Project index
Verified liveAI SaaS Platform2025-2026

AI Visibility Platform 

Model routing, research, queues, content generation, AI visibility, reporting, and publishing coordinated as a production platform.

Open live product Open full system flow
Architecture knowledge graph17 connected nodes
System coreAI Visibility PlatformAI SaaS Platform
01 / WorkspaceBrand intent enters through dedicated product surfaces
02 / ResearchSearch evidence grounds every automation
03 / OrchestrateLong-running work moves through durable queues
04 / ReasonA shared model layer routes by task and provider
05 / PublishGenerated assets cross a controlled integration boundary
06 / MeasureTraditional and AI search signals return to the workspace
Next.js
Node.js
LangChain
OpenAI
Anthropic
Gemini
Redis
MySQL
Playwright
Docker
Architecture stage Technology dependencyMove pointer to inspect depth

System brief

A queue-backed AI visibility and content platform

Engineering focus
Core platform and AI workflow engineering
Domain
AI SaaS Platform
Delivery window
2025-2026
System state
Verified live
4product surfaces in one repository
3+commercial model providers routed
24/7scheduled automation design

The engineering move

Complex behavior, made operational.

Constraint

The product problem

AI SEO is not a single prompt. It combines live search evidence, brand context, content strategy, generation, publishing, rank monitoring, citations, billing, and long-running jobs that must survive provider and browser failures.

System

The architecture decision

The platform uses separate web, telesales, desktop, and API surfaces over a shared Express core. Bull and Redis coordinate long-running work, sockets expose progress, Playwright and search services gather evidence, and a provider-neutral LLM layer routes work across OpenAI, Anthropic, Gemini, Azure, and compatible models.

Outcome

The operating result

A single brand workspace can move from discovery to production: analyze visibility, build a keyword universe, generate content and imagery, publish to connected sites, track traditional and AI search presence, and receive scheduled reports.

Delivered capability

What the system actually does.

01

AI visibility, citation verification, and overview tracking

02

Keyword-universe, competitor, trend, and rank research

03

Single and bulk content generation with image workflows

04

Provider-neutral LLM routing with tools and cost estimation

05

Bull and Redis job orchestration with live socket progress

06

Brand settings, credits, subscriptions, teams, and permissions

07

WordPress and ecommerce publishing integrations

08

Weekly and monthly report generation and delivery

Full system flow

The Mermaid diagram, rendered.

The interactive graph above is the executive view. This is the detailed service, data, control, and delivery path reviewed from source.
Mermaid / system architecture120%

Rendering the reviewed system flow

Drag the horizontal viewport at higher zoom levelsView .mmd source

Production architecture

A concrete path through the system.

Each stage below comes from the reviewed source tree, routes, services, infrastructure, and deployment evidence.
  1. 01
    Workspace

    Brand intent enters through dedicated product surfaces

    Next.js web, telesales, admin, and desktop clients share identity, brand configuration, credits, and workflow state.

    Next.jsNextAuthSocket.IO
  2. 02
    Research

    Search evidence grounds every automation

    SERP, DataForSEO, browser crawling, site audits, trend data, and competitor discovery assemble the working context.

    PlaywrightCheerioDataForSEO
  3. 03
    Orchestrate

    Long-running work moves through durable queues

    Bull, Redis, cron controls, and worker processes isolate retries, scheduling, cancellation, progress, and operational visibility.

    BullRedisWorkers
  4. 04
    Reason

    A shared model layer routes by task and provider

    OpenAI, Anthropic, Gemini, Azure, and compatible endpoints expose streaming, structured output, tools, imagery, and cost estimation.

    LangChainOpenAIAnthropicGemini
  5. 05
    Publish

    Generated assets cross a controlled integration boundary

    Content, SEO metadata, products, categories, and media are delivered through connected WordPress and commerce adapters.

    WordPress RESTS3Google Cloud
  6. 06
    Measure

    Traditional and AI search signals return to the workspace

    Rank trackers, citation checks, visibility history, automation logs, and scheduled PDF reports create the operating feedback loop.

    MySQLTrackersReporting

Engineering judgment

The decisions behind the delivery.

One platform, many execution modes

Interactive requests, bulk runs, schedules, background workers, and socket sessions use the same domain services.

Provider choice is a runtime concern

Model selection and tool binding sit behind a shared interface so workflows can evolve without hard-coding one vendor.

Operations are visible

Queue controls, automation logs, costs, credits, reports, and retry paths are part of the product surface.

Technical constellation

01Next.js02Node.js03LangChain04OpenAI05Anthropic06Gemini07Redis08MySQL09Playwright10Docker
AI Visibility Platform system architecture
Deployment evidence

This is running software.

Live product and source reviewed on 2026-07-16. The public link is provided as evidence, while protected product areas correctly remain behind authentication.

Open AI Visibility Platform

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