Approach

Architecture before acceleration

AI-assisted development works well when the basics are clear first: what the product needs to do, where the system starts and ends, how the data is shaped, how the APIs should behave, and what needs to be built in what order.

ITNeuroNet starts with documentation and system design. First we remove avoidable uncertainty, then we use AI tools inside a clear technical plan.

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Delivery pipeline

Stage 1

Documentation

PRD + hypothesis

Stage 2

Architecture

System + data model

Stage 3

Tasks

Planned + reviewed

Stage 4

Execution

AI-assisted + gated

Process

Eight stages, one controlled delivery system

Each stage produces a concrete artifact. AI accelerates execution inside the structure these artifacts define.

Eight-stage delivery blueprint, from Hypothesis to Handoff — Delivery blueprint

step 01Hypothesis brief

Discovery & Hypothesis Framing

We spell out the core product bet: who the product is for, what should improve for them, and which assumptions could cause trouble if they turn out to be wrong.

hypothesis · v0.1draft

bet

Voice notes → CRM summaries, in < 60s, with PII relay.

who · field SDRs
edge · noisy audio
kill · no time saved

step 02PRD

Product Requirements

We turn that into a structured PRD with scope, user flows, acceptance criteria, and a clear list of what will not be built in this phase.

PRD · v1reviewed

§1. Scope · session capture, transcript, CRM push
§2. Roles · SDR, manager, admin
§3. Out-of-scope · custom voice models, BI dashboards
§4. Acceptance · transcript p95 < 60s; PII never leaves relay
§5. Risks · audio quality, vendor lock-in, PII drift

step 03Architecture spec

Architecture & System Design

Before building features, we map the system: where one part ends and another begins, which services are needed, how they connect, and why each technical choice was made.

architecture · systemv1

step 04Data model + API contract

Data Models & API Contracts

We define the entities, relationships, and API contracts so frontend, backend, and AI-assisted execution share the same source of truth.

contract · /v1/sessionsschema

POST /v1/sessions
  → 201 { id, tenant, audio_uri }
  ↳ events.session.created

session {
  id        uuid    pk
  tenant    uuid    fk → tenants
  audio_uri text    not null
  pii_safe  bool    default false
}

step 05Task plan

Task Planning & Private Workspace

We break the work into reviewed tasks with clear technical expectations and keep them in a private workspace, not scattered across a shared chat.

workspace · sprint 03live

done

scaffold apibe

auth · sessionbe

wip

transcribe workerai

PII relaysec

demo · iteratepm

prod readinessops

step 06Reviewed implementation

Controlled AI-Assisted Execution

AI agents work inside the documented plan. Evgeniy keeps responsibility for the architecture, reviews the output, and makes any decision that should not be left to the model.

agent · tracereviewed

plansplit transcribe into chunker + diarizer
editworker/transcribe.ts +84 −12
testvitest · 14 pass · 0 fail
reviewsenior approved (contract unchanged)

step 07Test + review gate

Testing & Quality Gates

Tests, type checks, contract checks, and review gates run before anything is considered done. Failing a gate blocks the merge.

gates · pre-mergegreen

typecheckpass
tests · unit + contractpass
lint · portable boundariespass
security · PII rulepass
senior reviewpass

step 08Demo + handoff pack

Demo, Iteration & Handoff

We show working software against the PRD, improve it based on real feedback, and hand over the code, documentation, and architecture in a state your team can take over.

handoff · packshipped

code

main · tagged v1.0

docs

PRD + architecture + ADRs

data

model + migration history

ops

runbook + alerts

Why this matters

Most software projects go off track before the first line of code.

Projects usually run into trouble when the scope is still fuzzy, the architecture exists only in people’s heads, the data model is unclear, or feature work starts before the product idea has been tested properly.

The ITNeuroNet approach is designed to make the work explicit. That means fewer hidden assumptions, better task quality, clearer AI-agent execution, and a stronger basis for estimating time, complexity, and risk.

Private delivery workspace

Work happens in a structured workspace, not a shared chat.

Each project runs in a private workspace that keeps the PRD, architecture, data model, API contracts, task plan, and review history in one place. You see the same working reference that guides the engineering work.

Workspace illustration: a document linked to a task through an agent trace

PRD, architecture, and data model in one place
Tasks linked to contracts and acceptance criteria
AI-agent activity limited to documented modules
Review and gate history kept with the work
Clear ownership for every decision and artifact

AI-assisted, not autonomous

Where AI helps, and where senior review remains responsible

AI helps the work move faster, but only inside a documented process. System design and technical decisions still need human responsibility.

AI agents help with

Drafting code within defined contracts and modules
Generating tests against documented behavior
Refactoring within reviewed architectural boundaries
Routine wiring, schema scaffolding, and documentation drafts

Senior review is responsible for

Product scope, architecture, and data model decisions
Security, compliance, and privacy-sensitive logic
Anything that changes a public contract or integration
Final approval before code reaches the main branch

Next step

Want to apply this approach to your product?

Start by talking through your situation. We will look at your product, your current system if you have one, and the smallest next step that makes practical sense.

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