AI-native development: a first working prototype from 7 days.
We build MVPs, SaaS products, and internal systems through an agent workflow: AI accelerates code, tests, documentation, and checks, while senior engineers own architecture, security, and business logic.
We don't promise production in a week. In 7 days we show a working slice you can use to test the hypothesis.
10+ лет в IT50+ проектовSenior-командаPrivate-firstNDA по умолчанию
Fastest reply — on Telegram. Describe the task, we'll respond within 30 minutes.
Написать в TelegramHow this differs from traditional development
We don't think traditional development is bad. Some of the work that used to take weeks of manual effort can now be handled by agents — under engineer supervision.
Traditional development
- · A team manually walks the full path: analysis, code, tests, documentation, fixes.
- · The first prototype usually appears in 6–12 weeks.
- · A lot of budget goes to repetitive tasks and boilerplate implementation.
- · Quality depends on review and testing processes, and the team's seniority level.
- · Support is often designed only after the first release.
AI-native development
- · Team: a senior engineer + agent workflow + ad-hoc specialists when needed.
- · The first working slice can be ready in 1–2 weeks.
- · More budget goes into architecture, business logic, UX, and integrations.
- · An agent helps write code, tests, and documentation — but a human engineer reviews the decisions.
- · Support, logging, and evolution are baked into the architecture from day one.
Where AI-native shines.
AI-native delivers maximum impact when speed matters but you can't compromise architecture, security, or support.
Product teams and founder-led projects
You need to ship an MVP fast, validate a hypothesis, show a working contour to investors, the team, or first customers.
B2B SaaS and platforms
User accounts, subscription products, marketplaces, dashboards, integrations, roles, payments, APIs, and backend logic.
Internal company systems
CRM modules, operations dashboards, reporting, document workflows, backoffice, tools for staff and managers.
Enterprise and regulated industries
When security, roles, audit log, private contour, SLA, legacy integrations, and controlled rollout matter.
Legacy modernization
When the legacy system can't be thrown out, but needs to evolve, be documented, covered by tests, and gradually migrated to a modern stack.
From task to first release — 5 steps
Task discovery
AGENT + HUMAN
We start with a short conversation: an agent helps capture the inputs, and an engineer makes sure the goal, constraints, data, integrations, and definition of done are clear.
Technical scope
SENIOR ENGINEER
We turn the description into a technical scope: what we build, how we judge readiness, and what the constraints are.
Code assembly
AGENT
The agent prepares code, tests, and technical documentation. The engineer breaks down the task, sets the boundaries, and verifies we're building the right product.
Quality review
SENIOR ENGINEER
The senior engineer reviews architecture, security, and code readability — not just a “works / doesn't work” check.
Release
AGENT + HUMAN
We ship the first working release: with a how-to, clear limitations, and a list of next improvements.
What you'll get in the first 7 days
7 days is not a promise of a “turnkey business.” It's a short sprint that lets you see the working mechanics of the product fast and decide: grow it, change direction, or stop.
- ✓ A working slice of a web app, service, or internal tool
- ✓ 1–3 key user scenarios you can walk through by hand
- ✓ A draft architecture: data, roles, integrations, constraints
- ✓ A repository with the code, run instructions, and a list of technical decisions
- ✓ A plan for the next stage: what's needed for the pilot or production
7 days
to the first working prototype
AI-native workflow
code, tests, and documentation — faster
1–2
senior engineers on the project
When development needs a governance layer.
AI-native is useful not just for speed. It makes development transparent: capturing decisions, holding project memory, surfacing risks, and reducing dependence on a single CTO or tech lead.
- ▪Project memory across chats, tasks, PRs, and documentation.
- ▪Daily / weekly engineering brief for CEO and CTO.
- ▪Risks on deadlines, blockers, and dependencies — before the deadline, not at status.
- ▪Handover during lead or team changes — context doesn't leave with the person.
- ▪Changelog and release notes without manual assembly.
We move legacy systems to a modern stack
If you have a system that “sort of works” but you're afraid to touch it, we don't propose rewriting it all in one go.
First we understand the current mechanics, capture the critical scenarios, build a test layer, and migrate the system in pieces — alongside the working product.
in pieces
migration without the “big bang”
tests
before we rewrite critical logic
no downtime
migrating alongside the live system where technically feasible
Integrations without unnecessary pain
We connect external systems carefully: via APIs, webhooks, queues, files, or a closed perimeter.
We connect CRMs, payments, email, messengers, analytics, AI models, and internal systems.
