FAQ
Frequently asked questions
We answer the questions teams ask before an AI project: cost and time, your data and the law, the technology, and how to work with M-Squad.
What holds companies back on AI? Bitkom asked 603 German companies in 2026 (opens in a new tab). Those not yet using AI named three barriers most: missing know-how (85%), legal uncertainty (66%), and costs that are hard to calculate (63%). The first three blocks answer them.
Getting ready for an AI project
Is this a real AI use case, or would ordinary software do?
Ask that first. Make or buy still applies: if a product on the market does the job, buy it. If fixed rules solve the task, a script is cheaper, faster and easier to test. AI earns its place where the input is messy: language, documents, images, or judgment calls. In many good systems, plain code does most of the work and AI handles the exceptions.
What is a good first AI project?
Small and self-contained. One journey, a narrow set of inputs and outputs, and data you already have. It should show a result within weeks and have a positive return you can measure. That keeps the blast radius small and gives you a reviewable first step.
Check your idea against the first-step checklist →
Is our data good enough for AI?
We find that out before you spend on a build. Discovery includes a check of your data: where it lives, how clean it is, and who may use it. Many first projects need less data than you expect, because the model is not trained on it. It only works with it. If the data is not there yet, we say so and name what is missing.
We have little AI know-how in-house. Can we still start?
Yes. We bring the AI know-how: choosing the use case, the architecture, the evals and the delivery. Your team brings what it knows about your product and your customers. We build with your people, so the know-how stays with you after the project.
What do you need from our side?
Three things. People: someone who can decide, and someone who knows your systems and data. Access: the relevant APIs, a test environment and realistic test data. Time: for questions, reviews and feedback, most of it in the first weeks. For AI tooling, we bring our own assistants and licences, or work inside yours if your policy requires it.
Cost, time and what you get
What does an AI project cost?
A small project with a clear scope can start at €2,000 net, at a fixed price. A production system costs more, so larger work runs in phases. Each phase has its own scope and result, agreed before it starts. We agree the price phase by phase. You decide after each phase whether to continue.
What does the AI cost to run once it is live?
Building and running are two bills. To build and maintain something small, budget about €100 a month for an AI subscription. That covers a regular user. A heavy user needs more. Running a live product is different. Most cloud models bill by tokens, roughly the amount of text they read and write, so the cost grows with users and volume. Often a script does the routine steps, and the model only supervises or steps in when something breaks. That keeps the bill small. We measure token spend during the build and use smaller, cheaper models where they meet your quality bar. Before go-live, we estimate the monthly cost from your expected usage. We can also set budget caps and spending alerts.
How do we know an AI project will pay off?
We estimate it before we build. The return comes from two places: additional revenue, or time and cost saved. We put numbers on both, and on the cost to build and run. If the sum is not positive, we say so and look for a better project. A small first phase then tests those numbers before you commit to more.
How long does an AI project take?
Discovery typically takes about two weeks. A working prototype follows in a few weeks, depending on scope. Production takes longer, because security, testing and a limited live test come first.
Who owns the code?
You get all the source code, with the right to use, change and extend it, with us or without us. You are not tied to us. We keep the right to reuse the general building blocks and methods we develop along the way. That never includes your data, your business logic or anything confidential. Third-party and open-source components keep their own licences. If you need exclusive rights, we agree that before the project starts.
Your data and the law
Is our data used to train AI models?
That depends on the provider, the plan and the contract. We check all three before any of your data goes into a tool. Business and API plans usually exclude training on customer data by default. Consumer plans often train unless you switch it off. We also advise against training models on your proprietary data. In most projects the model works with your data at run time, without learning from it.
Where is our data processed? Can it stay in the EU?
We clarify that before the project starts, for every system and model in the flow. Where your data has to stay in the EU, we design the flow for it. The options include EU-hosted models, self-hosted models, and on-device AI, which runs on the phone. We validate the setup in a small test before we commit to it. Some widely used AI tools process data outside the EU. If that is not acceptable for your project, we switch tools.
How do you keep our data out of the wrong hands?
The AI gets only what the task needs. Agents work with scoped permissions, not broad access. Sensitive actions need approval, and requests are logged. Before the project starts, we agree which tools may see which data. We sign an NDA if you want one.
Do you sign a data processing agreement (AVV)?
Yes, wherever we handle personal data for you. Real customer data in a back-test is the typical case. We then act as your processor under GDPR, and the agreement lists the tools and sub-processors that may touch the data. Where possible, we work with anonymised or synthetic test data, so less personal data is in play.
How do you handle GDPR and the EU AI Act?
We design for the rules from the start and prepare what your reviewers need. That means data flows, an EU AI Act risk classification, action logs, and the technical input for a data protection impact assessment (DPIA). GDPR requires a DPIA when processing is likely to put people’s rights at high risk. The AI Act’s transparency duties have applied since 2 August 2026, for example telling users that they are talking to an AI. The rules for stand-alone high-risk systems apply from 2 December 2027. Which duties apply depends on your use case and your role, so we check instead of assuming. Where a question needs a lawyer, we bring in legal experts for AI and data protection. The decision stays with your company, advised by its data protection officer and legal team. We are engineers, not lawyers.
