Relay.app Upgrades AI Assistant and Table Formatting for Cross-Platform Automation

Relay.app released two substantive improvements to its automation platform. First, the AI Assistant received capability upgrades—though Relay hasn't detailed the specific enhancements, this typically signals improved prompt handling, better context retention, or expanded model integration. Second, rich text table support now extends to Slack and email outputs, meaning builders can structure data more intelligently rather than defaulting to plain text or basic formatting.

The table enhancement addresses a real friction point: automation platforms often force data into linear text or crude formatting. Rich tables let you preserve structure when surfacing information to users—critical for dashboards, reports, or notification workflows where format matters.

• AI Assistant improvements likely enhance reliability or add new model options for automation workflows
• Rich text tables now render properly in Slack messages and email bodies, not just in Relay's native interface
• Update targets teams managing cross-platform data flows where formatting has been a bottleneck

Most no-code automation platforms force a choice: send raw data (unreadable at scale) or spend engineering time templating output. Rich text tables remove that trade-off for Relay users. You can now pipe structured data—API responses, database queries, aggregated metrics—directly into Slack or email with proper formatting. This reduces the cognitive load on end-users interpreting automation results.

The AI Assistant upgrade is less transparent, but it signals Relay is competing on intelligence, not just orchestration. As AI becomes table stakes in automation platforms, Relay is acknowledging that smarter assistants mean fewer manual workflows and better prompt-to-action conversion.

For builders already using Relay, this is a capability unlock. For evaluating Relay against competitors, you now have clearer visibility into formatting and AI sophistication—two decision factors that often get glossed over in tool comparisons.

• Removes the formatting bottleneck that makes automation outputs hard to consume in chat and email
• AI improvements may reduce the trial-and-error cycle when building complex automation flows
• Positions Relay more competitively against platforms with stronger AI or native rich formatting

If you're using Relay, inventory your existing automations and identify any that output tabular or structured data. These are immediate candidates for refactoring with the new rich text table feature. The payoff is immediate: cleaner outputs, fewer support questions about data interpretation, and better user adoption of automation.

For the AI Assistant enhancement, test prompt patterns you previously abandoned due to reliability. Relay's upgrades may now handle edge cases or complex instructions that failed before. Run a small batch of previously problematic automations through the improved assistant to see if you can simplify your workflows.

If you're still evaluating Relay against competitors like Zapier or Make, use these updates as a comparison point. Request live demos showing table output in your target channels (Slack, email) and ask how the AI assistant handles your specific use cases. Concrete examples beat feature lists.

• Audit current automations for structured data outputs and flag them for table formatting
• Retest complex AI-driven prompts that previously required workarounds or manual intervention
• Request vendor comparisons that show table rendering and AI reliability side-by-side in your actual channels

This update reflects a broader pattern: no-code automation platforms are moving beyond orchestration into intelligence and presentation. Zapier, Make, Pipedream, and others have all added AI capabilities in the past 18 months. Relay's focus on output formatting alongside AI suggests the real competitive moat is no longer connecting tools—it's making automation useful and trustworthy.

The emphasis on Slack and email formatting is deliberate. These are the channels where humans actually consume automation results. Platforms that optimize for readability and usability at that last-mile step will win adoption, especially in operations and support teams where output clarity directly impacts decision-making speed.

• AI capability is now table stakes—the question is depth, not presence
• Output formatting is emerging as a competitive factor previously overlooked in automation platform evaluations
• Focus on end-user consumption suggests automation platforms understand that builds fail not from connection issues but from poor information delivery