ITNeuroNet
ITNeuroNet — Expert-led AI software studio

Decide what to build. Then build it with experienced technical guidance.

We help founders plan their first product, design the system, and develop it with AI assistance. Evgeniy Medvedev leads the technical work and reviews the implementation.

Led by Evgeniy Medvedev — 17+ years working across software delivery, architecture, infrastructure, product, and digital marketing.

Discuss your projectBook a Discovery Call

Start with a short assessment: scope, risks, timeline assumptions, and how we should work together.

project map / v1live
idea → architecture → deliveryno black-box outsourcing
AI-native time to market

As models improve, slow delivery becomes the risk.

Stronger AI models are changing how quickly products can be planned and built. The companies that benefit most are usually not the ones producing the most code, but the ones that can turn a market signal into a tested product change, review it properly, and ship it with less delay.

ITNeuroNet uses one connected workflow to shorten that path: defining scope, planning the architecture, breaking work into tasks, building with AI assistance, testing, review, and handoff all work together.

How we shorten the loopDiscuss your project
AI-native delivery

Make product changes easier to deliver and check.

We help you move from first discussion to handoff in one continuous process: clarify the goal, define the scope, design the architecture, build the system, test it, review security, and prepare the handover. Evgeniy Medvedev leads the technical decisions and reviews the work with AI support, so you receive a system you can inspect, maintain, and extend.

Discuss your projectHow we shorten the loop
  1. 01

    Clear intent in writing

    scope · architecture · contracts

  2. 02

    Build with tests in mind

    types · tests · review throughout

  3. 03

    Clear security boundaries

    data · providers · agent permissions

  4. 04

    Work you can see as it moves

    private workspace · change history

Founder situations

When founders come to ITNeuroNet

Many clients come in before the project is fully defined. They may have pressure from investors, customers, or an internal deadline, and they want to avoid building the wrong thing.

  1. 01

    I have an idea, but I do not know what the right MVP should include.

  2. 02

    I need the architecture, the documents, and a delivery plan before I spend more on development.

  3. 03

    I want to move faster without losing visibility or quality.

  4. 04

    I need AI or data capabilities, but only where they add practical value.

  5. 05

    I need to work carefully with healthcare or sensitive data.

Services

What ITNeuroNet helps you do

Each service helps you answer a specific question before you commit further: what to include, how the system should work, how to organize delivery, whether AI will help, whether your data is ready, where the main technical risks are, and how to handle sensitive information.

01 / service

Validate Your Product Idea

Product & Technical Discovery

Turn a vague product idea into a defined first version, an initial technical direction, and a practical plan for decisions.

02 / service

Plan Your MVP

MVP Sprint

Define the smallest credible version of your product and turn it into a documented plan that is ready to build.

03 / service

Turn Requirements Into Delivery

Architecture & Delivery Sprint

Turn product requirements into system architecture, data models, API contracts, task plans, and documentation the work can be reviewed and built from.

04 / service

Build Your AI-Assisted MVP

AI-Assisted Product Development

I help you plan the system, document the work, and build it with AI support while I review the architecture and key technical decisions.

05 / service

Automate Workflows With AI Agents

AI Agents & Workflow Automation

Design AI agents and automations that work with your real data, tools, and business processes.

06 / service

Make Your Data AI-Ready

Data Engineering & Analytics

Build the data foundation needed for reliable reporting, operational decisions, automation, and AI.

07 / service

Audit or Guide Your Technical Direction

Technical Audit & Fractional CTO

Get an experienced technical review of your architecture, delivery risks, roadmap choices, vendor work, and the quality of execution.

08 / service

Build Safely Around Healthcare and Sensitive Data

Healthcare & Compliance-Aware Systems

Design privacy-first systems with documentation, auditability, and HIPAA-aligned, NIST-aware safeguards where the context requires them.

Not sure which service fits? Start with a short review.

See all services
Approach

How delivery works

You work in one managed setup: documentation, system design, task planning, a private workspace, and AI-supported implementation connected in the same process.

  1. step 01

    Discovery & Hypothesis Framing

  2. step 02

    Requirements & Architecture

  3. step 03

    Delivery Plan & Workspace

  4. step 04

    Controlled AI-Assisted Execution

  5. step 05

    Demo, Iterate, Prepare for Production

Private delivery workspace

See what is being built and why

This is the working space where documents, technical decisions, and AI-agent tasks stay connected. You can follow real progress through the work itself, not through polished status updates.

  • 01

    Documentation stays connected to tasks.

  • 02

    Progress is visible without constant status meetings.

  • 03

    Work done by AI agents follows a defined process, is reviewed, and is linked to the documents and tasks used for delivery.

  • 04

    Decisions, assumptions, and risks are easier to trace.

Case studies

Proof from real systems

Selected public examples. Sensitive implementation details stay private; the parts that can be shared safely are shown here.

01 / caseHealth-tech platform

TodaiHealth

Shows the ability to deliver health-tech work from a clear specification, with strong system design and thorough documentation.

  • 18 microservices
  • 615 API operations
  • 147 DB tables
02 / caseAI speech analytics

Echo Speech Analytics

Production AI analytics platform with CRM and telephony integrations.

  • 273 endpoints
  • 11-stage pipeline
  • 9 runtime services
03 / casePrivacy-first AI voice

AIspeaky

Shows a voice system design that puts privacy first and keeps speech workflows portable across providers.

  • 5,300+ files
  • 9 backend domains
  • Privacy relay
04 / caseKnowledge management

BrainNet / Second Brain

AI-assisted documentation and knowledge management at scale.

  • Documentation graph
  • AI-assisted authoring
  • Internal scale
All case studies
Compliance & Healthcare

Built with healthcare and sensitive-data constraints in mind.

Privacy-first system architecture informed by HIPAA and NIST, for products that need to consider compliance. This work covers technical design and architecture. It does not replace legal review or formal certification.

Compliance approach
About

An expert-led studio, not a general-purpose agency.

Evgeniy Medvedev leads the work, makes the senior technical decisions, and owns delivery. AI agents help with implementation, documentation, analysis, testing, and task execution inside a defined workflow with human review.

You work directly with a solo senior expert, supported by AI agents for execution. This is not a large agency model, and it is not unstructured freelance coding.

Articles

Practical thinking for founders and CTOs

Notes on defining an MVP, designing architecture, using AI in delivery, and building systems that handle sensitive data.

AI-Assisted Delivery

Why time-to-market is changing in the AI-native software era

Speed no longer comes from typing code faster. It comes from turning product questions into system changes you have checked, can explain, and can deploy safely.

Read article
AI-Assisted Delivery

Faster code is not faster delivery

Why faster code generation does not shorten a project by itself, and what else must be in place for AI-supported development to save time instead of creating rework.

Read article
AI-Assisted Delivery

Why AI agent projects fail before the first prompt

When an agent performs badly, the problem is often not the prompt. More often, its role is unclear or it lacks the right tools, permissions, data limits, and review process.

Read article
Next step

Need a clearer technical plan for your product idea, existing system, or AI workflow?

Start with a short assessment. You will get a clearer scope, key risks to address, a realistic view of timing, and a recommendation on how to work together.

Discuss your projectBook a Discovery Call