What about our works council and our employees?
Involve both early. In Germany, the works council has co-determination rights on technical systems that can monitor staff behaviour or performance. Many AI tools can. We join those talks and explain what the system records, what it does not, and who sees what. What is acceptable is for your company and its works council to agree. Acceptance grows when the people who will use the tool test it early and help shape it.
Technology and quality
Does it work with our existing app and systems?
In most cases, yes. We build on your existing app, APIs and identity systems where possible, so you do not have to rebuild first. We can add an agent to an existing mobile app or backend. For broader access, an Agent Gateway sits in front of the APIs you already have.
Which AI models do you use, and can we switch later?
We are model-flexible. We pick the model per task, cloud, self-hosted or on-device, and pin its version where the provider allows it. Because evals measure quality, you can test a newer or cheaper model against the same cases before you switch.
What happens when the AI gets it wrong?
It will, sometimes, so we plan for it. Before the build we define evals, short for evaluations: real test cases with the answers you would accept. Every change to a prompt, a model or the code runs against them. You see quality as a number, not a feeling. Evals do not make the AI perfect. They show how often it is right and where it fails. Where you have historical data, we back-test against it. In the product, risky actions need human approval, actions are logged, and the first release goes to a small group of users.
What is an Agent Gateway?
A secure action layer between AI agents and your product. It lets customer AI, partner agents and voice assistants use selected app and product capabilities. It works through MCP (the Model Context Protocol) and your existing APIs where possible. Agents do not get direct write access by default. For sensitive actions, the agent proposes, the user approves, the gateway executes, and everything is logged. You can start small, with a Lighthouse Project: one valuable journey and a small set of safe actions.
Working with M-Squad
How does an AI project run, from first call to go-live?
In small steps, each with a result you can check. Steps one to four are discovery, which typically takes about two weeks.
- Map and select. We list candidate projects with you and pick one with a positive return.
- Discover. We check your data, then define the architecture, APIs, security and privacy concepts, performance targets, and the test plan with its test data.
- Define evals. We agree the test cases that show what a good result looks like.
- Make, adapt or buy. We check what the market and open source already offer before we build anything new.
- Plan in phases. We split the work into phases and sub-projects that can run in parallel.
- Build and test. We test, measure quality and token spend, tune, and test again.
- Run a limited live test. Internal testers or a small group of real customers use it first.
- Go live. We monitor, fix and improve, with a roadmap for what comes next.
Will you sign an NDA?
Yes. We sign one before the first detailed conversation if you want.
How do we get started?
With a short call. Tell us what you want to change and what you already have. If a small first project makes sense, we propose a scope and a fixed price. Call Daniel +49 (179) 691 3629 or use the contact page at m-squad.com/contact.
Who maintains it after launch?
Usually your own team. We build with your people, so by launch they know the code, the evals and the release steps. You get a defined handover. After that we are there when you need us, for a model update, a fix or the next phase. If your lead at M-Squad is unavailable, a colleague steps in.
About M-Squad
What does M-Squad do?
M-Squad is an AI-first mobile studio in Munich. We help product and engineering teams shape, prototype, and deliver mobile apps, AI agents, Agent Gateways, and digital product systems, using AI as a force multiplier for senior engineering, across Germany, DACH, and Central Europe.
What does "AI-first" mean at M-Squad?
AI accelerates the well-understood parts of the work, boilerplate, glue code, tests, adapters, translations, and documentation, so senior engineers can focus on architecture, edge cases, security and product. It is the opposite of "vibe coding": people decide the architecture before code is written, every pull request is reviewed to a production bar, and specifications and the type system are the source of truth.
Where is M-Squad based?
In Munich, Germany (Schellingstr. 141 Rgb., 80798 München). We focus on Central Europe / the DACH region (Germany, Austria, Switzerland).
Who runs M-Squad?
M-Squad GmbH was founded in 2001 and is led by Co-Founder and Managing Partner Daniel Melter, who co-founded and chaired MobileMonday Germany (2006–2013) and co-founded GenAI Wednesday in Munich.
Who does M-Squad work with?
Mobile operators and MVNOs, established consumer brands (media, entertainment, travel, retail, automotive, financial services), mobile businesses digitising their workforce, and emerging mobile firms entering or expanding in the German market.
How fast can M-Squad deliver?
AI-assisted delivery lets a small senior team reach feature parity in a fraction of a typical large-team timeline. As proof, we rebuilt the core of a mobile operator self-care app, with an AI assistant, voice and multi-language, as a working prototype in four days, with one person.
Learn more about our AI-first approach, why we build with Flutter, or get in touch.