AI-Native Software Engineering Studio · team experience since 2012
AI-native development is an evolution from 13+ years of engineering practice,
not a buzzword of recent years.
Our team has been building systems since 2012: 50+ projects across industries, from government structures to startups. We've seen the paradigm shifts: waterfall, agile, scrum, lean, MVP. AI-native is not «the next buzzword». It's an engineering answer to changed requirements: shipping speed is no longer «a nice bonus», it's a competitive advantage. A senior team + AI as an amplifier = the quality of classical engineering plus the speed that used to be impossible.
13+ years of team's engineering expertiseWe know how classical development is done — and we see exactly where AI-native overtakes it. The comparison — in the next chapter, on a concrete project.
50+ projects · government and startupsComplexity range from regulated government systems to startup MVPs with burning time-to-feature.
The source code stays with youAll code, documentation, infrastructure — the customer's property. No vendor lock-in, no «works only with us» conditions.
Delivery agent includedFor AI-native development customers the Engineering Governance Agent is plugged in for free during the project. Owner-level transparency at no extra cost.
How we arrived at AI-native — through 13+ years of the team's engineering practice.
Our team's engineering practice began in 2012. At first — custom development for government structures and large corporations: document workflow systems, regional portals, billing, reporting, secure contours. That's about discipline: 200-page specs, long architecture reviews, load testing, formal acceptance. A good foundation — we know how to build big reliable systems.
In parallel — startups. A different world. MVP in 2 months, a feature in a week, daily releases, A/B tests, rewriting architecture on the fly. Here the gov-project discipline hurts — while you write the spec, the idea has aged out. Here you need speed and adaptability. Over 13+ years our team has worked with both worlds: we know when heavy corporate engineering is needed, and when — startup speed. The most interesting thing is — these two worlds long seemed incompatible.
In 2023 we started systematically using AI tools in real projects — not as «let's try», but as part of the working process. And we saw something that wasn't there before: the false dilemma «fast or quality» disappeared. A senior engineer plus the right AI tools delivers in a week what a classical team delivers in a month — without losing quality. Not because «AI writes code for us». But because AI removes the routine part from the engineer: boilerplate, documentation, tests, refactoring, search across a large codebase, generation of typical solutions. The engineer spends time where decisions are needed, not where clicks are.
After that we became an AI-Native Software Engineering Studio. This isn't marketing — it's the work process. Every project we deliver with a senior engineer team plus our own AI infrastructure for development. This gives us three things at once: startup-team speed, corporate-grade quality, and adaptability to change that neither of the two «old» worlds had.
Year — team's engineering practice started
Years of team's engineering experience
Completed projects
And why now
Competitiveness today is the speed of shipping change.
It used to be okay to ship a product once a year — that was the norm. Today the «correct» product doesn't win, the one that changes faster than competitors does. A customer who can't ship an update in a week loses to one who can — even if the first builds «higher quality». AI-native is the engineering answer to this new market requirement. Not «here's some AI». Rather, «here's a team that can ship quality changes quickly».
Same MVP — classical development vs AI-native.
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 an AI-native one, by our experience.
Discovery and spec
Classical
3–4 weeks
A business analyst writes 50–100 pages of spec. Sign-offs. Clarifications. By the end — half of it is already outdated.
AI-native
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 makes mockups. Review. Edits. Design system from scratch. Handover to development. More edits at the boundary. Slow and expensive.
AI-native
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
2 backend, 2 frontend, devops. Each feature — a separate sprint. Boilerplate is written by hand. Tests are written separately after the feature. Deadlines slip regularly.
AI-native
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 separately. Bug fixes separately. Regression. Test plans. Customer acceptance. Some bugs surface only in production.
AI-native
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
AI-native
≈ 1.5–2 months
2 seniors + AI, ~1.2–2M RUB
Where the savings come from
Why AI-native is 2–3× faster — four concrete mechanisms.
A classical team doesn't «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 the seams and iterations after reviews. AI-native attacks 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 the classical model the client sees the result on a demo in 2–3 months — and often it's «not what I meant». Then — big rewrites. With AI a clickable prototype appears in week one. The client steers the idea while it's cheap, not after the code is written.