If there's no ready API — we design a safe workaround.
Roles, access rights, logs, and error handling are designed in from the start.
We work in the cloud or inside your perimeter — if your security requires it.
Who is responsible for what on our team
The agent does
- → Writes repetitive code
- → Creates first-pass tests
- → Checks against the technical scope
- → Drafts documentation from the code
- → Prepares database migrations
- → Prepares integration code
The engineer does
- → Makes architectural decisions
- → Decides which approach to take
- → Reviews code quality
- → Reviews security
- → Thinks about scaling
- → Talks to the client
What exactly we can build
AI-native is not a separate type of product. It's a way to build ordinary business systems faster and better: MVPs, SaaS, dashboards, integrations, internal tools, and AI features.
We don't sell 'development in general'. We take projects with a business process, product hypothesis, or engineering system that can be brought to a working contour and evolved.
MVPs and SaaS products
We build first versions of products: user cabinets, admin panels, subscription services, marketplaces, B2B platforms, and tools for internal teams.
Internal business systems
We build systems for company processes: CRM modules, control panels, document workflows, operational cabinets, reporting, and employee workstations.
Integrations and automation
We connect products to CRM, ERP, payments, email, messengers, analytics, warehouses, spreadsheets, partner APIs, and internal databases. Especially useful when the company already uses several systems but data and statuses live separately.
AI features inside the product
We embed AI where it genuinely matters: document search, draft generation, request classification, data analysis, and assistants for employees and customers.
Legacy modernization
We unpack legacy systems, document the logic, cover critical scenarios with tests, and gradually migrate the product to a modern stack — with controlled tech debt and minimized production downtime.
Admin panels, dashboards, and backoffice
We build interfaces where the team manages data, requests, users, documents, processes, and metrics — without manual spreadsheet juggling.
The stack we work with
We're not tied to one framework for the sake of it. We pick the stack based on the task, the client's team, the security requirements, and long-term support.
Frontend
- React
- Next.js
- TypeScript
- Tailwind CSS
- shadcn/ui
- Vite
We build fast interfaces, user cabinets, admin panels, dashboards, and client applications.
Backend
- Node.js
- NestJS
- Python
- FastAPI
- Django
- REST API
- GraphQL
- WebSocket
We build APIs, business logic, background jobs, integrations, queues, and data-processing services.
Databases & Storage
- PostgreSQL
- MongoDB
- Redis
- ClickHouse
- S3-compatible
- Supabase
- vector DBs
We design data storage, search, logs, analytics, file storage, and RAG environments.
AI / LLM
- OpenAI
- Anthropic
- local models
- embeddings
- RAG
- agent workflows
- prompt/context engineering
We embed AI in the product as a controlled feature: with roles, logs, constraints, and human-in-control.
Infrastructure
- Docker
- CI/CD
- Linux servers
- cloud infrastructure
- monitoring
- backups
- private deployments
We can deploy in the cloud, on a dedicated server, or inside the client's perimeter.
Integrations
- CRM
- ERP
- Telegram
- payment systems
- Google Workspace
- Bitrix24
- amoCRM
- 1C
- webhooks
- external APIs
If there's no ready-made API — we design a safe workaround.
Five principles guiding our decisions
Release beats the pitch
(Ship > Slide)
A working product matters more than a pretty slide. We aim to show something you can touch as early as possible.
AI is the work environment, not a magic button
(Agent-Native)
Agents help write code, tests, documentation, and run checks. But they don't replace engineering judgment.
Privacy first
(Private-first)
Client data, code, and IP stay isolated. Where it matters — we work inside your perimeter.
Honest velocity
(Honest Velocity)
We move faster thanks to the agent workflow and the absence of unnecessary steps — not by cutting corners.
Humans make the calls
(Human-in-control)
AI proposes, assembles, and verifies. Architecture, security, and the final decision stay with the senior engineer.
Our filter: honest, expert, bold
We don't pretend AI is a magic button. And we don't pretend old processes will live forever.
Our job is to take the product, the process, or the internal pain apart down to the mechanics — and rebuild it: faster, simpler, with AI agents and engineering oversight.
Honest
If it's a prototype, we call it a prototype. If you need a pilot — we say so. If the task isn't for us — we don't take it just for the money.
Expert
We think about architecture, data, integrations, security, roles, logs, support, and total cost of ownership.
Client-safe
AI accelerates the work but doesn't go on autopilot. Code, access, releases, and key decisions all go through human review.
