Adsroid White Label Is Live: Build Your Own AI Advertising Agent on Top of Adsroid

Adsroid - Build Your Own AI Advertising Agent on Top of Adsroid
Build your own AI advertising platform on top of Adsroid. White Label gives agencies, SaaS companies and AI teams the infrastructure to power their own agents, manage multiple projects and control API usage.

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AI is changing the way advertising is managed.

But while building an AI agent that can understand a marketing brief or recommend a campaign can be relatively straightforward, giving that agent reliable access to real advertising platforms is a completely different challenge.

Google Ads, Meta Ads, account authentication, permissions, campaign management, reporting, multiple customers, usage limits, API costs, security and infrastructure all have to work together.

That is the problem Adsroid has been solving from the beginning.

And today, we are opening that infrastructure to other companies.

Introducing Adsroid White Label.

With Adsroid White Label, agencies, SaaS companies, AI startups and other organizations can use Adsroid as the underlying advertising infrastructure for their own AI-powered products.

Instead of building an entire advertising infrastructure from scratch, you can build your own agent, application or service on top of Adsroid.

Your product stays yours.

Your users stay yours.

Your brand stays yours.

Adsroid handles the advertising layer underneath.

From an AI agent to an AI advertising infrastructure

Adsroid was originally built as an AI platform for managing advertising accounts.

Over time, the architecture evolved.

We built a unified API, project-based account management, MCP access, AI agents, advertising tools and a system capable of connecting AI applications with real advertising platforms.

That created an interesting possibility.

If Adsroid can provide this infrastructure for our own AI agent, why should every other company building an AI advertising product have to build the same infrastructure again?

They shouldn’t.

The idea behind White Label is therefore simple:

Adsroid provides the advertising infrastructure. You build the product on top of it.

An agency can build an AI assistant for its clients.

A SaaS company can add AI-powered campaign management to its platform.

An AI startup can create a specialized advertising agent.

A marketing technology company can build its own interface and workflows around advertising APIs.

All of them can use the same underlying Adsroid infrastructure.

A multi-tenant architecture built for organizations

White Label introduces an organization-level architecture designed specifically for this use case.

Instead of having a single Adsroid account containing everything, an organization can manage multiple independent projects.

For example, an agency could have an organization structured like this:

AGENCY
Your organization
CLIENT A
Client workspace
project_a
CLIENT B
Client workspace
project_b
CLIENT C
Client workspace
project_c
CLIENT D
Client workspace
project_d

Each project can be connected to its own advertising accounts and used independently.

The parent organization, however, remains in control of the infrastructure.

This creates a true multi-tenant setup without requiring the organization to maintain completely separate API infrastructure for every customer.

One API key for the entire organization

One of the most important parts of the architecture is how API authentication works.

The organization can use a single Adsroid API key.

The API request specifies the project_id corresponding to the customer or project that should be used.

Adsroid then uses that project identifier to route the request to the correct environment and advertising accounts.

Conceptually:

YOUR APPLICATION
App · SaaS · Workflow
API KEY
ADSROID API
One API · Multiple platforms
project_1
Client A
Google Ads
project_2
Client B
Google Ads
project_3
Client C
Meta Ads

The application doesn’t need to maintain a different authentication mechanism for every customer.

The API remains the same.

The credentials remain centralized.

The project_id determines which project the operation belongs to.

This is particularly useful when building an AI agent that needs to operate across hundreds of customers.

The parent organization controls usage

Multi-tenancy creates another important requirement: usage control.

If an organization provides AI advertising capabilities to its customers, it needs to be able to control how much infrastructure each customer can consume.

Adsroid White Label therefore includes credit management at the project level.

The parent organization can allocate credits to individual sub-accounts and define limits according to its own business model.

For example:

AGENCY ORGANIZATION
Centralized credit management
Client A
1,000 credits
Client B
500 credits
Client C
2,000 credits
Client D
250 credits

This allows the organization to keep control over API consumption while giving each customer an isolated usage allowance.

The organization can therefore build its own commercial model around the infrastructure.

For example, an agency could include a certain amount of AI advertising usage within each client package.

A SaaS company could include a number of AI actions within each subscription.

An enterprise platform could allocate credits internally between different business units.

The underlying infrastructure remains centralized in Adsroid.

Build the agent. Don’t rebuild the infrastructure.

This is where White Label becomes particularly interesting.

Suppose you want to build an AI advertising agent.

The obvious first step is to connect an LLM.

But the LLM is only one part of the system.

You also need to solve:

  • Authentication with advertising platforms
  • Advertising account connections
  • Campaign and ad management
  • Reporting and data retrieval
  • Tool execution
  • API error handling
  • Account isolation
  • Multi-tenancy
  • Usage tracking
  • Credit management
  • Permissions
  • API limits
  • Infrastructure
  • Monitoring

And then you have to maintain all of it.

Every time an advertising platform changes its API, your infrastructure has to adapt.

This is exactly the type of complexity Adsroid is designed to abstract.

Your team can therefore concentrate on the part that actually differentiates your product:

the agent itself.

You can decide how your AI behaves, what workflows it supports, what your interface looks like and how your customers interact with it.

Adsroid handles the advertising infrastructure underneath.

Your own AI agent, powered by Adsroid

White Label does not require you to use the Adsroid interface as the primary interface for your users.

You can build your own.

For example, an agency could create an application called:

Agency AI

A customer could log in and ask:

“Reduce wasted spend in my Google Ads account.”

Your agent interprets the request, decides which actions are required and calls the Adsroid API.