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»
Classically, testing and documentation are separate end-of-project phases that usually «ran out of time». AI generates test coverage and tech docs in parallel with development. By acceptance time tests exist, README is current, API spec matches reality. Fewer bugs in prod, less «I'll write the README next week».
This isn't «AI does it in 30 seconds» and isn't «magic». It's the same engineering at senior level — but with four specific losses removed that eat most of the budget in classical models. Code, architecture, tests, security — at the same level. What changes is how much engineering decision time the engineer has left.
Important distinction
AI-native is not vibe coding.
The term «vibe coding» is everywhere right now: write a prompt — AI generates code — you barely understand what's inside but it runs. It's a valid approach in its niche. But it's not what we do. The difference is fundamental — and the customer needs to see it before making a decision.
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
AI-native (what we do)
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 AI-native. Different categories. Different risks. 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 pilot we give an honest estimate for your task.
Six effects — for the customer.
AI-native isn't «the same, but faster». It's a different logic of the relationship with development. The customer stops being «a trusting party» and becomes a participant in the process.
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.
Cost — 2-3× lower than classical
Not «a cheap junior team», but senior + AI: fewer people, less time, the same expertise. Realistic MVP ranges: 1.2–2M instead of 3–5M. The exact range — on the pilot, after scoping.
Transparency instead of «a black box»
Before, the customer saw development as a black box: «we're working, don't bother us». Three months later — a demo, and either «yes» or «not really». Now — a weekly digest, clickable prototypes from week one, real-time progress visibility. Management, not faith.
Quality stays classical
Speed isn't at the expense of quality. The same architecture with a senior in the architect role. The same code reviews, tests, documentation. The routine part of the profession (boilerplate, types, templates) goes to AI, engineering thinking stays with the human.
Flexibility to changing requirements
The market changed — the product must change. In the classical model that's painful: rebuilding architecture is slow and expensive, easier to «live to release». AI-native lets you rebuild big chunks faster without catastrophe. This changes the very approach to product: you can validate hypotheses for real, not «later when there's time».
Full ownership of the result
All code, documentation, infrastructure — the customer's property. Open formats and languages, no proprietary platforms. If tomorrow you decide to bring development in-house or change vendors — you have everything to do it. No vendor lock-in, no «works only on our platform».
Six directions — everything that falls under development.
AI-native is a development approach, not «a product category». Applies to any of the directions below. From a startup MVP to legacy migration at government scale. All of this is within our reach. Quickly. Classical in quality. At a competitive price.
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. AI-native makes this faster and more accurate: parsing legacy code, restoring logic, porting without losses.
Examples
If your task doesn't fit any of the six directions — it doesn't mean «we won't take it». In the pilot we scope the task and honestly say: does our format fit or is another contractor better.
Six directions surrounding one product
At the centre — what you're building. Left — directions where you need results fast: MVP, mobile app, internal automation. Right — larger systems where architecture and scale matter.
Your product
From idea to prod release. AI tools speed up routine, engineers hold architecture and quality
- 2–3× faster 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 don't «do AI» — we build what you need, in an AI-native approach.
Source code, infrastructure, team — all yours.
This isn't fine print, it's a base principle. Everything we build for the customer is the customer's property. Code, documentation, DB schemas, deploy scripts, configs, tests, git history. No «works only with us», no proprietary platforms, no hidden SaaS lock-in.
This matters for two reasons. First — legal: the IP is yours, and you can evolve the system however you want, hand it to another contractor, bring it in-house, sell it as a company asset. Second — technical: an open stack (PostgreSQL, Docker, standard languages and frameworks) doesn't depend on whether we remain in business. In 5 years your system won't become «unsupported, vendor gone».
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. Often we use format A for the pilot (quick start), and switch to B or C for full implementation. That's normal — the pilot doesn't «tie» you to one infrastructure.
Three engagement levels — and the move from «black box» to a process.