Bold
We don't come to “digitize chaos”. We come to take apart an old process and rebuild a new one — without extra bureaucracy or theatre.
Four levels — pick the one you need
Fast prototype
Timeline: from 7 days
Budget: 350–800K RUB
A working slice for one hypothesis: UI, core logic, a simple backend, a demo on test data.
Who: When you need to quickly test an idea, show the mechanics to the team, or decide whether to go into full development.
Product pilot
Timeline: 3–6 weeks
Budget: 800K – 2M RUB
A pilot on real data and scenarios: roles, integrations, baseline security, logs, user feedback.
Who: When the hypothesis needs to be validated inside a real workflow, not just a demo.
Production development
Timeline: from 2 months
Budget: from 2M RUB
A product or internal system ready for real users: architecture, tests, monitoring, documentation, support.
Who: When you need not a prototype but a system you can actually rely on.
Enterprise perimeter
Timeline: from 3 months
Budget: from 3M RUB
Systems with heightened requirements for security, integrations, SLAs, access, audits, and operation inside the client's perimeter.
Who: Enterprise, regulated industries, complex internal processes.
These ranges are a reference for typical projects. After a short task review we share a realistic range and explain what drives the budget. No “50/100/200K” cookie-cutter packages that reflect nothing.
If your task falls into this list — we'll say so up front
We don't take work we can't do well — and we don't take it just for the money. If you fall into one of the items below, we'll point you to who's worth talking to instead.
- ✗ Hardware, embedded systems, and device firmware
- ✗ Sports betting, gambling, dark patterns
- ✗ Personal data on a public LLM — only a closed perimeter
- ✗ Pixel-perfect markup without a product, logic, or an engineering task
- ✗ Endless legacy support without the right to diagnose and plan migration
- ✗ Voice cold-calling
For partners: engineering under your context.
If you're a consultant, integrator, digital team, or CRM/ERP partner — plug DevNeuroX in as an AI-native engineering team under your client's task.
White-label delivery
You own the client — we handle technical design, development, integrations, AI agents, and support.
Joint sales
We run discovery together, scope the pilot, defend the technical part, and divide responsibilities.
Technical presale
We quickly estimate the task, risks, stack, budget range, and a realistic first pilot.
Pilot → production
We handle the path from a short prototype to a production contour: architecture, integrations, security, audit log, roles, support.
We share project details under NDA. Tell us your industry — we'll pick the relevant cases.
Frequently asked questions
- How do you guarantee code quality when AI is involved in development?
- Quality isn't guaranteed by who typed the code by hand. It's guaranteed by architecture, review, tests, security checks, and the responsibility of a senior engineer. AI helps write code, tests, and documentation faster, but every important part goes through human review. For critical logic we add tests, logging, and a separate pre-release check.
- What about code security and intellectual property?
- Code is written inside our isolated environment, without anything leaving it. On request — we work inside your perimeter. All code rights are transferred to you by contract, with no hidden licensing traps.
- Do you work on fixed price or hourly rate?
- Usually fixed price — we know how to estimate scope through a short task review. We use hourly billing only for tasks with unpredictable scope (research work, long-running integrations).
- Can you join our existing team rather than running the whole project?
- Yes. One or two of our engineers join your team for 1–3 months and bring the agent workflow with them. In parallel, we train your team to work in this approach.
- What if we have a legacy system that needs maintenance rather than a new product?
- We take it on. The audit takes about a week. After that we gradually rewrite the critical parts with the agent workflow, migrate the tests, and drop outdated dependencies. See the block “We move legacy systems to a modern stack.”
- What technology stack do you work with?
- Usually: TypeScript / Python at the core, your choice of databases (PostgreSQL, MongoDB, Clickhouse), cloud infrastructure (AWS, Yandex Cloud, or yours). AI models — OpenAI, Anthropic, local. If your stack is different — we typically plug in, we'll discuss it on the review call.
- How much does the first 7-day prototype cost?
- Depends on the task complexity. Reference range — 350–800K RUB for a prototype ready to show users. After the task review we share a realistic range and explain what drives the budget.
What happens after you submit
A transparent process — no “I'll send a proposal and disappear” and no pressure on the decision.
- 01
2 hours
We'll reply on your chosen channel with clarifying questions about the task.
- 02
2–3 business days
A free 1-hour video review session, with no commitment.
- 03
5 business days
A concrete proposal: what we build, the timeline, and the price.
- 04
Go / no-go decision
Yours either way, no pressure. If we're not the right fit — we'll point you to colleagues who are.