Dedicated environments
We deploy isolated dev and staging stacks. Nothing reaches production until every gate is passed.
Dev
Stage
Prod
AI engineering · team & delivery
Ship automation and products faster with AI in how we build and what we deliver
Messenger mini-apps, scalable backends, practical AI. From $40/hour — typically well below loaded in-house cost per productive hour.
Reply within 4 business hours · Russia & worldwide
4h reply SLA
$40/hour transparent rate
6 service lines
2–6 weeks MVP
Founder access — when it matters
Day-to-day delivery runs through a dedicated project team. Dmitry Shein — founder and technical lead — is open to client conversations and will step in personally when the situation calls for founder-level input.
- Architecture, timeline and budget trade-offs, non-standard integrations
- Escalations, critical releases, and acceptance alignment
- Strategic calls on your request — no requirement to route everything through the founder
How we run projects
Transparent delivery: isolated environments, a fixed team roster, Git, and quality gates at every step — before production.
Foundation on every project
Project team
Every project gets mandatory roles — no single “generalist” covering everything.
BA Analyst
TL Tech lead
Dev Developer
UX UX designer
QA QA engineer
Git is mandatory
All code lives in a repo: history, branches, review and rollback — no exceptions.
Task path to production
Design first, then a feature branch. No stage is skipped.
1 Design Tech lead: architecture, API, solution
2 Branch feature/task-* in Git
3 Development Code and unit tests
4 QA Testing by QA engineer
5 Code review Tech lead code review
6 UAT Client testing on staging
7 Production Merge and deploy
AI changes the economics
You pay for outcomes
In engineering
Specs, code, tests with LLM under senior review. Many MVPs in 2–6 weeks.
In your product
Assistants, RAG, classification — with clear data boundaries.
Your rules
OpenAI, GCP, YandexGPT, open-source and on-prem. If you require it — Russia-only models; we can run LLMs on your infrastructure inside your security perimeter.
Agent Readiness — AI-agent-friendly web
robots.txt, llms.txt, Link headers, MCP Server Card and API discovery for AI agents.
We build MCP servers on your open API so agents can use your product safely — not just scrape HTML.
We scan discoverability, Markdown, AI bot policy, MCP Server Card, Agent Skills, OAuth/API discovery and commerce protocols.
No agent layer
18%
typical site After rollout
92%
yumasoft.ru - robots.txt
- llms.txt
- MCP Server Card
- Link headers
- commerce: when needed
Our own products
Live services under the Yumasoft brand — not our full client portfolio
We also ship custom work: mini-apps, platforms, integrations, AI and support. Below are studio-owned products only. Custom work →
Servus · Google Drive
Scan, compress & AI tips for Google Drive
Яндекс.Диск — оптимизация места
Экономьте на тарифе каждый месяц
Optimize Yandex.Disk space to save on your cloud bill. Free once — save every month.
OmniHub
Реклама в комментариях MAX. Ad network in MAX comment threads
Body Balance 365
Wellbeing for HR, clubs, coaches · 10 languages
Lower than a mid-level FTE — for real output
We compare productive hours, not timesheet face value: PTO, sick leave, and meeting overhead are already inside salary. You pay us only for delivered work.
Mid-level software engineer (in-house, US-loaded)
$12,650 employer cost / mo
~110 productive h/mo
Cost per productive hour: $115
100 productive hours $11,500
- ~$12,650/month fixed — paid through PTO and sick leave
- Only ~110 productive hours/month, not a nominal 160+
- One hire rarely covers analyst, tech lead, UX, and QA at once
Studio hourly delivery
$40/hour
100 productive hours $4,000
~65% on a 100h block
- 100h = $4,000 — billed work only, transparent tracking
- Project team: analyst, tech lead, developer, UX, QA
- Scale hours up or down — from ad-hoc fixes to MVP without a new FTE
Loaded employer cost (~32% on top of salary) for 100 truly productive hours is about $11,500. At $4,000 you get senior delivery with AI-assisted engineering, a full project team model, and no recruiting, benefits, or bench time.
How we calculate
- Base salary $115,000/year, employer burden ~32%
- 220 effective workdays/year, ~75% productive time
- US market benchmark; EU/UK figures are in a similar band for mid-level roles