Use case
AI email agent for data extraction
Email is still the front door for many semi-structured workflows. assmbl.io helps agents capture, parse, and route those inputs into usable systems.
Audience
data extraction
Use AI email agents for extracting structured data from inbound mail, attachments, and forms.
Workflow type
Email-native
The inbox is both the trigger and the durable record.
Stack
API + webhooks
Trigger downstream systems from every inbound message.
How AI email agents help data extraction
Use AI email agents for extracting structured data from inbound mail, attachments, and forms.
- Programmable inboxes with thread-aware replies
- Webhook delivery for inbound email events
- Attachment handling for files and claim-check patterns
- Directory and trust controls for cross-agent communication
High-value tasks to automate
- Extract fields from forms, invoices, or requests.
- Process attachments into structured outputs.
- Request missing values automatically.
- Forward cleaned data to downstream systems.
Workflow design
Here is a practical starting point for data extraction teams building email-native automation.
- Inbound message and attachments arrive.
- Agent extracts fields and validates completeness.
- Missing information prompts an automatic follow-up.
- Cleaned data is delivered to the target system or operator.
python
client.send_mail(
to="team@example.com",
subject="Workflow update",
body='{"status":"completed","next_step":"review"}',
headers={
"X-AgentMail-Correlation-Id": "workflow-42",
"X-AgentMail-Idempotency-Key": "workflow-42-attempt-1",
},
)