ITNeuroNet
ITNeuroNet — Expert-led AI software studio

AI software studio for founders who need clarity before they scale engineering.

ITNeuroNet turns vague product ideas into documented MVP scope, architecture, delivery plans, and controlled AI-assisted execution.

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

Request MVP Sprint AssessmentBook a Discovery Call

Start with a short assessment — scope, risks, timeline, collaboration model.

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

When models get stronger, slow delivery becomes the risk.

Strong models are changing the economics of product delivery. The companies that adapt fastest will not be the ones generating the most code, but the ones that can turn a market signal into a tested, reviewable product change with less delay.

ITNeuroNet uses an AI-native delivery process to shorten that path: scope, architecture, tasks, implementation, tests, review, and handoff are designed as one system.

How we shorten the loopRequest MVP Sprint Assessment
AI-native delivery

The next advantage is not more code. It is faster, safer adaptation.

We shorten the whole delivery loop — discovery, scope, architecture, implementation, testing, security review, handoff, feedback — under senior architectural control. The system that ships stays reviewable and ready to keep evolving.

Request MVP Sprint AssessmentHow we shorten the loop
  1. 01

    Documented intent

    scope · architecture · contracts

  2. 02

    Test-aware build

    types · tests · review in the loop

  3. 03

    Secure boundaries

    data · providers · agent permissions

  4. 04

    Visible delivery

    private workspace · change trail

Founder situations

When founders come to ITNeuroNet

Most clients do not arrive with a clean specification. They arrive with uncertainty, pressure, and a product bet they do not want to get wrong.

  1. 01

    I have an idea, but I do not know what the real MVP should be.

  2. 02

    I need architecture, documentation, and a delivery plan before I invest further.

  3. 03

    I want faster execution without losing visibility or quality.

  4. 04

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

  5. 05

    I need to work carefully around healthcare or sensitive data.

Services

What ITNeuroNet helps you do

Each engagement reduces a specific kind of uncertainty: scope, architecture, delivery planning, AI usefulness, data readiness, technical risk, or sensitive-data constraints.

01 / service

Validate Your Product Idea

Product & Technical Discovery

Turn a vague idea into clearer scope, first technical decisions, and a practical next-step plan.

02 / service

Plan Your MVP

MVP Sprint

A structured sprint that produces a documented, buildable MVP plan with scope, architecture, and delivery model.

03 / service

Turn Requirements Into Delivery

Architecture & Delivery Sprint

Move from requirements to system design, data model, API contracts, and a task plan ready for execution.

04 / service

Build Your AI-Assisted MVP

AI-Assisted Product Development

Implementation inside a controlled AI-assisted workflow with senior review, tests, and documented decisions.

05 / service

Automate Workflows With AI Agents

AI Agents & Workflow Automation

Apply AI agents where they actually shorten a workflow, with clear boundaries, monitoring, and fallbacks.

06 / service

Make Your Data AI-Ready

Data Engineering & Analytics

Shape pipelines, schemas, and quality gates so analytics and AI systems can operate on reliable data.

07 / service

Audit or Guide Your Technical Direction

Technical Audit & Fractional CTO

Independent review of architecture, delivery, risk, and team setup with concrete remediation options.

08 / service

Build Safely Around Healthcare and Sensitive Data

Healthcare & Compliance-Aware Systems

HIPAA-aligned, NIST-aware, privacy-first architecture for products that handle sensitive information.

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

See all services
Approach

How delivery works

Documentation, architecture, task planning, private workspace, and AI-assisted execution as one controlled system.

  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

Transparent delivery, not black-box outsourcing

The workspace where documentation, decisions, and AI-agent task execution live together. Clients see structured progress, not status theater.

  • 01

    Documentation stays connected to tasks.

  • 02

    Progress is visible without constant status meetings.

  • 03

    AI-agent work is structured, reviewed, and tied to delivery artifacts.

  • 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 safely shared are here.

01 / caseHealth-tech platform

TodaiHealth

Spec-driven health-tech delivery with deep architecture and 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

Privacy-first voice architecture with provider-agnostic speech workflows.

  • 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.

HIPAA-aligned, NIST-aware, privacy-first, compliance-aware architecture. Technical and architectural in nature; not a substitute for legal review or formal certification.

Compliance approach
About

An expert-led studio, not a generic agency.

Evgeniy Medvedev is the senior technical partner and delivery owner. AI agents support implementation, documentation, analysis, testing, and task execution inside a structured workflow under senior review.

The model is honest: a solo expert-led studio with controlled AI-agent delivery — not a large traditional agency, and not freelance development.

Articles

Practical thinking for founders and CTOs

Field notes on MVP clarity, architecture, AI-assisted delivery, and sensitive-data systems.

MVP Clarity

How to define an MVP before you start development

Most MVPs fail not in code but in scope. A working definition founders can use before hiring or building.

Coming soon
Architecture

Architecture before acceleration: why AI-assisted teams need design first

AI-assisted delivery amplifies whatever structure you start with. Without architecture, it amplifies the wrong things.

Coming soon
Healthcare

Designing privacy-first AI systems for sensitive data

Practical patterns from health and voice work: relay layers, provider-agnostic interfaces, and conservative defaults.

Coming soon
Next step

Have an idea, system, or AI workflow that needs a clearer technical path?

Start with a short assessment to clarify scope, risks, timeline assumptions, and the right collaboration model.

Request MVP Sprint AssessmentBook a Discovery Call