MVP · SaaS · internal systems · integrations
Product development
with AI acceleration and senior control.
We build MVPs, SaaS products, internal systems, integrations with 1C, ERP, internal APIs and staged legacy replacement. AI accelerates prototypes, code, tests and documentation; senior engineers own architecture, security and production outcomes.
AI removes manual routinePrototypes, boilerplate, types, tests, documentation and integration drafts appear faster. The engineer spends time on substance, not copy-paste.
Senior engineers own decisionsArchitecture, security, business logic and final decisions stay with humans. AI accelerates the team, but does not replace engineering judgment.
Progress visible from week oneA clickable prototype in 7 days, a weekly digest. You steer the idea while it's cheap, not after the code is written.
Production disciplineGrowth architecture, security, load, tests, monitoring. A production system the business runs on: not «a weekend prototype».
Two worlds behind us: less risk for your project.
For more than 13 years, our team has built systems for two very different environments. Government and corporate projects demand load-aware architecture, security, formal acceptance and secure perimeters; startups demand MVPs in weeks, frequent releases and fast hypothesis testing. We have worked in both and choose the pace that fits the project.
In 2023 we built AI tools into this process: not «let's try», but as part of a senior engineer's work. The routine (boilerplate, types, tests, documentation) went to AI, the engineering decisions stayed with people. That gave us something that wasn't there before: startup speed at corporate-grade quality.
For you that means less risk. We've already been through every direction: from regulated government systems to investor MVPs: on dozens of projects. So we see the pitfalls in advance and give a realistic estimate, not an optimistic one we later «overrun».
Year: team's engineering practice started
Years of team's engineering experience
Completed projects
And why it matters now
It's not the «most correct» product that wins, but the one that changes faster than competitors.
If you can't ship an update in a week, you lose to the one who can: even if you build «higher quality». Shipping speed is no longer a nice bonus, it's your competitiveness. Our job is to give you that without sacrificing reliability.
Same MVP: classical team vs seniors + AI tools.
Not «take our word for it»: a concrete example. We're taking a real production project, not «a landing page with a form». Say, a B2B SaaS for managing client requests, the kind that mid-market companies typically commission:
- ▪Auth (email + SSO), 3 roles: client / manager / admin
- ▪Client cabinet with request history and statuses
- ▪Internal CRM for managers: Kanban, filters, search, comments
- ▪Integrations with 2–3 external services (billing, email provider, OCR / telephony)
- ▪Email + push notifications, templated mailer
- ▪Basic analytics for admin: charts, filters, CSV export
- ▪Production infrastructure: CI/CD, monitoring, backups, environment isolation
- ▪Security: validation, XSS/CSRF protection, secrets encryption, audit log
This is a full production system that a business actually runs on: not a toy. Below: how long such a project takes a classical team vs a senior team with AI tools, by our experience.
Discovery and spec
Classical
3–4 weeks
A business analyst writes a 50–100 page specification, followed by rounds of sign-off and clarification. By the end, half of it may already be outdated.
Seniors + AI
3–5 days
A joint session: you tell, we ask. AI tools immediately turn the conversation into a clickable prototype. You see the idea in 3 days, not a month.
Design
Classical
3–4 weeks
A designer prepares mockups and a design system, goes through review and makes revisions. More changes appear at the development handoff, making the stage slow and expensive.
Seniors + AI
5–10 days
The prototype is already clickable. The designer works on top: brings it to brand, ergonomics, accessibility. No long «Photoshop mockup approvals».
Backend + frontend
Classical
8–12 weeks
The team has two backend engineers, two frontend engineers and a DevOps specialist; each feature takes a separate sprint. Boilerplate is written manually and tests follow implementation, so deadlines regularly slip.
Seniors + AI
3–5 weeks
Senior + AI tools for boilerplate, typical components, types, tests, migrations, docs. The engineer focuses on architecture and business logic. What a classical team does in a day: we do in an hour, at the same quality.
Testing and acceptance
Classical
2–3 weeks
QA, bug fixes, regression, test plans and customer acceptance run as separate stages. Some defects surface only in production.
Seniors + AI
1 week
Tests run in parallel with development (AI helps generate coverage). Acceptance is short: the customer saw clickable prototypes from day one, no surprises.