The flow could look like this:

CUSTOMER
Your user
YOUR AI AGENT
Understands the request & decides what to do
ADSROID API
Executes the requested action
project_id = client_123
CLIENT’S ADVERTISING ACCOUNT
Connected through Adsroid
Google Ads
Meta Ads

The customer interacts entirely with your product.

Adsroid becomes the infrastructure layer behind the scenes.

This means the value proposition of your product can be completely different from Adsroid itself.

You might specialize in e-commerce.

Or lead generation.

Or real estate.

Or enterprise marketing.

Or a specific agency methodology.

The underlying advertising capabilities don’t need to be rebuilt every time.

A natural extension of Adsroid MCP

White Label also builds on the same philosophy behind Adsroid MCP.

With MCP, AI applications such as Claude can interact with advertising accounts through Adsroid without requiring every AI application to implement each advertising platform independently.

The MCP architecture provides a standardized interface between AI and advertising infrastructure.

White Label extends this concept one step further.

Instead of simply allowing an existing AI application to use Adsroid, an organization can build its own AI application on top of Adsroid.

The distinction is important.

MCP makes advertising capabilities available to AI applications.

White Label allows organizations to build and operate their own AI applications using those capabilities.

The result is a layered architecture:

YOUR AI APPLICATION
Agent  ·  SaaS  ·  Interface  ·  UX
ADSROID
API  ·  Projects  ·  Credits  ·  Routing
ADVERTISING PLATFORMS
Google Ads  ·  Meta Ads  ·  More

Each layer has a different responsibility.

Your application provides the intelligence and customer experience.

Adsroid provides the advertising infrastructure.

The advertising platforms provide the underlying advertising capabilities.

Designed for agencies

Agencies are one of the most obvious use cases for this architecture.

An agency managing dozens or hundreds of advertising accounts can build an internal or customer-facing AI layer without having to create an entirely new advertising infrastructure.

For example, an agency could provide each customer with a dedicated AI assistant.

Each customer receives their own Adsroid project.

The agency remains the parent organization.

The agency controls credits and usage.

The agency controls the AI experience.

The customer simply interacts with the agency’s product.

This also means the agency can gradually evolve its AI capabilities without having to redesign the underlying advertising architecture every time.

A recommendation agent today could become a campaign creation agent tomorrow.

A reporting assistant could become an optimization agent.

The infrastructure remains the same.

Designed for SaaS companies

The same architecture applies to SaaS products.

Imagine an existing marketing platform that already provides CRM, analytics or lead generation tools.

The company wants to add a new capability:

“Manage your advertising campaigns with AI.”

Building the advertising integrations internally would require significant engineering resources.

With Adsroid White Label, advertising becomes an infrastructure component that can be integrated into the existing product.

The SaaS company controls:

  • The user experience
  • The AI model
  • The prompts
  • The workflows
  • The pricing
  • The customer relationship
  • The branding

Adsroid provides the advertising layer.

This makes it possible to add advertising capabilities without becoming an advertising API provider yourself.

Designed for AI startups

There is another category that we believe will become increasingly important: companies whose primary product is an AI agent.

These companies don’t necessarily want to build traditional advertising software.

They want their agent to be able to take action.

For example:

“Analyze my campaigns and move budget from underperforming campaigns to campaigns with more potential.”

For the AI agent, this is simply a task.

Behind the scenes, however, executing that task requires access to advertising data, campaign settings, budgets, permissions and APIs.

Adsroid provides that execution layer.

The agent can therefore focus on reasoning and orchestration rather than implementing every advertising integration itself.

A new role for Adsroid

White Label represents an important evolution in what we are building at Adsroid.

Adsroid is no longer only an application that marketers use directly.

It can also act as an infrastructure layer for other applications.

The distinction is subtle but important.

Instead of asking:

“How can Adsroid become the AI advertising product for everyone?”

we can also ask:

“How can Adsroid provide the infrastructure that allows anyone to build an AI advertising product?”

That second question opens a much larger ecosystem.

Different organizations can build different experiences.

Different agents can use different models.

Different businesses can create different pricing and workflows.

But they can all rely on the same underlying advertising infrastructure.

The opportunity behind White Label

We believe the future of AI-powered advertising will not be defined by a single interface.

There will be specialized agents for different industries.

There will be agencies building their own AI assistants.

There will be SaaS platforms embedding advertising agents into their products.

There will be internal enterprise agents operating across large advertising portfolios.

There will be entirely new products that don’t exist today.

Building all of these products independently would require repeatedly solving the same infrastructure problems.

Adsroid can provide that common layer.

The goal is not to replace the products that organizations want to build.

The goal is to make those products easier to build.

Build on top of Adsroid

With Adsroid White Label, an organization can now create a multi-tenant advertising environment where:

One organization

manages

multiple projects

using

one API infrastructure

with

individual credit limits

while allowing

its own AI agents and applications

to interact with

real advertising accounts.

That means you don’t need to build the entire advertising infrastructure before you can start building your AI product.

You can focus on what makes your product unique.

The agent.

The experience.

The workflow.

The industry expertise.

The business model.

Adsroid takes care of the advertising infrastructure underneath.

If you’re an agency, SaaS company, AI startup or technology company interested in building your own AI-powered advertising product, Adsroid White Label is now available.

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About the author

Picture of Danny Da Rocha - Founder of Adsroid
Danny Da Rocha - Founder of Adsroid
Danny Da Rocha is a digital marketing and automation expert with over 10 years of experience at the intersection of performance advertising, AI, and large-scale automation. He has designed and deployed advanced systems combining Google Ads, data pipelines, and AI-driven decision-making for startups, agencies, and large advertisers. His work has been recognized through multiple industry distinctions for innovation in marketing automation and AI-powered advertising systems. Danny focuses on building practical AI tools that augment human decision-making rather than replacing it.

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