Corporate product delivery
UI and product management support for a banking app built through a corporate accelerator.
Services
Strategy and prototypes for products worth building.
Turn a new product or service idea into a decision your team can act on. We define the product, prototype the risky part, and help you decide whether to build, change, or stop. Strategy that leads to a clear next decision, not open-ended consulting.
From rough idea to build path
We support the whole path, not just the build.
M-Squad works best alongside internal teams, helping them move from early product concept to prototype, architecture, build support, and launch readiness.
- Shape Clarify the user problem, product idea, and business context.
- Prototype Make the core journey visible and testable.
- Architect Define the build path, AI use cases, integrations, and system boundaries.
- Build Support Support implementation decisions alongside your internal team.
- Launch Readiness Prepare release, pilot, handover, QA, roadmap, and iteration.
Not every project starts with a full build. Many start with a clearer concept, a sharper prototype, or a build path the internal team can trust.
Controlled product experiments
Test the bet before you fund the programme.
Corporate product and innovation teams can move quickly without pretending that security, stakeholders, existing systems, and launch readiness do not exist. Start with the decision. Test the assumption that could sink it. Leave with evidence and a clear next move.
Built from zero
Two start-ups, plus the grassroots communities GenAI Wednesday and MobileMonday Germany, built from scratch.
What we help with
- Product discovery and AI use-case mapping
- UX concepts and prototype design
- Technical architecture
- MVP definition and build roadmap
- Vendor and technology decisions
When strategy & prototyping helps
This is the right starting point when your team has a promising idea, but not yet enough clarity to commit to a full build.
- Your corporate product or innovation team wants to test a new product or service idea before funding a full programme.
- You have an AI use case and need to test whether it is technically and commercially realistic.
- Your team needs decision-grade evidence and, if building is next, a clear MVP scope before budget is approved.
- You need a prototype that product, engineering, and stakeholders can review.
- You want to test the core user journey before committing to production.
- You need architecture decisions before AI-generated code creates long-term complexity.
What you get
- Evidence for a build, change, or stop decision
- A clickable prototype or working product slice
- A sharper product concept and a mapped core user journey
- Technical findings and architecture options
- An AI feasibility review
- A risk list with the open decisions
- If building is next: an MVP scope, architecture recommendation, cost and timeline estimate, and build roadmap
What does build-ready mean?
When the evidence supports building, build-ready means your team can start implementation without guessing. You have the core user journey, the MVP scope, the architecture, the AI feasibility call, the integration assumptions, and the open risks written down. The next decision is clear.
This is the entry point for uncertain projects: a reviewable first step that turns an idea into evidence and, when it earns the next step, a build-ready plan. It is usually a discovery or prototype sprint.
Example: AI-first operator prototype
For a mobile operator with about 3 million customers, we built an internal prototype to answer one question: could the next self-care app become a platform, not just a rewrite?
The prototype covered a native cross-platform shell over the existing backend, secure login, an AI assistant with live account tools, voice and multi-language support, plus an Agent Gateway concept.
One senior engineer built it in four days as an internal prototype sprint. Not a production launch, but a fast, contained way to test product direction before committing to a full build. See more of our work →
What we do not do
We do not build throwaway demos that look good in a meeting and collapse in implementation.
We do not start with broad AI promises. We start with the user problem, the business workflow, the data constraints, and the product system behind the screen.
AI speeds up the work. Senior product and engineering judgment decides what is worth building. That is how we run AI-assisted engineering across all delivery work.
Frequently asked questions
Do we need a finished specification to start?
No. We help shape the product, map AI use cases and define scope, often starting from just an idea or rough designs.
What is an AI prototype sprint?
An AI prototype sprint is a short, focused engagement, separate from a full build. It lets you test a product idea before committing budget to production.
Can you run a corporate product or design sprint?
Yes. We define the decision first, then prototype the riskiest part. If the user journey is the risk, that may be a design sprint. If integration or AI feasibility is the risk, we include a working technical slice. The sprint serves the decision, not the ritual.
What do we get from a prototype sprint?
A clickable or working slice that tests the core experience and the AI use case, plus evidence for a build, change, or stop decision. If building is the right next step, you also get an MVP scope, architecture recommendation, cost and timeline estimate, and build roadmap.
How long does a discovery or prototype sprint take?
Typically about two weeks for discovery and a few weeks for a working prototype, depending on scope.
Can a prototype become the real product?
Often, yes, we build prototypes on a standard, documented foundation so the work can carry into the MVP and production build.
Is this only for startups?
No. It works for startups, Mittelstand companies, and larger organizations. Startups often need speed and investor-ready clarity. Mittelstand teams often need practical workflows and integration with existing systems. Larger organizations often need security, compliance, and a small controlled scope before anything moves forward.
Is this a pilot, a prototype, or an MVP?
It depends on the goal. A prototype tests the experience or the technical feasibility. A pilot tests a controlled version with real users or real workflows. An MVP is the first version that creates real value and can be improved after launch. We help you choose the right first step instead of forcing every idea into the same format.
How do you keep AI-assisted prototyping from becoming messy?
We use AI to speed up exploration and implementation, but senior engineers own the architecture, review the code, and set the quality gates. For production-facing work this means code review, tests, type checks, security checks, privacy review, and documented architecture decisions.
Can this work inside a larger organization?
Yes. The sprint itself moves quickly. Internal approvals, system access, security review, and stakeholder alignment usually take longer than the prototype work itself. That is why we define a controlled first scope with limited access, clear permissions, and reviewable outputs.