BuildOrbit
All Projects

LinkedIn Growth OS

linkedin-growth-os — zsh
$ cat system-status.json
{
"app_modules": "5",
"deployment_targets": "3",
"workflow_steps": "8",
"safety_controls": "4"
}
$ echo $STACK
Next.jsReactTypeScriptMongoDBBullMQRedis
●Private system delivered

LinkedIn-first growth platform converting activity into prioritized pipeline with AI-assisted engagement.

A sales intelligence platform that helps B2B teams run signal-driven LinkedIn outreach. It combines persona-driven targeting, intent scoring from LinkedIn activity, AI-assisted engagement drafting, and approval-first safety controls into one unified workflow spanning a web dashboard, Chrome extension, and async workers.

Sales IntelligenceGrowth Platform + ExtensionPrivate deploymentWebChrome Extension
Discuss a Similar Project
Delivery footprint

The scale of what shipped.

Quick numbers to show the size, complexity, or scope of the system behind this case study.

5
App Modules
3
Deployment Targets
8
Workflow Steps
4
Safety Controls
Case Study

How the project moved from constraint to launch.

A concise read of the challenge, the system we designed, and what changed once the product reached production.

01

The Challenge

Most LinkedIn outbound workflows are fragmented: targeting lives in spreadsheets, signals are noticed too late, draft quality is inconsistent, and teams either over-automate (risk) or under-scale (manual effort).

02

Our Approach

We built an end-to-end LinkedIn operating system with persona wizards, AI-powered shortlist generation, real-time signal capture and intent scoring, engagement draft generation with style constraints, and safety controls including approval gates, suppression lists, and cooldown windows.

03

The Result

Delivered a full monorepo platform with a Next.js web dashboard, Chrome extension with inline LinkedIn actions, BullMQ async workers, and MongoDB workspace-scoped data all connected through shared type contracts and explainable AI scoring pipelines.

Key Features

What actually shipped.

Selected capabilities and system details that mattered in the final product, not a padded feature checklist.

Persona wizard with AI-powered targeting suggestions

Intent scoring with weighted signals and recency decay

Engagement copilot with context-aware draft generation

Chrome extension with inline LinkedIn page actions

Approval-first execution with suppression and cooldowns

Workspace-scoped multi-tenant architecture

BullMQ async processing for signal and scoring pipelines

Explainable scoring with driver breakdown and evidence gating

Tech Stack

Built with reliable production tooling.

The stack was chosen to fit the product constraints, not to chase trends. These are the technologies behind the shipped system.

Next.jsReactTypeScriptMongoDBBullMQRedisClerkOpenAI Agents SDKChrome Extension API
More Work

Explore adjacent case studies.

If this build is close to what you have in mind, these projects show the range around it.

Building something with this level of complexity?

If you're planning a growth platform + extension with real delivery constraints, we can help you scope it, make the technical calls early, and ship it properly.

Start a Conversation
Direct technical conversations before scope gets expensive.
Full-stack product delivery from interface to backend to launch.
Clear tradeoffs and execution plans instead of vague agency promises.