Email API
Calendar API
Scheduler
Contacts API
ExtractAI
Salesforce email integration
Google email & calendar integration
Outlook email & calendar integration
IMAP integration
Calendar management
Embedded contextual email
Scheduling automation
Automated outreach
Intelligent data extraction
Ecommerce
Telehealth
Real estate
Financial technology
Educational technology
Expense management
Customer relationship management
Applicant tracking system
Communications platform
Marketplaces
Document management
Docs
SDKs
Tutorials
API status
Changelog
Support
Blog
Events
Guides
Inspiration gallery
Partners
Build vs. buy
Customer stories
Security & Compliance
Why Nylas
NewsroomTroubleshooting and support
Purpose of troubleshooting and support
Having a reliable troubleshooting process and accessible support is vital for keeping your AI solutions running smoothly. Even the most advanced systems can hit a snag now and then, so being ready to tackle issues promptly helps keep your workflows on track. Effective troubleshooting also contributes to the long-term success and reliability of your AI implementation.
When working with an AI tool, you might come across some common challenges:
- Parsing errors: Situations where the AI misreads or doesn’t correctly extract information from emails or other data sources.
- Automation failures: Instances where automated workflows don’t kick in as they should or don’t perform the expected actions.
- Integration challenges: Hurdles in connecting the AI tool with your existing systems, software, or platforms.
By being aware of these potential issues, you can anticipate problems and put strategies in place to address them. Taking a proactive approach to troubleshooting and making the most of available support resources can help you maintain the efficiency and effectiveness of your AI solutions.
Common issues and solutions
Issue #1: Inaccurate data extraction due to parsing errors
- Issue: AI can sometimes misinterpret or fail to correctly extract information from emails, resulting in inaccurate or incomplete data.
- Solution: Reevaluate your parsing rules and algorithms. Enhancing the AI model with additional training data that reflects the variety of email formats you encounter can improve accuracy. Fine-tuning keyword recognition and pattern matching helps the AI better understand and extract the necessary information.
- Tips: Regularly monitor the accuracy of your data extraction. Implement feedback mechanisms that allow users to report errors, enabling continuous improvement of the AI’s parsing capabilities.
Issue #2: Automated workflows not executing properly
- Issue: Automation sequences fail to trigger as intended or perform incorrect actions, causing disruptions in your workflows.
- Solution: Review the automation triggers and conditions to ensure they align with the email content and events they’re supposed to respond to. Verify that any recent changes in email templates or content are reflected in your automation settings.
- Tips: Test your automated workflows under various scenarios to validate their reliability. Maintain clear documentation of your automation processes to facilitate troubleshooting when issues arise.
Issue #3: Challenges integrating the AI tool with existing systems
- Issue: Difficulties arise when connecting the AI solution with your current software, platforms, or IT infrastructure, limiting its effectiveness.
- Solution: Collaborate with your IT and development teams to identify and resolve compatibility issues. Utilize APIs or middleware that can bridge gaps between systems. Refer to the AI tool’s technical documentation and support resources for guidance on integration strategies.
- Tips: Plan your integration approach in advance. Engage relevant stakeholders early to ensure all requirements are understood. Consider conducting a pilot integration to identify and address potential issues before a full-scale rollout.
Diagnostic tools and resources
When you’re working with AI systems, having the right diagnostic tools can make troubleshooting much more manageable. Let’s talk about some resources that can help you identify and resolve issues quickly, keeping your workflows running smoothly.
Error logs and reporting
Think of error logs as your system’s diary — they record when something goes wrong, along with details that can help you figure out why. Regularly checking these logs can reveal patterns or recurring issues that might need your attention. Setting up real-time error reporting can also be a game-changer. By getting immediate alerts when problems occur, you can address them promptly before they escalate into bigger headaches.
Performance monitoring
Monitoring key metrics like response times, processing speeds, and the success rates of data extractions or automated tasks gives you a clear picture of how well your system is functioning. Performance dashboards can provide visual insights, making it easier to spot any dips or anomalies. If you notice something off, you can dive in and make necessary adjustments before it impacts your operations.
Testing and debugging workflows
Before rolling out new automation sequences or parsing rules, it’s wise to test them in a controlled environment. Simulating various scenarios — including unexpected ones — helps ensure your workflows handle different situations gracefully. And if something doesn’t work as expected, debugging tools can help you pinpoint exactly where things went wrong, so you can fix the issue without too much fuss.
Submit a support ticket
When working with AI solutions, such as ExtractAI, it’s normal to encounter occasional issues or have questions. Start by checking the available documentation and resources, which often address common problems and provide helpful guidance.
- Using Nylas ExtractAI
- Using the Nylas Order Consolidation API
- ExtractAI and Order Consolidation API scopes
- Merchants and vendors
If you’re still facing challenges after reviewing these materials, don’t hesitate to reach out to the support team for assistance.