ITNeuroNet
Approach

Architecture before acceleration

AI-assisted development works best when the product goal, system boundaries, data model, API contracts, and task plan are clear first.

ITNeuroNet uses a documentation-first, architecture-first delivery process. The goal is to reduce ambiguity before implementation and then use AI-assisted workflows inside a controlled technical structure.

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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.

Delivery rail · blueprint
Eight-station delivery rail blueprint, from Hypothesis to Handoff
asset · v108 stations
step 01Hypothesis brief

Discovery & Hypothesis Framing

We make the product bet explicit: who it serves, what changes for them, and which assumptions are most likely to break the project.

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 translate the bet into a structured PRD — scope, user flows, acceptance criteria, and what is intentionally out of scope.

PRD · v1reviewed
  1. §1. Scope · session capture, transcript, CRM push
  2. §2. Roles · SDR, manager, admin
  3. §3. Out-of-scope · custom voice models, BI dashboards
  4. §4. Acceptance · transcript p95 < 60s; PII never leaves relay
  5. §5. Risks · audio quality, vendor lock-in, PII drift
step 03Architecture spec

Architecture & System Design

We design the system boundaries, services, integration points, and the rationale behind each technical choice before any feature is built.

architecture · systemv1
clientweb · mobileapi/v1/sessionsworkertranscribestorepg · audio_urirelayPII boundaryHIPAA boundary
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

Work is broken down into reviewed, contract-aware tasks and tracked inside a private delivery workspace — not in a shared chat.

workspace · sprint 03live
done
scaffold apibe
auth · sessionbe
wip
transcribe workerai
PII relaysec
next
demo · iteratepm
prod readinessops
step 06Reviewed implementation

Controlled AI-Assisted Execution

AI agents execute inside the documented structure. Senior engineers own architecture, review, and any decision the model is not allowed to make alone.

agent · tracereviewed
  1. plansplit transcribe into chunker + diarizer
  2. editworker/transcribe.ts +84 −12
  3. testvitest · 14 pass · 0 fail
  4. 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 demo working software against the PRD, iterate on real feedback, and hand off code, docs, and architecture in a state your team can own.

handoff · packshipped
code
main · tagged v1.0
docs
PRD + architecture + ADRs
data
model + migration history
ops
runbook + alerts
Why this matters

Most software projects fail before the first line of code.

They fail because the scope is vague, the architecture is implicit, the data model is unclear, or the team starts building features before understanding the product bet.

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.

Every engagement runs inside a private workspace that holds the PRD, architecture, data model, API contracts, task plan, and review history. Clients see the same source of truth their engineering partner is executing against.

Workspace spot illustration: 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 scoped to documented modules
  • Review and gate history kept with the work
  • Clear ownership for every decision and artifact
AI-assisted, not AI-autonomous

Where AI helps, and where senior review stays responsible

AI is a delivery accelerator inside a documented system. Architecture and judgment remain a human responsibility.

AI agents help with
  • Drafting code inside defined contracts and modules
  • Generating tests against documented behavior
  • Refactoring within reviewed architectural boundaries
  • Routine wiring, schema scaffolding, and documentation drafts
Senior review owns
  • 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 this approach applied to your product?

Start with an assessment. We will review your product context, current architecture, and the smallest credible next step.

Request MVP Sprint AssessmentBook a Discovery Call