In classical development the customer lives long in «trust the vendor» mode: signed the spec, wait, three months later see what came out. AI-native enables something different: development stops being «a black box». Weekly demos, clickable prototypes from day one, clear progress, an architecture decisions document instead of «ask the tech lead». The customer becomes not «a trusting party», but a partner who sees and steers.
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.
Pilot 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.
Not a separate service to pay extra for. While we develop your project, the Engineering Governance Agent is already working in the stack: connecting to Git, Jira, chats and documentation, accumulating project memory, preparing the weekly engineering brief for the owner and CTO. The cost of connecting and running the agent during development — zero rubles. It's part of the AI-native approach: 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 is the «complete AI-native offer»: development + automatic management oversight via the AI agent. Not two separate services, but a single pipeline from the first prototype week to multi-year operations.
When AI-native development isn't your fit.
If you want vibe coding or no-code over the weekend — this isn't our format. AI-native doesn't cancel engineering, it makes it more efficient. We build production systems the business will run on — not a prototype that «runs, no idea how». For vibe coding tasks there are dedicated tools (Cursor, Bolt, v0), for no-code — Tilda, Bubble, Glide. Cheaper and faster for their respective tasks.
If you expect «a day-precise deadline guarantee» — this isn't our format. AI-native accelerates development but doesn't cancel uncertainty on a new task. We give realistic ranges and hold them. Hard «no more than 14 business days» is either a lie or a very typical task at a premium.
If there's no product owner on your side — AI-native won't work. Speed only works when there's a person who quickly decides «yes / no». If decisions go through a committee every two weeks — all the speed is eaten by waiting. Find the decision-maker first, then start.
If you expect «AI will write everything for you» — this isn't our format. AI-native is a senior engineer team plus AI tools. The senior remains the core of the process: architecture, security, business logic, quality — all stay with the human. AI removes routine, doesn't replace engineering thinking.
If your budget is 300-500K for «turnkey everything» — this isn't our format. At least a 1.2M pilot to get a production-ready MVP. If there's no budget for the pilot — we'll say so, and recommend a freelancer or an off-the-shelf solution.
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.
We don't just deploy — we teach people to use AI properly.
In 2–3 years AI tools will be the standard in engineering and business teams. Whoever starts learning now will gain a competitive advantage that latecomers won't have. That's why we have a separate direction: training client teams, so that AI tools and agents don't become «a toy for 3 months», but turn into part of the working process.
Corporate programs for adopting AI in development
For company engineering teams that want to switch to the AI-native approach. A 2–4 week program: review of the team's stack, selecting AI tools for the tasks, training on using them, ramp-up to regular production use. Not a «theoretical course», but a real shift in how the team works.
CTO, tech leads, developers · teams 5–50+
Client team onboarding for working with AI agents
Once we've deployed Sales, Personal, Support, RAG or another agent — we train your team to work with it well. How to adjust scenarios, write prompts for typical tasks, handle complex cases, give the agent feedback for improvement. Without this even a well-deployed agent gets «abandoned» in 3 months.
Client operational teams · typically after agent deployment
«Where to start with AI» mentor sessions for executives
Short individual sessions with CEOs, CTOs, business owners: which AI tools actually work, where to start in your company, how to link AI to business goals, how not to burn budget on «let's try something trendy». Sessions — 90 minutes, with a concrete roadmap as the output.
CEO, CTO, owners · 90-minute sessions
Open learning materials in the Telegram channel
A free column in our Telegram channel @dxaiblog. AI tool reviews, practices from real projects, mistakes and how to avoid them, reviews of new models and platforms. Anyone can subscribe — engineer, owner, product manager. This is the public part of our education, no commitment.
Anyone exploring AI · public channel · free
Training cost is discussed individually — depends on team size, format (online/hybrid/corporate visit), program depth, and whether you need a process built from scratch or accompaniment of an existing one.
If you want to start small — subscribe to the Telegram channel @dxaiblog. All materials there are free. That's our «step zero» of training.
Describe your task — we'll respond with a realistic estimate within 2 hours
We reply within 2 hours during business hours. In the pilot call we'll show where the agent pays off and where it shouldn't be tried.