AI agents analyze every inbound email for sentiment, tag urgent messages, and route escalations — before a frustrated customer becomes a lost account.
Support teams process hundreds of emails daily. A frustrated customer who waited three weeks for a refund sits in the same queue as a routine status check. By the time a human spots the tone, the customer has already posted a negative review or initiated a chargeback. Sentiment signals are present in every email — the problem is reading them at scale, in real time.
MultiMail delivers inbound emails as structured markdown via webhook and stores them queryable via API. An AI agent subscribes to inbound webhooks, reads the email body via read_email, scores sentiment using your model of choice, and calls tag_email to mark urgency level. High-negative emails can trigger an immediate reply draft or route to a priority queue — all without human intervention on the detection step.
MultiMail fires an inbound webhook to your endpoint when a message arrives. The payload includes the email ID, sender, subject, and a markdown-rendered body. Your agent receives this event and kicks off the analysis pipeline immediately — no polling required.
Use read_email to retrieve the full message body. MultiMail stores emails as clean markdown, stripping HTML noise and normalizing quoted reply chains — so your model sees the customer's actual words, not a soup of style tags and duplicate quoted text.
Pass the subject and body to your LLM or classification model. Classify tone as positive, neutral, negative, or hostile. Also detect escalation language: mentions of refunds, cancellations, legal threats, or explicit time pressure. The normalized markdown makes token usage predictable.
Call tag_email with structured labels like sentiment:negative and urgency:critical. Tags are queryable via the API, so your support tooling, dashboards, and routing rules can filter on sentiment without any schema changes or separate data stores on your end.
For emails classified as high-urgency or hostile, trigger downstream actions: draft a holding reply via reply_email for human review, push a Slack alert, or move the thread to a priority queue. The agent handles detection autonomously; humans handle resolution language.
Pick your platform, copy the prompt, and paste it to your AI agent — it sets up MultiMail and builds the whole flow. Nothing to fill in.
Sentiment scoring runs on every inbound email via webhook — no batch delays. A frustrated customer gets flagged within seconds of sending, not hours later when a human eventually scans the queue.
tag_email writes labels like sentiment:negative and urgency:critical that your existing tooling can filter on. No ETL pipeline, no separate sentiment database — the signal lives with the email and is accessible via the same API.
MultiMail normalizes emails to clean markdown before your agent reads them, stripping HTML noise and deduplicating quoted reply chains. You're not burning tokens on boilerplate — just the customer's actual words.
Aggregate sentiment tags over a rolling window to surface accounts sending multiple negative emails in a week. That pattern is a churn signal your CSM team can act on before a cancellation request lands.
Monitored mode lets the agent tag and route autonomously. For holding replies to hostile emails, gated_send keeps a human in the loop on the specific language used — detection is automated, escalation tone is not.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a hosted MCP server. Formally verified in Lean 4.