UTM Copilot
An AI-powered UTM management platform that replaces brittle spreadsheets with conversational link creation, a multi-agent validation pipeline, and real governance for marketing teams that care about clean analytics.
<30s
To a validated UTM
5
Specialized AI agents
100%
Convention-compliant
0
Broken spreadsheets
Stop guessing. Start tracking with confidence.
UTM Copilot reframes a boring, error-prone workflow as a single conversation with an AI that actually understands your governance rules.
Six features cover the full lifecycle: conversational creation, governance & conventions, analytics, CSV import, the multi-agent pipeline, and team collaboration.
Four steps take you from a plain-English campaign brief to a validated, tagged UTM with a searchable library entry.
Inside the product
Real screenshots from the logged-in app — this isn't marketing art, it's the product working.
Dashboard — total & active UTMs, draft queue, live compliance rate, recent UTMs, and per-day LLM cost tracking for transparency on AI spend.
Conversational entrypoint — create UTMs, manage the library, set conventions, or query compliance in natural language with a CSV attach for bulk import.
UTM Library — one searchable table for every tracking code with status, source, medium, per-row edit/duplicate/open/delete actions, and CSV export.
The Problem
Every marketing team has the same buried mess: a shared UTM spreadsheet nobody trusts, three slightly different naming conventions, inconsistent case on utm_source, and analytics dashboards that quietly split the same campaign into four rows. The people who enforce the conventions don't scale, so governance dies the first time someone's in a hurry.
The Solution
UTM Copilot puts governance at the point of creation. You describe the campaign in plain language, a multi-agent AI pipeline drafts the UTM, validates it against your organization's rules, fixes anything non-compliant, and explains exactly what it did before you approve. Every link lives in a searchable library with version history, tags, and QR codes — and the whole thing is auditable.
How it was built
Map → Model → Make → Monitor
Map
Allowed values, regex patterns, required fields, field mappings — encoded once so every agent agrees on what 'compliant' means.
Model
Five specialized agents — Planner, Validator, Fixer, Data Agent, Explainer — each with a small job and explicit handoffs for reliable, debuggable results.
Make
A single chat surface handles create, edit, bulk import, and audit queries. CSV upload triggers column mapping and per-row validation with full audit trails.
Monitor
LLM cost per request, compliance rate, usage patterns, and bulk operation history — exposed in-product so marketing ops can tune governance and AI spend together.
Why this matters
Broken analytics is a tax on every decision a marketing team makes. UTM Copilot shows what happens when you treat UTMs as first-class data with real governance and real tooling: the team trusts the dashboards again, the ops lead stops being a human linter, and the AI does the tedious validation work at the moment it's needed, not a week later in a QA review.
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