Interfaces · Claravine Agent
Describe the campaign. Get a compliant one back.
The Claravine AI Agent generates names, tracking codes and campaign variants from your own templates — proposing, never deciding. Every suggestion is reviewed before it applies.
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Why the default way runs out
The work is not hard. It is repetitive, and repetition is where a standard quietly stops being followed.
- You need forty variants of one campaign by Thursday, and every one of them has to be named correctly.
- You know the convention perfectly well, and typing it forty times is still where your errors come from.
- You get the corrections after the campaign is live, when fixing them means re-tagging what already ran.
What it does
Generate from a prompt
Describe the campaign and the agent produces the records against your template — not a plausible-looking name, a compliant one.
Build the variants
Smart Campaign Builder expands one campaign into the set you actually need, each variant named correctly rather than copied and edited.
Catch what is already wrong
Smart Correct identifies errors in existing data, offers the fix in one click, and standardizes inconsistent values without changing what they mean.
Work at the volume you have
Bulk generation applies the same template to a set rather than a row, which is the difference between a governed process and a well-intentioned one.
How it works
The agent works from your templates, controlled vocabularies and relationships — the same structures that govern a manual submission. It proposes; a person confirms. Where you would rather it ran on your own model, Bring Your Own AI is supported. Where you would rather it did not run at all, Smart Correct and the campaign builder both have paths that use no AI.
Three capabilities, each with a non-AI path.
What it looks like to use
The agent works inside Claravine, against the templates you already maintain. Nothing to install, nothing to configure.
What it decides, and what it does not
This ordering — propose, then confirm — is the section. For an audience accountable for data being correct, it matters more than any accuracy claim.
- It proposes. A person confirms before anything applies. There is no autonomous path to production.
- It generates against your templates and vocabularies, so a suggestion that would fail validation is not offered.
- Bring your own model if you would rather the reasoning happened somewhere you control.
- Every capability has a path that uses no AI, and those paths are not second-class.
The same taxonomy, whichever way you reach it
These are not three products. They are three ways into the same governed taxonomy — the one your templates define, your approvals control and your teams already submit against. A change approved in the interface is the change the API returns and the agent works from. Nothing forks, and nothing has to be kept in sync by hand.
Documentation
Capability detail, the model options including bring-your-own, and the non-AI paths are covered in the product documentation.
Frequently asked questions
Didn't find your answer? Bring your questions to a thirty-minute walkthrough with someone who has done this at enterprise scale — we'll show you, not tell you.
Does the agent decide our naming for us?
No. It generates against the template you defined, and a person confirms before anything applies. It removes the typing, not the judgement — and because it works from your controlled vocabularies, it cannot propose a value your standard does not allow.
Can we use our own model?
Yes. Bring Your Own AI is supported, so the reasoning can happen on a model you control rather than a default. Teams with an internal AI policy usually start here.
What if we do not want to use AI at all?
Smart Correct and the campaign builder both have paths that use no AI, and they are not second-class. You get the template-driven generation and the correction workflow without a model in the loop.
How is this different from asking ChatGPT for a campaign name?
A general assistant produces something that looks right. This produces something that passes validation, because it is generating from your template and your controlled vocabularies rather than from a description of them. The difference shows up at submission, which is too late to discover it.
How does this relate to the MCP server?
The agent is Claravine generating against your standard. MCP is a way for an outside assistant to reach that standard. If your team works inside Claude or ChatGPT all day, MCP is the door; if they want generation inside Claravine, this is.
See it against your own taxonomy
Thirty minutes with someone who has implemented this at enterprise scale.