two use cases work fine as standalone applications. The platform pays off when several workloads run or are planned, and the questions of releases, rollbacks, access, and monitoring come up again with every new system. The assessment answers this for your situation — and if a standalone application is enough, we say so.
That the full configuration of the platform and its workloads lives in version control, and every change reaches the environments from there. Every deployment is traceable: you can see who changed what and when. A rollback is the restoration of a previous, working state — a version switch instead of manual intervention.
Through a blue-green release: the new version starts alongside the old one and only receives traffic after the checks pass. Users notice nothing at the switch. If the new version fails, traffic returns to the old one — the rollback is immediate, because the old version keeps running.
Yes. We build platforms on-premise and in private cloud environments, with open-source models (Meta Muse, Qwen3.8, DeepSeek V4, Mistral Large 3) running on your servers. Where data handling allows it, we use enterprise models over API: GPT-5.6, Claude Opus 5, Gemini 3.6. The two coexist on the same platform, decided per workload.
Workloads on the platform reach your existing ERP, CRM, and SharePoint systems via APIs. Permissions stay in the existing systems: a workload gets no more rights than the user whose work it supports. The assessment describes the scope of the integrations.
That there is a plan for failure too. The retrieval pipeline answers with source citations, inputs are filtered by prompt-injection defence, and every component carries a failure policy: what happens when retrieval returns nothing, when the model does not respond, or when the agent gets stuck. Anything the system cannot close reliably is handed to a person.
Logging and human oversight points are built in per workload, as part of the system, so they can be retrieved at a later check. We run the risk classification per workload at the start of the project. We provide the technical and organizational preparation; the legal assessment stays with your legal counsel.
The entry point is the AI opportunity assessment: from HUF 500,000 + VAT — it establishes the platform scope and the cost estimate. Individual workloads on the platform fall into the published bands: an AI assistant or integration project is usually €15-40K; a document processing system €30-80K. Typical ROI is 6-12 months; across our 46+ completed projects, automated processes reach a 40-45% efficiency gain.
Still have questions?
Start with a conversation
Tell us how many AI systems you run or plan and on what infrastructure — we tell you what belongs on a shared platform.
What would you automate in your business?
Tell us briefly about the task. We will reply by email to arrange an initial conversation.
The next step is the AI opportunity assessment: from HUF 500,000 + VAT.
What happens after you send?
In the first conversation we review the task, your existing systems and the outcome you need. Then we discuss whether a detailed assessment would help. We provide a proposal before any paid work.
Or reach us directly:
[email protected]
Monday-Friday, 9:00-17:00 CET