An idea, but no product
The business model, the deck or the mock-up is already there, but there is nothing to show an investor, a partner or the first client — no working system to run a real scenario on.
Problem → solution
Build a software product
You have a product idea, a business model or a mock-up — but no working system, or one that cannot cope with growth. We design and build the product with operation in mind: the minimum scope that tests the hypothesis, on an architecture that will survive the second client.
For founders, product teams and companies that need to build a SaaS, a platform, a marketplace, an internal system or a mobile app — quickly, but without having to rewrite it from scratch later.
What people come with
The prototype cannot carry growth
The first version was put together in a hurry and is already cracking: the second client breaks the architecture, each new feature costs more, and data and access rights hang by a thread.
Slow, expensive and opaque
Development drags on for months with no visible result, estimates slip, and responsibility gets lost between contractors — no one owns the product as a whole.
How we solve it
Separate the essential from the secondary
We define what the product has to prove to the business and who needs it. We build the scope without which a launch makes no sense, and defer the rest until the hypothesis is tested.
Build the MVP on a solid architecture
A minimal working system built for operation: a hypothesis that works out can be grown, not rewritten. Domain rules go into the code and the database.
Develop in iterations
With demos and automated tests. You see a growing product, not a percent-complete report, and can correct course at any step.
Take it to production and grow it
Load, security, compliance with 152-FZ (the Russian personal data law), operation — against measurable acceptance criteria. New modules are added on the same architecture.
What it is built on
A proven stack, not dependence on us
We build client products on a standard stack — Python/FastAPI, TypeScript/React, PostgreSQL and the like — so that the result can be maintained by someone other than whoever wrote it.
The full cycle, one team
Architecture, backend, interfaces, infrastructure and acceptance — with no handing of responsibility between contractors, which is where results usually get lost.
AI and knowledge graphs, when they are needed
If the product needs semantic search, a connected domain model or an assistant, we bring in our own work on knowledge graphs and agents — always so that it pays for itself, not as decoration.
What we do for it
What it looked like in practice
Music tech · SaaS
MVP in operation (closed launch)Music releases without missed deadlines
Release ops for independent labels: 9-stage lifecycle, pitching deadlines for 12 stores, AI drafts with human approval. Zero to production-grade in 7 weeks.
- 7 weeks
- from first commit to a production-grade stack with Vault, encrypted backups, SLOs and a move to a Russian data centre (265 commits)
- 16 days
- from project start to an investor presentation with a live product demo
Real estate · PropTech marketplace
MVP in operationLand-plot marketplace with a next-step plan: MVP in 2 months
From a mock-up to a working platform: plot → works → contractor → request. Rule-based next-step engine, three dashboards, personal-data compliance. MVP in 2 months.
- 2 months
- from approved spec to pre-release MVP on an HTTPS stand — week 9 against the plan's own 10–13 week estimate
- 7 days
- from spec to the first full-implementation commit: 168 files, 21.5k lines, then two months of hardening to pre-release
EdTech · HR-tech
MVP in operation (demo stand)From business canvas to a student-work marketplace demo in about six weeks
The client had a business model and no product. We built a student-task marketplace MVP where the server enforces legal and platform rules, and put a demo stand live.
- ~6 weeks
- from first commit to a deployed demo with all workspaces; 7 working days in commit history
- 3 checks
- run by the server before a contractor is assigned: data consent, self-employed status, mentor — each with a plain refusal
EdTech · Mobile app
In-house productA mobile client for an AI learning platform, built without waiting on the backend
Flutter client for an exam-prep platform: adaptive micro-lessons, a streaming AI assistant, a knowledge map. UI built in parallel with the backend via a mock layer.
- 13 weeks
- from first commit to a working app with auth, goals, lessons and AI chat
- 41 → 81 endpoints
- API coverage doubled from MVP to the extended version — 14 → 31 modules on the same architecture
Cost the effect on your own numbers
AVA is a process economics calculator. In a couple of minutes it shows whether automating your case is worth it — before you talk to us, with no commitment.
Estimate the impact with AVAFrequently asked questions
How quickly can we get a working MVP?
In our cases, the path from a business canvas or prototype to a working platform in production took weeks, not quarters; the specific timelines in the cases are tied to the commit history. Your timeline depends on scope and integrations — we give a range after analysing the task.
Can you improve our existing product rather than build a new one?
Yes. We start with an audit of the code and architecture and say plainly which is cheaper — improving what you have or rebuilding the bottleneck. Sometimes the honest answer is “no rewrite needed”.
What do we keep after the project?
Source code, documentation, deployment infrastructure and a trained team. By default, the rights to the result belong to the client; the terms are fixed in the contract before the start.
Other tasks
Automate a business process
Manual work has become the bottleneck: people sort through requests and documents, move data between systems and answer the same questions over and over. Automation does not pay off everywhere — so we start with where exactly the time and money go, and automate what can be counted in money.
Adopt AI
You need AI, but it is unclear where to start and whether it will pay off. AI projects more often fail not at the model but earlier — the chosen process cannot be counted in money, or the data for it does not exist. We start with the process and a data check, and decide on the technology last.
Build the infrastructure
The product is growing, but the infrastructure holds it back: releases are scary to ship, clients are the first to report failures, and the backup is first tested when it is already needed. We make deployment a boring operation, failures visible in advance and recovery a tested procedure.
Tell us about your task
Describe what needs solving. If it cannot be solved or will not pay off, we will say so straight away, before any work starts.
or email us directly: hello@xteam.pro