Total
Classical
≈ 4–5 months
4–5 people, ~3–5M RUB
Seniors + AI
≈ 1.5–2 months
2 seniors + AI, ~1.2–2M RUB
Where the savings come from
Why it gets faster: four concrete mechanisms.
A classical team does not «work slowly»: it just has a different structure of losses. Most of the time goes not into code but into coordination between roles, handoffs, fixes at seams and iterations after reviews. Our process reduces these four losses at once.
Fewer handoffs between roles
Classical chain: product → designer → backend → frontend → QA → product → client → fixes. Each seam is days of «context transfer» and weeks of fixing mismatches. A senior with AI closes most of this chain full-stack: no losses at seams.
Prototype from week one: the client doesn't «trust», they see
In a classical model, the client first sees the result after two or three months and may discover that the team misunderstood the brief. With AI, a clickable prototype appears in week one. The client can steer the idea before changes become expensive.
AI does the routine, not «thinking for the engineer»
Boilerplate (models, migrations, forms, base components, TypeScript types, API shapes, error parsing) is up to 60% of a junior+middle developer's time. AI tools generate it in minutes; the senior reviews and integrates. This isn't «AI writes architecture», it's «AI removes copy-paste».
Tests and docs: in parallel with code, not «after»
In a classical model, testing and documentation often remain until the end, when time is already short. AI helps prepare tests and technical documentation alongside development. At acceptance, the tests are ready, the README is current and the API specification matches the system.
AI does not remove the engineering work or produce a finished product in 30 seconds. A senior engineer still owns the code, architecture, tests and security, while the tools reduce time lost to routine work and handoffs. What changes is how much engineering decision time the engineer has left.
Important distinction
Seniors + AI tools is not vibe coding.
With vibe coding, a person describes the task, receives generated code and runs it with limited understanding of its internals. That approach can suit some prototypes. In production development we retain engineering control, and the client needs to understand the distinction before starting.
Vibe coding
Hypothesis check. Prototype. Edge case.
AI writes code, the human barely controls what's inside. The point: «it runs». Works for a weekend idea check, a personal script, a research prototype, a one-off utility.
- ·«It works, no idea how: figure it out later»
- ·Architecture: whatever came out
- ·Security: by luck
- ·Maintenance after a year: rewrite from scratch
- ·Fits: a personal project, MVP-day, an experiment
Seniors + AI tools
Production systems. Business-critical. Long life.
A senior engineer who understands every line + AI as an accelerator of routine. Architecture is designed, security engineered, tests written, documentation kept current. AI removes the boring part but doesn't make engineering decisions.
- ·«It works, I know why, I can explain it three years from now»
- ·Architecture: designed and documented
- ·Security: a dedicated senior's responsibility
- ·Long-term maintenance: system is designed for it
- ·Fits: production service, B2B platform, business-critical system
If your task is to check a hypothesis over the weekend, write a utility for internal use, or assemble a prototype: vibe coding fits, there are great tools. If you're building a product the business will run on,: this is engineering development with AI tools under senior-team control. Different categories, different risks and a different cost of error.
Numbers are realistic ranges from our experience, not «a guarantee». On a specific project it can be faster (simple logic) or slower (complex integrations, regulation, NDA). On the first review we give an honest estimate for your task.
Six effects: for the customer.
For the customer the term is not the point. Control is: seeing progress, understanding budget, owning the code and making decisions before a mistake becomes expensive.
Speed of shipping changes
From hypothesis to production: weeks, not months. The customer changes the idea in a week: we change it in a week. In a market where «faster-changing» wins, that's a direct competitive advantage.
Budget is clear before start
After scoping we give a realistic range: what goes into the first stage, which integrations affect price, what can be deferred. MVP reference starts from 1.2M RUB, without selling a junior team as «savings».
Transparency instead of a black box
Previously, a client might see the first result only at a demo three months later. Now they receive a weekly brief, clickable prototypes from week one and a current view of progress. This lets them steer the project using facts.
Engineering quality remains
A senior engineer owns the architecture, and the code goes through review and testing. AI handles boilerplate, types and templates. The engineer keeps more time for decisions that affect product reliability.
Flexibility to changing requirements
The market changed: the product must change. We show clickable progress early, so questionable decisions can be corrected before they become expensive. This reduces the risk of «three months later we saw the wrong thing».
