For Social Media Agencies

AI Automation for
Social Media Agencies.

Custom AI operating systems for social media managers and small agencies. Per-client memory, scheduled briefings, content pipelines, and client reports, each in the client's own voice.

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At a glance

AI automation for social media agencies, in plain English.

JQ AI SYSTEMS builds custom AI operating systems for social media managers and small agencies running multiple client accounts. Each system handles the repetitive layer: weekly briefings, content drafts in the client's voice, monthly reports, and ongoing research. The full 4-agent Camille AI OS is the reference build. Your agency owns the code, the prompts, and the results.

The Reality

What social media agencies actually struggle with.

The bottlenecks nobody puts in a pitch deck, but everyone recognises.

01
Generic AI tools ignore client voice
ChatGPT can write a post, but not in the voice of a specific client. It can summarise an article, but not remember who said what last month. Every 20 minutes saved on generation is lost in 30 minutes of editing. At six clients, the math stops working.
02
Monday briefings are a 4-hour black hole
Reading industry news for every client, tracking competitor moves, assembling a per-client summary. The work is not hard. The volume is what kills you. At six clients, the Monday briefing alone is half a day before you have written a single post.
03
Monthly reports never quite match the client
Every client wants a custom report structure, but you only have time to fill out one template. Leadership expects insight. You deliver metrics. The gap is where retention quietly leaks out.
04
Scaling past 6 clients feels impossible
Every extra client adds 6 to 10 hours of repetitive work per week. You either hire (expensive, adds coordination overhead) or cap the agency and watch your income plateau. Most solo and small agencies stall here.
What I Build

What an AI build looks like for you.

Not a product list. A shortlist of the builds that actually move the needle for this type of work.

Build 01

Multi-agent client OS

The full Camille OS pattern: 4 specialised Claude Code agents (briefing, content, reporting, research) sharing one per-client memory layer. Handles 70 to 80% of the repetitive work for 4 to 7 simultaneous clients. This is the flagship build for agencies ready to level up.

Build 02

Weekly briefing automation

A lighter-touch build for agencies that want to start with one workflow. Scheduled delivery every Monday morning with per-client industry news, competitor moves, and a structured summary. Turns a 4-hour ritual into a 30-minute review.

Build 03

Content pipeline with brand voice

A per-client content drafting system. Reads the client brief, reviews the content history, drafts platform-specific posts in the client's voice, and queues them for your review. Every client gets their own brief document so outputs stay on-voice without rewriting.

Build 04

Monthly client report generator

Pulls performance data, synthesises it against each client's goals, drafts a structured monthly report, and flags anything that needs your attention before it goes out. Different report shape per client without different templates to maintain.

Build 05

Shared memory vault

The layer that makes everything else work. An Obsidian-based vault where per-client briefs, content history, research findings and decision logs all live in one structured place every agent can read and write. This is why the output stays consistent between runs and between agents.

Real Builds

Systems built for social media agencies.

Live case studies. Click through for the full technical and commercial breakdown.

Case Study · Live

Camille AI Operating System

The flagship build. A 4-agent Claude Code system for Camille Guillain, a Paris-based social media manager running 4 to 7 client accounts. Handles briefings, content, reports and research across all clients. 70 to 80% of manual work automated. Monday briefing cut from 4 hours to 30 minutes.

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Case Study · Live

AI Brief Generator

A lightweight system for building structured per-client brief documents. The layer that sits underneath the content pipeline and makes per-client voice consistency possible. A good first build for agencies that want to systematise client onboarding before committing to a full OS.

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Case Study · Live

AI Content Calendar

A scheduled content calendar system that drafts posts per client, plans the release schedule, and tracks what shipped against the plan. Pairs with the content pipeline agent for agencies that want calendar-first planning.

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Why We Get You

This isn't a generic landing page.

The Camille AI Operating System is the single strongest case study on this entire site. It was built for a working Paris-based social media manager running real client accounts, it is in production right now, and it is what made Camille able to take on more clients without losing the personalised, human quality her existing clients pay her for. 70 to 80% of her repetitive work is now handled by the system. The judgement calls, the client relationships, and the strategy are still hers.

That build exists because João spent 12 years running a branding and communications studio before moving into AI systems. The single most important thing a social media agency needs from an AI system is brand voice consistency across 4, 6, 8 different client accounts. Generic AI tools cannot do this. It takes a communications background to even understand what the problem actually is, and it takes custom prompt engineering plus per-client memory to solve it. He has done both.

Every agency build starts from the same foundation as Camille\'s: per-client briefs as the memory layer, specialised agents for the specialised work, and a review queue so nothing publishes without human eyes on it. The AI handles the repetitive layer. The agency keeps the judgement. That split is the whole point of the build.

