Enterprise AI Delivery

AI moves fast.
A human signs off every gate.

Solving your business problems with maximum velocity — without sacrificing security or human accountability.

Across the entire SDLC, AI drafts, generates and scans — then a named engineer reviews and approves before anything moves. Every change is labelled [AI-GEN], rule-compliant by construction, and 100% your IP.

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AI across the SDLC

From requirements through release to reporting, AI accelerates every stage of delivery — never operating outside a governed process.

Governed by humans

Nothing ships unreviewed. Every AI-touched artifact passes a named human gate before it moves downstream.

Measured as evidence

AI impact is tracked as a delta against a pre-AI baseline — team-level, honest, and never an individual KPI.

WHAT THIS MEANS FOR YOUR TEAM

The engineering rigor didn't disappear — it moved upstream

AI removed the bottleneck at the keyboard. It did not remove the engineering. The judgement that used to happen in code review now has to happen in the specification and the architecture — before a single line is generated. That is where our senior people spend their time.

Traditional delivery

Specification: 15% Architecture: 10% Code generation: 50% Verification: 25%
50% Rigor & Quality
Zaiten Model

AI-assisted, governed delivery

Specification: 30% Architecture: 25% Code generation: 15% Verification: 30%
85% Rigor & Quality
  • Specification
  • Architecture
  • Code generation
  • Verification

Illustrative distribution. What changes is where senior effort is spent — not how much of it an enterprise system needs. The share of code generation falls because the same engineers move to specification, architecture and verification.

What never moves to AI

The work that protects your product over the long term.

Architecture ownership

A named architect owns every design decision.

Human review gates

Nothing AI-touched ships unreviewed.

QA depth & coverage

Coverage held at ≥ 80%; edge cases owned by QC.

Security & IP controls

Shift-left checks; 100% client IP ownership.

Proof in Practice

Deep-dive showcases, measured against a baseline

How to read these numbers. The first card reports outcomes against a measured pre-AI baseline. The rest report how quickly a deliverable reaches you — not the price of an engagement. Time returned by automation is reinvested into specification, architecture review and testing, which is where the long-term cost of a system is actually decided.

Full Transparency: Delivery Governance & Impact Dashboard

AX Smart Hiring Smart Retail

Context & challenge

Many concurrent projects needing honest delivery visibility — without manual report wrangling or inflated AI claims.

AI-assisted practice

AI summarizes Jira status, blockers and effort variance; one workbook auto-rolls up KPIs including AI impact per sprint.

Human gate + reusable asset

The PL validates before sharing. AI is measured as a delta vs baseline, team-level — never an individual KPI.

−67%
Defect escape (Retail)
+30%
Velocity vs baseline
−35%
Cycle time vs baseline
Lesson learned — without a baseline, every "% improvement" is noise. Capture 2–4 pre-AI sprints first, then let the delta tell the story.

Getting the Requirement Right: Requirements & Solution Design

RSM · Auth & session

Context & challenge

Mission-critical emergency platform — vague specs, a non-technical client and hard NFRs. Traditionally 3 review cycles to approval.

AI-assisted practice

AI drafts stories, AC and edge cases, prototypes the UI, and proposes 2–3 architecture options with C4 and DB schema.

Human gate + reusable asset

BA validates; AC stays DRAFT until approved. The TA sets criteria before AI compares options.

Where the time goes

Reinvested into deeper NFR coverage and architectural review.

3 → 1
Review iterations to approval
−90%
Time to a reviewable prototype
−50–60%
Time to an approved design
Lesson learned — AI output is only as good as the requirement it reads; criteria-first keeps the architect, not the AI, owning the trade-off.

Faster Time-to-Market: High-Speed Coding & Legacy Upgrade

RSM · build DiM · JP

Context & challenge

Routine auth features plus a Spring Boot & Java major-version upgrade, and regression-heavy maintenance across a Japanese language barrier.

AI-assisted practice

Cursor/MCP generate code from design + AC, build API clients, refactor legacy and automate regression — despite limited team expertise.

Human gate + reusable asset

Developers review, test and own all AI code; peer review mandatory, PRs labelled [AI-GEN].

Where the time goes

Reinvested into hardening, refactoring and regression depth.

−20–40%
Time to a working increment
−40%
Time to a full regression pass (JP)
+30%
Team velocity (JP)
Lesson learned — AI pays off most on routine and migration work; novel tasks gain less. Language barriers shrink when AI carries the boilerplate.

Protecting Your Users: Quality Control & Edge-Case Engineering

DiM · test RSM · IoT

Context & challenge

Slow manual test design that misses edge cases, on a lean IoT team with thin domain knowledge needing fast, safe coverage.

