Dedicated AI Team
AI engineers who work as part of your team, not as a separate vendor.
AI engineers, data engineers, backend developers, DevOps/MLOps and QA for AI — one person or a full team, on a monthly retainer, working in your tools and your sprints.
AI ENGINEERS, ON YOUR TEAM
More AI capacity, without building a department from scratch.
Engineers who join your stand-ups, work in your repositories and hand over everything they build.
Hiring for AI is slow, and the first AI projects need a mix of skills that rarely sits in one person: model and prompt work, data pipelines, backend integration, deployment and evaluation. A full-time hire for each role is hard to justify before you know which use cases will stick. Meanwhile your own developers are busy keeping the product running.
We give you individual engineers or a small dedicated team that works as an extension of yours: your backlog, your tools (GitHub or GitLab, Jira or Linear, Slack or Teams) and your sprint rhythm, on a monthly retainer. Typical work includes AI agents connected to internal systems, data pipelines and analytics, LLM features inside existing custom software, and taking over prototypes built with AI coding tools. Our team has been building production software in Cluj-Napoca since 2013.
The stack follows your project. We work with Python and TypeScript, LangGraph, the OpenAI and Anthropic Claude APIs, open-weight models such as Llama, Mistral and Qwen served with vLLM, PostgreSQL with pgvector, Docker and Kubernetes. When models need to run privately, the team can use our own GPU cloud inside our network, AS56951.
One engineer or a full team
Start with a single AI or backend engineer, or a cross-functional team with data, DevOps/MLOps and QA. Add or remove roles month by month as the roadmap changes.
Your code, your process
We work in your repositories, tickets and channels. Everything we write belongs to you from day one and is documented so your own team can maintain it.
Senior oversight included
A senior engineer reviews architecture and code, keeps an eye on quality and model costs, and is your contact when something in the team needs to change.- AI engineers: LLM integration, agents, retrieval and evaluation
- Data engineers: pipelines, warehouses and data quality
- Backend developers: APIs and integration with your existing systems
- DevOps / MLOps: CI/CD, model serving, monitoring, GPU infrastructure
- QA for AI: test sets, evaluation runs and regression checks
- Monthly retainer, scale up or down with agreed notice
- You own the code, prompts, configuration and documentation
- English or Romanian, working hours overlapping the EU and UK
- Code review by a senior engineer on every change
- NDA, least-privilege access and security from day one
HOW WE WORK
Onboarding in steps, not in one leap
You see how we work on a small scope before you commit to a larger team.
Discovery call
We talk about your product, roadmap and current team, and which skills are actually missing. Sometimes the answer is one engineer, not a team.
Team proposal
You receive the proposed composition, the engineer profiles, the working model and the monthly retainer. You can meet the engineers before they start.
Secure onboarding
NDA signed, access granted per role with least privilege, environments set up. We read your code and documentation before we change anything.
First sprint
We start with a clearly scoped piece of work, so you can judge quality, communication and pace before adding more people.
Steady delivery
Daily work in your sprints, senior code review, short written updates and a monthly review of what was delivered and what comes next.
Scale or hand over
Add roles when the roadmap grows, reduce when it settles. When you want to own it internally, we hand over with documentation and pairing sessions.FAQ
Common questions about a dedicated AI team
Short answers. The details go into the proposal.
How is a dedicated team different from a fixed-scope project?
A project has a defined scope and an end date. A dedicated team works on your backlog, follows your priorities and adapts as you learn which AI use cases are worth pursuing. If your scope is clear and bounded, a project or a value-based engagement may suit you better — we compare the options on our AI engagement models page.
Who owns the code and everything the team produces?
You do. The code lives in your repositories from the first commit and the contract assigns the intellectual property to you. Prompts, evaluation sets, pipeline configurations, infrastructure as code and documentation are part of the delivery, not something we keep.
How quickly can we scale the team up or down?
Changes are agreed in advance, with a notice period set in the contract, so both sides can plan. Reducing the team is straightforward. Adding a role depends on the profile you need; we give you a realistic start date up front instead of promising someone tomorrow.
How do you handle security and confidential data?
NDA before any access, a separate account for every engineer, least-privilege permissions, no client data on personal devices, and all access removed when the engagement ends. If data must not leave your environment, we work inside it, or run models privately on infrastructure we operate in our own network.
Which language and time zone do you work in?
We are in Cluj-Napoca, Romania (UTC+2, UTC+3 in summer). That gives a full working-day overlap with continental Europe and most of the UK working day. We work in English or Romanian, in calls and in writing.
Need AI engineers on your team? Let us talk.
Tell us what you are building and which skills are missing. We will propose a team composition, or tell you honestly if a smaller setup fits better.