The first agent on Next Best Action. Reads your team's marketing email samples, runs your checklist against the rendered version, and posts structured feedback into the project management tool you already use. No new dashboard, no new login.
The demo plays through six stages: a Marketo send-sample lands at our inbox, the agent scans every rule, posts a structured comment on the matching ClickUp task, and the requester reads, fixes, and re-sends, all before the senior reviewer is paged.
▶ Watch live demoThe agent is built on a tool-agnostic core. Add an ESP integration, add a project-management integration, and the same engine and rule packs work across both. Here's what's live, what ships next, and what's on the roadmap.
We'll show you what an Email QA Agent looks like running on your stack.
Your team keeps working in the tools they already use. The agent meets them there.
Your requester clicks "Send sample" in your ESP and types our address. That's the only new step in their workflow.
The agent runs your checklist against the rendered email: image dimensions, padding, font sizes, UTM tags, personalization defaults, spelling, footer correctness, and more.
A structured pass/fail comment lands on the matching task in your PM tool, organized by category, addressed to the requester by name.
The requester reads, fixes, re-sends. Your senior reviewer is only paged once everything's clean.
Every comment is structured, friendly, and educational. Junior requesters get the same patient feedback every time, in writing, with links to the best-practices doc. The agent is the first reviewer your green person ever sees.
The same person QA'ing 378 emails per quarter shouldn't be re-explaining UTM tags. The agent catches deterministic mistakes so your senior brain stays on the work that needs it.
The rule engine is plain Python. No model in the loop for the core checks. Per-team rule packs are versioned JSON, so every comment carries a "v2026.05.06" stamp and your QA standards have a real audit trail.
Every team's checklist is different, so we start with a fixed-scope pilot to tune the rule pack against your actual sends. Ongoing operation is a separate, lighter-weight retainer.