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External Integrations

Ask Monty can pull in data from external sources to give you a complete picture of your product and customer feedback, including development work, issue tracking, and public discourse.

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GitHub Integration

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What Can Monty Analyze?

  • GitHub Issues: Bug reports, feature requests, discussions
  • Pull Requests: Code changes, reviews, merge status
survey dataset

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Asking About GitHub

"Summarize GitHub issues from the past week"
"What pull requests were merged last month?"
"Show me open issues labeled 'bug'"
"List recent GitHub activity"
Time range options:
  • Maximum lookback: 60 days
  • Can specify: “last week”, “past 30 days”, “this month”

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GitHub Data Fields

For each issue/PR, Monty has access to:
  • Title and description
  • State: Open, closed, merged (for PRs)
  • Labels: bug, enhancement, documentation, etc.
  • Dates: Created, updated, closed
  • Key: Issue/PR number
  • URL: Link to the item
  • Assignees and authors (when available)
Github issues dataset

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Use Cases

GitHub integration excels at creating development summaries. Ask for an executive summary of the team’s work over the past month, and Monty pulls all issues and PRs to summarize what’s been completed, what’s in progress, and what’s planned. This is particularly useful for sprint retrospectives—“Summarize closed GitHub issues from the last 2 weeks”—giving you a quick view of completed work. For bug tracking and feature planning, query open issues directly: “What bugs are currently open in GitHub?” or “Show me the most recent feature requests.” These queries help with prioritization and roadmap planning, and you can export the results for further analysis. Github issues response text

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Combining with Customer Feedback

Link customer feedback to development work: 
"Compare customer bug reports to open GitHub issues"

→ Dataset 1: Customer-reported bugs
→ Dataset 2: Open GitHub bug issues
→ Analysis shows which customer issues have corresponding dev work
Powerful for:
  • Prioritizing GitHub issues based on customer pain
  • Checking if customer requests have tickets
  • Reporting back to customers on fix status

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Jira Integration

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What Can Monty Analyze?

  • Jira Issues: Stories, bugs, tasks, epics
  • Status tracking: To-do, in progress, done
  • Sprint information: Current and past sprint work
Jira issues dataset

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Asking About Jira

"Summarize Jira tickets from the past month"
"What issues are currently in progress?"
"Show me completed work from this sprint"
"List open bugs in Jira"
Time range options:
  • Maximum lookback: 60 days
  • Can specify: “last week”, “this sprint”, “past 30 days”

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Jira Data Fields

For each issue, Monty has access to:
  • Summary and description
  • Issue type: Bug, story, task, epic
  • Status: To-do, in progress, done, etc.
  • Priority: High, medium, low
  • Dates: Created, updated, completed
  • Key: Issue ID (e.g., PROJ-123)
  • URL: Link to the Jira ticket
  • Assignee and reporter
  • Labels and components
Jira issues dataset - expanded

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Use Cases

Team Status Reports: 
"What work has the team completed in the past 2 weeks?"

→ Pulls completed Jira issues
→ Perfect for status updates to leadership
Sprint Summaries: 
"Summarize our current sprint work"

→ Shows in-progress and completed items
→ Great for daily standups or sprint reviews
Bug Analysis: 
"How many critical bugs are open right now?"

→ Filters to high-priority bugs
→ Helps with triage and planning
Roadmap Alignment: 
"What features are we building this quarter?"

→ Shows feature/story tickets
→ Aligns with product roadmap discussions

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Combining with Feedback

Connect development work to customer needs: 
"Compare feature requests from customers to our Jira roadmap"

→ Dataset 1: Customer feature requests
→ Dataset 2: Jira feature tickets
→ Shows gaps and alignments

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What Can Monty Search?

Monty can search the web for:
  • News articles about your product
  • Public discussions and reviews
  • Competitor information
  • Industry trends
  • Blog posts and social mentions
Web search dataset - expanded

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Asking About Web Content

"What has the news said about our recent product launch?"
"How is our product being discussed online?"
"Search the web for mentions of [feature]"
"What are people saying about [competitor] online?"

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Web Search Data

Results include:
  • Page titles
  • Highlighted excerpts: Most relevant passages
  • URLs: Links to sources
  • Published dates: When content was created
  • Relevance scores: How well each result matches
web-search-results

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Use Cases

Launch Monitoring: 
"Search the web for news about our [product] launch"

→ Finds recent coverage
→ Shows public reception
Competitive Intelligence: 
"What are people saying about [competitor]'s new feature?"

→ Gathers competitive insights
→ Informs strategy
Sentiment Check: 
"How is [our feature] being received online?"

→ Shows public discourse
→ Complements internal feedback
Industry Research: 
"What are the latest trends in [industry]?"

→ Finds thought leadership and news
→ Provides market context
Validation: 
"Compare our customer feedback about [issue] to what's
being said online"

→ Dataset 1: Internal customer feedback
→ Dataset 2: Web search results
→ Shows if internal feedback aligns with public perception
Web search dataset - expanded

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External Data Limitations

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Time Ranges

  • GitHub: Maximum 60 days of history
  • Jira: Maximum 60 days of history
  • Web search: Recent results prioritized
  • Feedback: No time limits
If you request longer ranges, Monty will use the maximum available and inform you.

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Data Freshness

External data is fetched in real-time when you ask, but:
  • Recent activity may take minutes to appear
  • Historical data depends on integration setup date
  • Web search shows indexed content (may lag by hours/days)

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Access Requirements

External integrations must be connected and authorized:
  • GitHub: Repository access configured
  • Jira: Project access configured
  • Web search: No special setup needed
If an integration isn’t available, Monty will let you know.

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Best Practices

External integrations work best when combined with internal customer feedback. Don’t just ask “What GitHub issues are open?”—instead, compare open GitHub bugs to customer-reported bugs to see which customer pain points have engineering attention. Use multi-source queries for comprehensive analysis: “Create a monthly product health report including customer feedback, development work, and public sentiment.” This gives stakeholders a complete picture across all data sources. Validate assumptions by connecting dots between sources. Ask “Is the payment issue customers are reporting in GitHub?” to check if customer pain has corresponding engineering work. Monitor launches holistically with queries like “Analyze the [feature] launch including customer feedback, GitHub activity, and web mentions.” Keep time ranges reasonable—under 60 days for GitHub and Jira due to API limitations. Time-bound your analysis: “What happened in GitHub during the last sprint?” or “Show me web mentions from the past week.”
Next: Advanced Features →