AI-assisted practice

Copilot/Cursor generate test cases, data and edge cases from AC; a gap-analysis pass maps requirement → test and flags missing NFR and UX.

Human gate + reusable asset

QC reviews and adjusts every AI case; coverage gate enforced at ≥ 80%.

Where the time goes

Reinvested into edge cases the happy path never reaches.

−50%
Time to a complete test suite
≥80%
Coverage gate enforced
−40–50%
Time to a signed-off spec (IoT)
Lesson learned — AI surfaces edge cases people skip, but QC still owns what "enough coverage" means for this product.
Under the hood

The engineering detail, if you want it

You can safely assume we have this covered. Open any section if you would like to see exactly how.

Our AI-Assisted Delivery Framework

AI accelerates all nine SDLC stages — experts own the outcome

1 · Requirements

Reverse-engineer ambiguity; draft testable, edge-case acceptance criteria.

2 · Analysis

Consistency-check stories against system rules; surface gaps and conflicts.

3 · Architecture

Propose 2–3 options from NFRs; draft C4 diagrams and risk notes.

4 · Estimation

AI as challenger: red-team estimates against history; flag optimistic scope.

5 · Development

Boilerplate, integrations and refactors from the design — engineers own the logic.

6 · Testing

Generate cases, data and edge cases from AC; analyze coverage gaps.

7 · Security

Shift-left scanning on every commit; the lead approves the gate.

8 · Release

Draft release notes, runbooks and hypercare checklists.

9 · Reporting

Summarize Jira status, blockers and the AI-impact delta against baseline.

AI drafts · analyzes · generates · scans · summarizes → a named human reviews, decides and signs off at every stage.

Human-in-the-Loop Governance

AI assists, humans own — and it's enforced

Every phase has a named owner and a hard gate. The IDE and the model are swappable; the governance is not.

Each phase splits into what AI drafts (cool) and the named human who owns the gate (red). The recurring red seal down the right column is the whole point — "enforced at every phase" lands without a word.

AI assists · draftsHuman owns · gate
Planstories, AC, estimates
ClaudeChatGPT
✓
BA & PM DRAFT until validated
Designoptions, ADRs, prototypes
ClaudeFigmaLovable
✓
Tech architectsets criteria, owns design
Buildboilerplate, features, debug
CursorCopilot
✓
Developerspeer review required
Review & QAPR checks, tests, SAST
Copilot SonarQube
✓
QCcoverage ≥ 80%
Release & runnotes, deploy, triage
Claude Copilot
✓
PMsigns off the release
Cross-cuttingquality, lessons, assets
SonarQube GHAS
✓
Security & qualitygate across lifecycle
Enforced in tooling Jira DRAFT states[AI-GEN] PR labels SonarQube coverage gatesAudit trail

How We Actually Run It · MCP-Governed Workflow

One governed AI run — rules, tickets and source

Cursor pulls context from four governed sources over MCP, runs the task on an LLM, then submits back for review.

01

Hub & spoke — context pulled from governed sources

One agent reaches into four governed sources, executes, then hands off to a human gate. This is the most literal reading of the title — it makes "rules, tickets and source" visibly the inputs, not steps.

AI Reference · code Project rules Jira · tickets Git · source
MCP›
CursorMCP agent
→
Claude Sonnetgenerate / review
→
Human gate ✓PR review · sign-off
→ main
02

Swimlanes — machine lane vs. human lane

Three lanes: governed sources feed the agent loop; the human owns the exit. Best for a technical buyer who wants to see exactly who does what — and that the human sign-off is a separate, non-negotiable lane.

Sources · MCP
AI Reference Rules Jira Git
Cursor agent
Acquire rulesUnderstand feature Retrieve sourceGenerate / review Build · auto-fixSubmit PR
Human
PR review · sign-off ✓ → main
Every gate is enforced in tooling — Jira DRAFT states · [AI-GEN] PR labels · SonarQube coverage gates · a full audit trail.

Enterprise AI Assets

Reusable assets — and the workflow that runs them

The AI practice stack

Company delivery process (DX Framework)
Project rules
Cursor – Claude Skills / AI skills
Code graph & context packs
Approved AI tools
Human review gates
Jira evidence & dashboard

Reusable skills

Requirement Review
Acceptance Criteria
Test Case Generation
Code Review
Unit Test Generation
Defect Analysis
Architecture Review
Release Notes

The developer workflow runs on Cursor + MCP: it pulls rules, tickets and source over MCP, then submits back through a human gate — so reusable skills produce consistent, rule-compliant output every run.

Want this governed AI delivery on your roadmap?

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