Respondable

Respondable is an email polishing tool. Respondable by Boomerang for Gmail analyzes your draft as you type, predicting how likely it is to get a reply and explaining which factors (tone, length, politeness) affect the outcome. Built on machine learning from millions of emails, it gives live, actionable writing advice inside Gmail for outreach, client and internal messages.

Respondable is an email writing assistant built by Baydin, the team behind Boomerang for Gmail. It watches your draft as you type and uses machine learning models trained on millions of messages to estimate how likely that email is to get a reply. The assessment is delivered live, so the feedback arrives while you can still act on it rather than after the message has already gone out. The analysis breaks an email down into the factors that influence response rates and explains each one in plain language. That includes tone, the balance of politeness, positivity and subjectivity, and structural choices such as length and how clearly the message asks for something. Rather than simply scoring the draft, it suggests concrete adjustments, which makes it useful as a learning tool as much as a checking tool. The tool fits naturally into day to day correspondence for people who send a lot of consequential email: sales outreach, recruiting, client communication, internal requests to managers, and any message where a reply matters. It is aimed at individuals and teams who already work in Gmail and want an extra layer of feedback without switching to a separate writing app. Respondable sits alongside grammar checkers and general purpose writing assistants, but its focus is narrower and more practical. Those tools mostly correct language and clarity; Respondable is concerned with whether the message will produce a response and whether the tone lands correctly for the recipient. It is not a full email client or a relationship manager. It is a feedback layer that lives inside the composing window, and it works best for writers who are willing to revise rather than send on the first pass. Because the guidance is driven by statistical patterns from large email datasets, treat it as advice rather than a guarantee. Short messages, unusual contexts and highly personal notes may not map neatly onto the models. In those cases the suggestions are still worth reading, but editorial judgment stays with the sender.