AI coding assistants
- Cursor
- GitHub Copilot
SERVICE
AI and vibe coding
Built something with Cursor, Lovable or v0? A senior Milo engineer reviews your codebase and tells you what to keep, fix or rebuild. AI is a force multiplier for an experienced engineer, and a shortcut that quietly accumulates debt for everyone else.
- Senior engineers reviewing every AI-generated line
- Fast POCs without sacrificing what comes after
- AI used where it's strong
When AI alone is enough vs. when you need an engineer
AI can take you from idea to demo in a weekend. Whether that demo can become a real product depends on who's steering it.
AI-assisted is usually right when:
You need more than AI alone when:
Why AI-assisted development needs a senior engineer
Where vibe coding stops being safe
Every unreviewed AI shortcut looks fine on its own. Read it top to bottom, like a bill — this is how ten-plus rebuilds happened in six months..
AI plus a senior engineer reviewing every line
THE FOUNDATION
AI-assisted development stays safe here
+1
No one on the team can read the code AI wrote
+2
An AI-generated scope document sent to a vendor, unread
+3
A frontend that looks finished with no backend behind it
+4
An optimistic estimate, taken at face value
+5
A full rebuild — the same result, every time
Every one of these felt like a shortcut in the moment.
Where AI helps and where an engineer steps in
We use AI where it's strong — and put a senior engineer wherever the stakes are higher.
Faster iteration
Test ideas and features with shorter development cycles.
Smarter problem solving
Explore solutions and alternatives with AI assistance.
Easier maintenance
Keep AI-assisted code clean, structured, and maintainable.
Safer experimentation
Explore new ideas while keeping development under control.
Products we're proud of
Finance
Bflow
BFlow represents a transformative journey in the automation of the accounting process. Our partnership with this Swedish digital accounting agency evolved from initial technical support to a comprehensive system overhaul, culminating in our role as the main technology partner and investor. This project showcases our ability to modernize legacy systems, scale applications, and support businesses through various growth stages - from startup to market expansion.
- React
Questions we help clients answer
Can AI replace an engineering team?
No — and the founders who treat it that way are the ones who come back needing a rebuild. AI makes an experienced engineer faster; it doesn't substitute for the judgment that catches its mistakes.
Why do vibe-coded MVPs usually need a rebuild?
AI code without human oversight accumulates debt fast, and the frontend often looks finished long before there's a real backend, data layer, or infrastructure underneath it. What looks like a product is usually still a prototype.
Is AI safe to use in healthcare or fintech products?
Only in limited, controlled environments. Data-sensitive markets need a senior engineer defining exactly where AI is allowed to touch the system, and where it isn't.
How do you make sure AI-generated estimates are realistic?
AI tends to estimate optimistically, and those scope documents are often shared with providers unread. We have a senior engineer stress-test any AI-generated estimate before it goes anywhere near a contract.
What AI tools do you actually use?
Cursor and GitHub Copilot for day-to-day coding, v0 and Lovable for rapid prototyping, and AI for copy, presentations, and ideation — all reviewed by a senior engineer before anything ships.
Is our code or data sent to AI providers? We only share client code, project information, or data with external AI providers when explicitly agreed upon with the client. AI-assisted development tools are used within the boundaries of each project's agreed
We only share client code, project information, or data with external AI providers when explicitly agreed upon with the client. AI-assisted development tools are used within the boundaries of each project's agreed requirements and confidentiality arrangements. Without client agreement, project code and confidential client data are not submitted to external AI services.
Recommended reading
Picked for founders scoping their first MVP
Development
How to choose an MVP development company (and avoid the expensive mistakes)
Choosing an MVP development company determines whether your first version teaches you anything useful. Pick well, and you get a product that tests your actual hypothesis with real users. Pick badly, and you get code you cannot extend, data you cannot trust, and a few months you cannot get back. I have run Milo Solutions for sixteen years and watched founders make the same avoidable mistakes here. This guide covers how to evaluate partners and where the real risk sits.
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MVP development cost: how much does it really cost to build an MVP?
Every founder who asks me what an MVP costs has read three articles saying the answer sits between $10,000 and $500,000. That range is true and useless for building a budget.Most MVPs land between $45,000 and $91,000, the standard row in the cost table below: 700 to 1,200 hours at senior Central and Eastern European rates. A thin slice starts at $19,000 and marketplace work reaches $190,000. Where you fall comes down to five things: how much you build, who builds it, what you connect to, how much design you buy, and what you commit to after launch. The number follows the scope.
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MVP testing: how to validate your product before a full launch
The expensive way to find out that nobody wants your product is to build it all and launch. The projects that reach me in the worst shape are the ones where a founder spent nine months on engineering questions before answering the demand question, and then watched the signups flatten within weeks.MVP testing means checking that people want and will pay for your product before you build it. Name the assumption that sinks the idea if it turns out to be wrong, pick the cheapest test that measures what people actually do, set your pass threshold in advance, then decide whether to build, pivot, or stop.CB Insights examined 431 venture-backed companies that shut down since 2023 and identified the reason for failure in 385 of them. Of those 385, 43% had poor product-market fit and 70% ran out of capital, which the report treats as the final cause of death rather than the root problem; and because the analysis assigns several reasons to each company, the percentages total more than 100. Companies rarely run out of money doing something customers were desperate to pay for.
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Read moreReady to build with AI the right way?
Let's talk through what you've already built, what AI got right, and what a senior engineer needs to take it from here.
- Free consultation
- Tailored approach
- No obligation
