Service
Custom AI agents for marketing teams
We design and build AI agents and automations for marketing teams with repetitive work worth removing. We define the inputs, permissions and human review needed for reporting, lead qualification, compliance checks and media operations before writing the system.
The work behind the service.
An agent should have a job, not an invitation to do everything. We start with the routine the team repeats and identify which parts are rules, which parts need interpretation and which actions carry risk. The design then specifies the output and the reviewer. This makes the system testable and gives the team a clear way to reject an incorrect result.
Data handling is part of that design. We agree on the sources, access permissions and external services before connecting anything. Sensitive lead information should not move into a tool simply because a connector exists. We document what the agent can read, what it can write and where records are retained.
A useful automation also needs a failure path. Missing inputs, changed platform formats and uncertain answers should produce an escalation rather than confident invented output. We build operating checks and a human fallback into the scope. Maintenance is not a surprise that appears after a successful demonstration.
What's included.
- AI agents and automations
- custom GPTs and copilots
- AI lead qualification
- live compliance checker
- AI ops installation and supervision
What an AI marketing agent is.
An AI agent is software that can carry out a series of steps toward a defined result, using tools and data you've given it access to. A chatbot mostly answers. An agent can also pull data, compare it against rules, prepare an output and pass it to a person.
Ordinary automation follows fixed rules, and it's often the better choice when a task never changes. An agent becomes useful when the work involves reading messy information or making small judgment calls within clear limits. We'll tell you when a simpler automation would do the job.
What marketing teams use agents for.
Reporting
Pulling numbers from each platform, flagging changes against the prior period and drafting a first summary for a person to edit.
Media operations
Checking budget pacing, spotting unusual changes and preparing search term, placement or creative reviews.
Lead qualification
Scoring enquiries against criteria you've agreed on, then sending the good ones to the right person or back to your ad platforms.
Compliance checks
Comparing ads and pages with your rules before review. A person still approves the final work.
Research and monitoring
Watching platform changes, industry news or market signals, and summarizing what matters to your team.
Copilots and custom GPTs
Giving your team a tool that knows your documents, processes and voice.
Limits and human review.
The risk rises with what an agent is allowed to do. An agent that reads data and writes a draft carries little risk. An agent that can change a budget or publish an ad carries much more.
So we set limits by action. Reading, flagging and drafting can often run freely. Anything that changes spending, targeting, claims or what the public sees goes to a person unless a narrow permission has been agreed. We build those limits into the system, because a written instruction alone isn't a control.
Agency or software.
You can often buy a product that does part of this work. It's usually the right choice when the workflow is common and the product fits. An AI automation agency makes more sense when the work crosses several systems, when your process is specific or when you need more control over data and permissions.
We compare those options first. We can also help you set up and supervise a product you've already chosen.
How it works.
- 01
Map the routine
We observe the current work and identify the repeatable steps. We agree on the output the team needs and the cost of an error. This tells us whether an agent, ordinary automation or a simpler process change is the right fit.
- 02
Set data and action boundaries
We confirm the data sources, permissions and allowed actions. External services and retention requirements are discussed before connection. We decide which outputs require approval and which narrow actions can run without it.
- 03
Build a testable first version
We create the workflow around representative inputs. The system records enough context to explain its output. We test missing data, conflicting evidence and common failure cases rather than demonstrating only the easiest example.
- 04
Review with the operators
The people doing the work evaluate outputs against the real routine. We correct ambiguous instructions and unhelpful alerts. The review asks whether time is saved without shifting an unreasonable checking burden onto the team.
- 05
Launch with ownership
We document the workflow, monitoring and fallback. Maintenance responsibilities and service dependencies are assigned. Expansion happens only when the first routine behaves predictably and the team can operate it safely.
How we use agents ourselves.
Our own media operations run on an internal set of agents that handle bounded routines and support review. They read account data, check pacing and flag problems, and they recommend changes for a person to decide. Changes to budgets, campaign status, dates and geographic targeting are blocked in code from going through their automated routes.
Running agents on real accounts taught us that the guardrails matter as much as the model. We test the judgment step against past mistakes, keep a record of what each agent found, and require evidence behind its claims. We don't publish a client performance result that a public project doesn't support.
How we price it.
We bill hourly and set the scope in hours. Strategy and advisory are CAD $175/h. Media management and execution are CAD $110/h. We separate those kinds of work in the estimate so you can see what is being bought, rather than hiding them inside a percentage of spend.
We agree on the deliverables, dependencies and review points before starting. If new work appears, we discuss the hours before expanding the scope. Engineering builds, hosting, software subscriptions, creative production and advertising budgets are identified separately where relevant. We confirm the applicable engineering rate in the scope rather than assigning a media rate to a technical build.
You can engage one discipline without buying the others. A tracking correction does not require ongoing media work, and a media engagement does not require a website rebuild. We recommend a sequence only when the evidence shows a dependency. The estimate makes that relationship visible, so your team can decide what to do now and what can wait.
We also agree on how progress will be reviewed and how hours will be reported. Your team should be able to connect the time used to the work delivered. When an access issue or an approval blocks delivery, we explain the dependency rather than letting it disappear inside an open-ended engagement.
Questions before we start.
What is an AI automation agency?
An AI automation agency helps a business find repetitive work and build AI systems to handle it. We focus on marketing teams, so we start with reporting, lead handling, compliance checks and media operations.
How much does a custom AI agent cost?
It depends on the number of systems involved, how much risk the task carries and who maintains it. There's usually an ongoing cost for AI usage and upkeep too. We start with one routine and scope it in hours.
How long does it take to build one?
A narrow routine with clean data can move fairly quickly, while connected systems and approvals take longer. We'll set out the timing after mapping the work.
Will AI agents replace our marketing team?
We don't build them for that. They take over repetitive work with a result that can be checked, while people keep strategy, sensitive claims and decisions about budget.
Can an AI agent make mistakes?
Yes. That's why we test failure cases, require outputs a person can check and build in a way to escalate. We watch performance after launch too, because platforms and inputs change.
Can you install agents inside our existing tools?
Often, yes, depending on what those tools allow. We check the connections, permissions and data rules before building.
What can a marketing AI agent actually do?
An agent can prepare reports, qualify leads against agreed criteria, check content against a review checklist or support media operations. Its value depends on the quality of its inputs and the clarity of its task. We do not treat generated text as a verified decision.
Build or buy?
We compare a product’s fit, exportability and operating constraints against the work you need done. A custom build is useful when the workflow or ownership requirements justify it. We do not build simply because a task can be described as AI.
Where does our data go?
That depends on the agreed architecture and providers. We document the data flow, permissions and retention before delivery. Sensitive information is only connected when the purpose and controls have been approved.
Who maintains it after launch?
Ownership is agreed in the scope. We can maintain and supervise the workflow, or document it for your team to operate. Provider costs and changes remain visible so the system does not become an unowned dependency.
Related services.
Notes from the work.
What AI actually changes inside a media team
AI does not replace media buyers. It removes the parts of the job that never deserved a human in the first place.
The case for owning your marketing tech
Rented funnels, locked-in page builders, and no-code sprawl all charge the same tax: you cannot change the thing that matters most.
Start a conversation.
Tell us what you are trying to move. We will tell you which disciplines are worth engaging, and which are not.