Jason AI SDR creates a unique message for every contact by combining your business context with publicly available contact information.
Each email or LinkedIn message uses different wording and structure based on the contact's background and your business inputs. This helps your outreach feel personal and relevant. Since every message is different, it's also less likely to be flagged as spam.
How personalization works
Jason AI SDR uses two main sources to personalize messages:
1. Your business context
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This includes the information you provide about your product or service, such as:
Pain points
Value propositions
Case studies
CTAs
2. Public contact data
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This includes publicly available information about your prospects and their companies.
If you'd like to go beyond default personalization β for example, researching a specific insight, niche angle, or recent achievement β you can use Personalization points for more advanced research.
π Learn more in our Personalization points article
Business context for personalization
Jason AI SDR uses your business inputs to build personalized messages using your key selling points. For example, if you've added:
5 pain points
6 value propositions
4 case studies
3 CTAs
The system selects one of each when generating a message. This creates natural variation, so no two emails are exactly the same.
For example:
One email might use the 2nd pain point, 3rd value proposition, 1st case study, and 2nd CTA.
Another might use the 4th pain point, 1st value proposition, 3rd case study, and 1st CTA.
This keeps your messaging consistent while still making each email feel different.
You can learn more about adding business context in this article.
Contact-specific personalization
Jason AI SDR also personalizes messages using publicly available information about your contacts.
This data may come from:
Web search β Recent news or mentions
LinkedIn activity β Contact's posts and comments from the past 3 months
Company LinkedIn page β The company's About section and recent posts
Company website β Updates from the News, Blog, or What's New sections.
If no relevant information is found, a default template email is used instead. This way, your sequence can continue even when information is limited.
Previewing and editing personalized messages
You can preview how each personalized email will look in the Preview tab.
Before personalization is complete, you'll see a placeholder (Your personalized email will appear here). Messages are generated right before they're scheduled to be sent, so they include the most relevant and up-to-date information. Once ready, you'll see the full version that will be sent.
If you want to personalize a message immediately:
Click Personalize now in the Preview tab for a single contact.
Click Bulk personalize for multiple contacts at once. Please note that it can take up to 3 minutes per contact to generate the full thread.
Once a message is generated, you can also see which sources were used to personalize it. Click the Sources drop-down next to the message to view the details.
This works for all types of personalization, including business context, scraped contact data, and personalization points.
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You can also edit or regenerate messages:
Adjust the content directly in the editor if you want to make quick changes
Click Re-generate preview to create a new version.
Please note that this action can't be undone.
Self-learning AI
Jason AI SDR learns from your feedback and edits to make future messages more relevant and in your style.
Rate messages: In Approval mode, Preview, and Inbox, you'll see π and π buttons.
Learn from edits: Even if you don't rate a message, any changes you make in Pending Approvals, Preview, or the Inbox are recorded. The AI notices how you adjust messages and adapts its voice and style for next time.
The more you interact, the better AI gets at creating messages that fit your tone and style.
π‘How this differs from Personalization points
This article covers default personalization based on your business inputs and publicly available contact data.
If you're looking for deeper, research-based personalization β such as exploring a prospect's achievements, industry trends, or competitors' overview β check out our Personalization points article to learn how to use advanced research options in your sequences.






