We help select a useful workflow, set up the AI role and hand it over to your team. Or we run the work for you: you brief an FMM manager, and they manage the agent.
We start with a specific recurring task, not with a tool.Three engagement models
The right model depends on the workflow, available data and who will own the outcome after launch.
We map the process, define the AI role, create the working scenario, checks and human handoff.
We work alongside your specialists to select tools, build the first scenario and turn it into a repeatable practice.
You speak to an FMM manager. They clarify the task, assign it to the agent, review the work and return the result.
AI agent with a human manager
The manager keeps the context, resolves uncertainty and does not return work that has not been reviewed.
If the next step requires a business decision, the agent does not guess—the manager comes back with a question.
Message the manager in a familiar format and share the relevant context.
They clarify the goal, boundaries and acceptance criteria.
It works only within the agreed scenario and available context.
The manager checks the material and returns it with context when needed.
Where it can help
We confirm the actual scenario after reviewing the process and systems. These are useful starting points for that conversation.
Collect context and prepare drafts, message variants or a campaign structure for specialist review.
Consolidate campaign signals, flag deviations and prepare a question list for the manager.
Collect enquiry details, standardise them and hand the context to the responsible manager.
Find the relevant instruction, draft an answer within the source material and hand complex cases to a person.
Assemble agreed data, format a report and highlight changes that need attention.
Clarify an incoming task, add context and route it to the right owner.
How we start
We do not build a large automation before the value is clear on a specific task.
Define the recurring work, its owner and the expected output.
Check which data and systems are actually available for this scenario.
Document AI actions, review points and the human handoff.
Review output quality before deciding what should expand.
Control by design
Access and review levels depend on the scenario. Sensitive actions should not happen without an agreed human decision.
Define what the agent may do and where it must stop.
Keep critical decisions and external actions with an accountable specialist.
The team knows who receives the task when AI lacks enough context.
FAQ
A chatbot mainly holds a conversation. An agent works with permitted context and can perform defined actions in systems, such as creating a task, updating CRM, preparing a report or handing work to a person.
Only for predefined low-risk actions. Budget changes, external messages and other sensitive operations can remain subject to mandatory manager approval.
We first review available APIs, roles and data quality in CRM, advertising, email, messengers or the knowledge base. Only sources and actions needed for the selected workflow are connected.
Turnkey implementation fits a defined process, coaching suits a team that wants to build capability internally, and an agent with a human manager fits teams that need delivery without an in-house AI function.
You brief an FMM manager. They clarify context, assign the task to the agent, review execution and return the result—or ask for a decision when automation is not sufficient.
We use minimum required roles, define permitted sources and actions, keep logs and provide a way to disconnect integrations. The exact controls depend on the client’s systems.
Start with a repeatable process that has a clear input, an owner and a verifiable output, such as lead qualification, report preparation, advertising control or internal task routing.
Start with the numbers
In the first conversation, we will determine whether AI fits the task and which of the three models makes sense.
