Gleanly Integration with Emergent | Build Research Repository Apps by Prompt

Gleanly + Emergent

The Gleanly and Emergent integration enables UX researchers, product teams, design organizations, and business analysts to build and deploy custom research repository applications and automated insight workflows by prompt, combining Emergent's full-stack vibe coding capabilities with Gleanly's evidence-based research management platform. This allows teams to create intelligent knowledge systems, unified research dashboards, and cross-platform integrations around their insights without complex development or manual data organization.

With Emergent, you can:

• Extract and sync research experiments, insights, facts, recommendations, and evidence scores from Gleanly's repository

• Build automated reporting systems that connect Gleanly insights with product roadmaps in tools like Jira, Notion, or Asana

• Create intelligent alert workflows that notify teams via Slack when new research supports or contradicts existing insights

• Combine Gleanly with collaboration tools like Miro for workshops, survey platforms like Typeform for data collection, or analytics tools like Google Sheets for custom reporting

• Deploy instantly with encrypted credential management, version control, research sync monitoring, and usage analytics

About Gleanly

Gleanly is a research repository platform specifically designed to help organizations store, organize, and connect insights from UX research, marketing research, and business analytics in a single searchable, secure, and scalable location. The platform enables evidence-based decision making through its innovative approach to research knowledge management.

• Living Research Repository: Centralized repository where insights evolve over time as new research supports or disproves existing findings, storing what teams actually learned rather than raw data, presenting information in ways organizational members can understand and directly benefit from, and maintaining comprehensive provenance for important decisions

• Atomic UX Research Framework: Built on Atomic UX Research methodology for storing user research at scale with adoption from organizations including The European Commission and Uber, enables teams to structure research in granular connected ways, supports building comprehensive evidence chains across studies, and facilitates connecting facts from across the business without limiting organizational scale

• Innovative Evidence Scoring System: Confidence scores that allow users to judge quantity and quality of evidence backing insights at a glance, combines multiple factors including evidence type, age, and amount for comprehensive assessment, enables teams to move quickly with confidence on well-supported insights, and provides transparency into research strength for stakeholder communication

• Comprehensive Insight Management: Experiments tab stores and manages all completed studies and research projects, Insights tab allows searching and creating insights by connecting supporting or disproving facts, Recommendations tab enables developing actionable recommendations based on research evidence, Facts tab organizes individual findings with tagging and connection capabilities

• Flexible Data Input Methods: Direct upload of research files from various study formats, bulk tagging and connection capabilities for linking facts to insights and recommendations, browser tool that allows instant text selection and addition from web sources, and integration capabilities for pulling research from external platforms

Why Integrate Gleanly with Emergent?

Connecting Gleanly research insights with product roadmaps, design tools, collaboration platforms, or custom reporting systems typically requires manual exports, copying insights across tools, building complex integration workflows, or developing custom scripts for data synchronization. Each cross-platform workflow or insight distribution system can consume significant time or create knowledge silos.

Emergent removes that complexity:

• Build by prompt: Describe the research workflow or insight distribution system you need and the Gleanly data you want to access. Emergent automatically scaffolds the UI, data connections, evidence aggregation logic, and cross-platform integrations.

• Research-aware mapping: Emergent understands Gleanly's data structure including experiments, insights, facts, recommendations, evidence scores, tags, and confidence assessments, helping you map and synchronize research knowledge accurately across your product development and design technology stack.

• Secure by design: Built-in encrypted credential vaults, environment isolation (development, staging, production), role-based access control matching research permissions, comprehensive audit logs, and secure data handling ensure your UX research, user insights, and strategic recommendations remain protected and compliant.

• Automated workflows: Scheduled insight syncs for stakeholder updates, real-time trigger systems when evidence thresholds change, automatic retry logic with exponential backoff, intelligent caching for performance optimization, and detailed error handling are included out of the box for reliable research automation.

• Multi-tool orchestration: Seamlessly combine Gleanly with Jira for linking insights to product features, Notion for research documentation, Slack for team notifications on new findings, Miro for collaborative synthesis workshops, Airtable for custom research tracking, Google Sheets for quantitative analysis, and any other tool in your stack to build complete research operations ecosystems.

How Emergent Works with Gleanly in Real Time?

Step 1: Describe Your App

Example: "Build a research insights dashboard that syncs Gleanly high-confidence insights to Notion, posts Slack alerts when new evidence contradicts existing assumptions, and generates weekly insight reports in Google Sheets organized by product area."