Full ownership of the result
The client owns the code, documentation and infrastructure. We use open formats and languages without tying the product to a proprietary platform. If you bring development in-house or change vendors, you retain everything required to continue.
Six directions: development, integrations, legacy migration.
We take on work that needs an operational engineering result, from MVPs and integrations to internal systems and legacy migration. First we define the format and boundaries. Then we give an estimate, plan and a clear first stage.
Over 13+ years of engineering practice our team has gone through each of these directions on at least 3–5 projects. Not «let's try it now», but accumulated expertise.
Startups and MVPs
When you need to validate a hypothesis on real users: without six months of development. Production-ready MVP, not «a demo»: real auth, DB, testing, deployment. If the hypothesis fails: you spent 1.5 months, not 6.
Examples
Mobile applications
iOS, Android, cross-platform (React Native, Flutter). From customer service apps to corporate mobile workplaces. With everything needed: push notifications, offline mode, biometrics, deeplinks, backend integration.
Examples
Backend and integration projects
REST/GraphQL API, microservices, event-driven architecture, queues, caches. Integration layers between heterogeneous systems: CRM, ERP, billing, 1C, banking gateways, external APIs. When a company has a system «zoo» that needs to be tied into one living infrastructure.
Examples
SaaS platforms and products
Multi-tenant architecture, role model, billing and pricing plans, analytics, load for thousands of users. When the MVP has proven value and you need a stable platform. Full cycle: architecture → development → load testing → production operations.
Examples
Internal systems and automation
When a company has a pain no off-the-shelf product covers. Custom CRM/ERP extensions, partner cabinets, internal portals, BI tools, process automation with an AI layer on top.
Examples
Legacy migration → modern stack
The old system works, but it's getting expensive to maintain, hard to hire for, scary to change. We rewrite it onto a modern stack incrementally without stopping the business: restoring logic, moving critical parts and keeping control.
Examples
If the task does not fit any of the six directions, it does not mean we will not take it. On the first review we honestly say whether our format fits, another contractor is better, or a smaller scope should come first.
Six directions surrounding one product
Your product sits at the centre. On the left are directions where speed matters: an MVP, a mobile app and internal automation. Larger systems that need strong architecture and scale are on the right.
Your product
From idea to prod release. AI tools speed up routine, engineers hold architecture and quality
- Faster and cheaper than classical development
- Production quality from day one
- Tests and docs: in parallel
- Delivery agent free with development
- Team's engineering experience: since 2012
Every direction orbits around a specific product. We do not «do AI»: we build what must work in your business.
We deploy where you need: you control the data.
Where your system lives: you decide, based on your data and security requirements. Cloud for speed, your perimeter under data-residency law, hybrid, or a full in-house handoff. Four formats for different needs: for the IT director, that's control over data and access without losing time-to-start.
And yes: code, documentation, infrastructure remain your property: an open stack (PostgreSQL, Docker, standard languages), no vendor lock-in, no «works only with us». You can evolve it in-house or switch contractors without rewriting from scratch. Not a special advantage: just how it should work.
On a cloud provider
Yandex Cloud, Selectel, VK Cloud, Cloud.ru, AWS, GCP. Open stack (PostgreSQL, Redis, Docker, k8s): no proprietary lock-in. If tomorrow you switch clouds: move without rewriting.
Best fit
Most startups and MVPs. When strict data-residency requirements don't apply.
On your servers
The customer provides servers: we deploy there. Full control over data, infrastructure, access. Fits regulated industries: banking, healthcare, government.
Best fit
Corporations, government, regulated industries, banking and medical systems.
Hybrid model
Part on our cloud (typical), part in your perimeter (sensitive data). Balances deployment speed with control over critical parts.
Best fit
Mid-market and enterprise with fixed sensitivity zones: e.g. billing and PII separate from the rest of the system.
Full handoff in-house
After development the system is handed over to your team completely. Training, documentation, source walkthrough, knowledge base, help with hiring the first developers. You evolve it yourself.
Best fit
Companies that want in-house development. Often: after a successful MVP, when growth direction becomes clear.
We pick the specific format during scoping. Sometimes the first stage is faster in the cloud, while full implementation moves to your perimeter or a hybrid model. That is normal: the start should not tie you to one infrastructure.
