Case Studies

We built the proof before selling the service.

EZ ONE Hub is the flagship case study for the AI-augmented managed-team model — four products, 55+ modules, full-stack multi-tenant SaaS, built in months. This is what the model delivers. Imagine it on your project.

55+

Modules

Products

~6 mo

Concept to launch

Architect directing AI agents

Case Study — EZ ONE LLC

The EZ ONE Platform

Full-stack · Multi-tenant SaaS · 4 products · 55+ modules · AI-augmented build

ez1solutions.com ↗

EZ ONE needed a complete technology stack — public marketing sites, a multi-tenant SaaS platform for restaurants, retail, and professional services, a business marketplace with real-time service readiness rules, a subscription billing engine, a delivery network, and all the back-office tooling to operate them. The entire system was designed and built as Product Owner and Senior Architect, directing AI agents in specialized roles under the managed-team model that EZ Build Team now offers to clients. No external agency. No offshore team. A structured process, a review gate on every push, and months of focused execution.

The Scale

What 55+ modules actually means.

Not 55 components — 55 independently functional subsystems, each with its own data model, API layer, business logic, and UI surface.

Multi-brand public site system — 4 brand domains, one Next.js app

Multi-tenant role-based access control (RBAC)

EZ ONE Hub SaaS platform — 55+ feature modules

Business marketplace with real-time readiness rules

Unified CRM and customer engagement layer

Stripe Connect platform payments + EZ Pay direct processing

Subscription and service-commerce billing engine

Review aggregation and reputation management

SEO and local listings sync engine

Email and SMS marketing automation (SendGrid + Twilio)

GloriaFood online ordering integration + real-time order intake

Coupon and promotions engine

Operations and support ticket system

Agency and sales team assignment engine

PICKD delivery network and driver dispatch

Real-time analytics and reporting dashboards

Backend BFF (Backend-for-Frontend) API layer

BullMQ background job processing

Google + Meta OAuth integrations

Cloudflare Tunnel development infrastructure

Architecture

Full-stack. Production-grade. Every layer intentional.

The tech stack was chosen for long-term maintainability, not speed of prototyping. Every layer was designed before it was built.

Frontend

Next.js 15, TypeScript, Tailwind CSS, Zod

App Router, server components, BFF pattern. Multi-brand via hostname middleware rewrite.

Backend

NestJS, TypeScript, class-validator, Zod

Modular architecture. Guards, interceptors, structured error handling throughout.

Database

PostgreSQL, TypeORM, Redis

TypeORM entities with migration-first workflow. Redis for sessions, caching, BullMQ queues.

Auth

JWT, RBAC, multi-tenant sessions

Organization → Location hierarchy. Role isolation between staff, managers, owners, agency.

Billing

Stripe Connect + EZ Pay

Platform payments (Connect), direct processing (EZ Pay), subscription tiers, webhooks.

Messaging

Twilio, SendGrid, BullMQ

SMS and email via queue workers. Transactional + marketing. Template-driven, rate-limited.

Integrations

GloriaFood, Google, Meta

Real-time order intake from GloriaFood. OAuth login via Google + Meta. Maps + reviews API.

Deploy

Vercel, Docker, Cloudflare Tunnel

Vercel for frontend. Docker + Cloudflare Tunnel for local dev with external webhook access.

BFF pattern throughout

Every browser request hits a same-origin Next.js route handler, which proxies to the NestJS backend. No internal tokens, backend URLs, or credentials ever reach the client.

Multi-tenant at the data layer

Organization → Location → User hierarchy enforced at the database and API levels. Row-level scoping prevents cross-tenant data access regardless of what the UI does.

Queue-backed async work

BullMQ handles all non-request-critical work: order processing, email delivery, report generation, webhook retries. The request cycle stays fast and decoupled.

How It Was Built

One architect. Four AI agent roles. A reviewed process.

This is not 'ChatGPT wrote the code.' It is a structured methodology where AI agents operate in specialized roles and no output ships without human review. The architect's job is design, direction, and validation — not typing.

01 — Specification

Architecture before code

Every module starts with a written specification: data model, API contracts, entity relationships, UI requirements. The senior architect owns this step. No agent produces output until the spec is approved.

02 — Agent work

Specialized agents in parallel roles

Backend agent (NestJS entities, controllers, services), frontend agent (Next.js pages, components), database agent (TypeORM migrations, seed data), and QA agent (test coverage, type-check, lint) each work within their domain.

03 — Review gate

Human review before every push

All agent output is reviewed by the senior architect before it reaches the repository. Type-check, lint, and tests must pass. Architectural coherence across modules is validated at the gate.

04 — Phase integration

Cross-module integration testing

After each phase (e.g., auth system, billing engine, marketplace), the integrated system is tested end-to-end. The next phase does not start until the gate criteria are fully met.

“The AI agents are fast and capable within their role. The senior architect is responsible for coherence across the entire system — the data model, the API contracts, the security model, the deployment strategy. The combination is what produces 55+ modules in months instead of years. That is the model we sell.”
— Juan Nino, Founder & Lead Engineer

Hard Problems Solved

The technical challenges — and how we solved them.

Challenge

One Next.js app serving 4 brand domains

Solution

Hostname-based middleware rewrite maps each domain to an internal route prefix. Per-brand CSS token overrides handle theming. No separate deployments. No duplicated routing logic.

Challenge

Marketplace content safety at scale

Solution

Backend enforces readiness rules before any service is exposed to the public API. The BFF layer never renders inactive, hidden, or mock services — the rule lives in the backend, not in the UI.

Challenge

Multi-tenant RBAC across organizations and locations

Solution

Full guard system scoped to organization → location hierarchy. Staff, managers, owners, and agency accounts each get isolated data views enforced at the API layer, not the UI.

Challenge

Real-time order intake without a polling loop

Solution

GloriaFood webhooks trigger BullMQ jobs. Order intake, notifications, and status updates flow through a queue — decoupled, retryable, and observable without blocking the request cycle.

Challenge

Payment platform complexity (platform + direct)

Solution

Stripe Connect handles platform payments and connected account onboarding. EZ Pay handles direct processing where Connect is not appropriate. Both flow through a unified billing service layer.

Challenge

Shipping 55+ modules with consistent architecture

Solution

AI-augmented process with a structured specification phase before each module. Every module starts with a data model, API contract, and UI requirement review — before any agent produces output.

What This Means for You

This is what the model builds. Imagine it on your project.

EZ ONE Hub is not a demo. It is a live, multi-tenant SaaS platform with real customers, real payments, and real operational load. The same process, the same architecture discipline, and the same review-before-push standard applies to every client project.

The scale of what you need may be different. An MVP might be a single product with a handful of modules. A platform might grow over time. The model — managed, AI-augmented, reviewed, flat-priced — is the same regardless of scope.

Start a Project See the process →

MVP

Scoped architecture + core feature set, deployed and documented. Fixed price.

Platform

Phased milestones. Each phase scoped, priced, and delivered. Same model scales with you.

Ongoing

Senior engineer on retainer for features, fixes, and architecture decisions on a live product.

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