What It Costs

Indicative ranges.

Every project is scoped and fixed-priced before work starts. These are the bands most social media agencies builds fall into.

From €2,400 Single-workflow build

One agent or one pipeline: weekly briefing, content drafting, or report generator. Good starter build if you want to test AI in your agency before committing to a full OS. 1 to 2 weeks.

From €5,500 2 to 3 agent build

Briefing plus content, or briefing plus reports. Shared memory layer, per-client briefs, scheduled delivery. Handles a meaningful chunk of the agency's weekly workload. 3 to 5 weeks.

From €12,000 Full client OS (Camille pattern)

The complete 4-agent operating system with briefing, content, reporting, research, shared memory, and scheduled triggers. Built and tuned to your agency's workflow. 5 to 8 weeks.

Custom projects start with a free 30-minute consultation. A precise, fixed quote is provided before any commitment. All deliverables include full source code and documentation. You own the result outright.

Process

How it actually runs.

01
Discovery
Free 30-minute call. You describe the agency, the client load, the repetitive work, and the brand voice challenge. I map it against the Camille OS template and we identify which build size fits.
02
Scope + quote
A one-page scope document with exactly which agents are built, how the memory layer is structured, which clients I start with, and what the delivery looks like. Fixed quote before anything starts.
03
Build
The system is built in Claude Code using the same architectural pattern as the Camille OS, tuned to your agency. One or two of your real clients are used as the first test bed so the output is on-voice from day one.
04
Tune + onboard
Brand voice calibration per client. Review queue setup. Scheduled triggers configured. The agency walks through how to add new clients to the memory layer without needing to touch the code.
05
Deliver + handoff
Full handoff with source code, the prompt library, documentation, and a live walkthrough call. 14-day warranty on bug fixes. Optional maintenance retainer available afterwards, but most agencies run independently.
FAQ

Before you book.

Will the AI sound like my clients or will it sound like AI?
This is the first problem the build solves, and it is the reason the Camille OS was designed the way it was. Each client gets their own brief document in the shared memory layer: brand voice, content themes, preferred tone, rolling content log. Every agent reads the brief before it writes anything. The output is per-client consistent because the memory is per-client, not because one shared prompt is tuned to average across clients. Camille has been running this for months now and her clients have not noticed the difference between the AI drafts and her own writing.
Can I start small and grow the system later?
Yes, and this is usually the right move. Most agencies start with one agent (weekly briefing or content pipeline) to see how they work with AI inside their existing routine. The architecture is designed to add agents later without rebuilding what is already there. The shared memory layer and per-client briefs are built on day one, so adding a reporting agent or a research agent in a second phase is straightforward and cheaper than a from-scratch build.
Do I need to use Claude Code, or can you integrate with my existing stack?
The Camille OS runs on Claude Code because Claude Code is the cleanest tool for multi-agent systems with shared memory right now. For agencies already invested in Notion, Airtable, Slack, or other tools, I can integrate the AI layer into those workflows instead of asking you to move. The agents can still run in Claude Code underneath. The review queue, content output and reporting can surface wherever your team already works.
What happens if my client list changes? Does the system break?
No. Clients are data, not code. Each client lives as a brief document inside the shared memory vault. Adding a new client means adding a new brief. Removing a client means archiving the brief. The agents pick up the change automatically on the next run. You will not need to call back for code changes every time your client roster moves.
Does the system publish posts automatically, or do I approve everything?
Everything goes through a review queue by default. Nothing publishes without your eyes on it. Camille runs this way on purpose: the AI handles the drafting and the repetitive layer, but the judgement call about what actually goes live stays with her. That split is what makes the system worth the investment. You can configure auto-publishing for specific low-stakes workflows if you want to later, but the default is review-first.
I run 3 clients, not 7. Is this overkill for me?
A full 4-agent OS is probably overkill at 3 clients. The right first build for a 3-client agency is usually a single-workflow automation: a weekly briefing, or a content pipeline, or a reporting generator. Lower cost, faster build, and it still removes the highest-friction part of your week. You can upgrade to a multi-agent OS later if the client load grows. That conversation happens on the discovery call.
Who owns the code and the prompts after delivery?
You do. Every deliverable includes full source code, the prompt library per agent, the memory layer structure, and documentation. The agency owns the result outright. No licensing fee, no ongoing subscription to JQ AI SYSTEMS, no dependency on this consultancy continuing to exist. If you wanted to hand the system to a different developer tomorrow, you could.
Free Consultation

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for your social media agencies?

Book a free 30-minute call. We map your situation, scope the right build for your practice, and agree on a fixed quote before anything starts.

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