Three engagement levels: with clear timeline, budget and control.
In development the customer often lives long in «trust the vendor» mode: signed the spec, wait, three months later see what came out. We structure the process differently: transparency instead of a black box: weekly demos, clickable prototypes from day one, clear progress and a record of architecture decisions. The client can see the work and steer the project.
Validate a hypothesis in 4-6 weeks
When you have a product idea but don't know if it'll fly. We build a production-ready MVP: real auth, DB, main scenario, basic design, deployment. Not «a prototype on a knee», but a system you can show to early customers.
The goal: in 6 weeks you understand whether there's product value on real users. If yes: we move to full development. If no: we'll say so, and you've saved 4 months and 2.5M.
Production system for thousands of users
When the MVP has proven value and you need a system that handles load, is secure, monitored, easy to extend. Not «the same with bells and whistles»: a qualitatively different project:
- ▪Growth architecture (microservices, queues, caches, replication)
- ▪Monitoring, alerts, logging, load testing
- ▪CI/CD pipeline and automated deployment
- ▪Security: role model, encryption, audit logs
- ▪Full documentation: architecture, operations, API spec
- ▪Training for your team to operate the system
- ▪Knowledge base handover and source walkthrough
On the customer's servers + team training + work process
When the system must live in your perimeter, and «handed off: enjoy» isn't enough. Enterprise is an engineering project at the corporate-group level:
- ▪Full installation and setup on your servers (on-prem or private cloud)
- ▪Local models: if required by security policy
- ▪Training for your dev, DevOps, ops teams
- ▪Process design for working with the system: roles, SLA, regulations
- ▪Handover of architecture memory, ADR docs, technical decisions
- ▪Support during the first 3-6 months of operation
- ▪Security audit and compliance with 152-FZ / industry requirements
At this level development stops being «we bought a product» and becomes a business-process transformation: your people learn to work with the system as part of daily work. This is the conversion of development from «the vendor's black box» into a direct, understandable process.
Order development: the Delivery agent is included.
The agent is included in the development service; while we work on the project, the Engineering Governance Agent is already working in the stack, connects to Git, Jira, chats and documentation, builds project memory and prepares a weekly engineering brief for the owner and CTO; connection and operation during this period cost zero rubles. It is part of our process: development with transparency from week one, not a «black box until acceptance».
What you get during development for free: a weekly engineering brief (what's done, risks, decisions taken), cumulative project memory (which stays with you forever), capture of architectural decisions from team chats, real-time progress visibility. After launch the agent continues in the standard maintenance model: from 40K RUB/month.
Details on the Delivery agent→This makes development and management oversight work as one process: from the first prototype week to operations and further growth.
When our development format isn't your fit.
If you want vibe coding or no-code over the weekend: this is not our format. We build production systems the business will run on, not a prototype that «runs, no idea how». For no-code and quick demos there are dedicated tools that are cheaper and faster for their tasks.
If you expect «a day-precise deadline guarantee»: we do not work that way. Discipline speeds up development, but a new task always carries uncertainty, so we give a realistic range and hold it. A promise of no more than 14 business days only makes sense for a standard task with clear boundaries.
If there's no product owner on your side: a fast result is unlikely. Someone needs to make product decisions promptly; a committee that meets every two weeks will turn the speed advantage into waiting. Appoint the decision owner before starting the project.
If you expect «AI will write everything for you»: this is not our format. AI tools help senior engineers, but do not replace engineering thinking. Architecture, security, business logic and quality stay with humans.
If your budget is 300-500K for «turnkey everything»: this is not our format. A production-ready MVP usually starts from 1.2M RUB. If the budget does not allow building a working product with architecture, tests and deployment, we will say so directly and suggest a more suitable path.
If any of the above describes you, mention it on the first call. Better to honestly decline than start a project that shouldn't have started. This is what «13+ years of the team's engineering practice» means: we know when to build and when not to.
Training after launch
Your team should be confident with the new tool
We show your team how to use the system, verify results and handle exceptions. The knowledge stays with you alongside the code and documentation.
See training optionsDescribe your task: we'll respond with a realistic estimate within 2 hours
We reply during business hours. On the first review we'll say where an agent can pay off, which metric proves it, and where budget is better not spent.