Step 2: Declare Integrations

Say "Gleanly + Notion + Slack + Google Sheets." Emergent configures authentication methods, data extraction patterns, evidence threshold triggers, and recommended workflow structures.

Step 3: Secure Credentials

Provide your Gleanly account credentials or API access tokens. All authentication details are encrypted and stored in isolated vaults for development, staging, and production environments with automatic credential rotation support.

Step 4: Select Data Objects and Map Fields

Emergent identifies available Gleanly data including research experiments with study details and dates, insights with confidence scores and evidence links, individual facts with tags and sources, recommendations with supporting evidence, confidence scores and evidence quality metrics, and organizational tags and categorizations. It guides you to map fields accurately (for example, Gleanly Insight to Notion database entry, Confidence Score to decision threshold, Fact Tags to product categories, Recommendations to action items).

Step 5: Configure Schedules and Triggers

Set up scheduled exports for weekly stakeholder reports, define event-based triggers (new high-confidence insights, contradicting evidence detected, recommendation updates, confidence score changes), create real-time monitoring for research evolution, or establish on-demand actions via dashboard buttons for manual insight distribution.

Step 6: Test and Preview

Run test data extractions from Gleanly, validate confidence score calculations and evidence aggregation logic, simulate trigger conditions for various research scenarios, check that insight relationships and fact connections preserve correctly across platforms, review detailed execution logs with error traces, and verify cross-platform data synchronization accuracy.

Step 7: Deploy

Deploy your research knowledge application with one click, complete with version control, real-time monitoring dashboards for insight evolution, error alerts with automatic retry logic, research update tracking, and usage analytics. Roll back to previous versions or iterate with new prompts anytime.

Step 8: Expand

Add new automation steps like AI-powered insight synthesis recommendations, integrate with additional research tools like user testing platforms, connect with analytics systems for quantitative evidence correlation, build stakeholder-facing research portals, and extend capabilities as your research operations mature.

Popular Gleanly + Emergent Integration Use Cases

1. Build a Product Insight Distribution System Using Emergent with Gleanly + Notion Integration

Automatically sync high-confidence research insights, supporting evidence, and recommendations from Gleanly to Notion product documentation for unified knowledge management and cross-functional visibility.

How It's Built with Emergent?

• Write your prompt: "Sync Gleanly insights with confidence scores above 80 percent to Notion databases organized by product area with linked evidence."

• Declare integrations: Choose Gleanly + Notion Integration.

• Share credentials securely: Connect Gleanly account credentials and Notion integration token in encrypted vaults.

• Select data objects and map fields: Map insights to database entries, link related facts, sync recommendations, preserve tags, include experiment sources, and maintain evidence chains.

• Configure schedules and triggers: Trigger immediate sync for new high-confidence insights, schedule daily updates, create weekly rollups, enable threshold triggers, or set manual sync buttons.

• Test and preview: Validate confidence filtering, check evidence relationships, ensure recommendation formatting, verify tag mapping, test bidirectional linking, and review historical migration.

• Deploy: Activate production distribution with monitoring, error notifications, version control, access controls, and automated backups.

• Expand: Add AI-generated summaries, integrate roadmap tools, build custom Notion views, connect design tools, or create automated newsletters.

Outcome: Democratized research access across product organization, eliminated knowledge silos between research and execution teams, evidence-based product decisions with clear confidence levels, maintained research provenance for strategic choices, and improved cross-functional alignment through shared insights.

2. Build a Research Alert System Using Emergent with Gleanly + Slack Integration

Create intelligent Slack notifications when new research insights emerge, existing insights gain or lose confidence, contradicting evidence appears, or high-value recommendations are added to Gleanly.

How It's Built with Emergent?

• Write your prompt: "Send Slack alerts when Gleanly insights reach high confidence, new evidence contradicts assumptions, or urgent recommendations are created."

• Declare integrations: Select Gleanly + Slack Integration.

• Share credentials securely: Authorize Gleanly account access and Slack workspace connection.

• Select data objects and map fields: Choose insights and evidence updates, define alert conditions, specify Slack channels, and customize message formats with summaries and links.

• Configure schedules and triggers: Enable real-time monitoring for high-impact changes, trigger alerts when confidence thresholds cross, create daily digests, send weekly highlights, or establish urgent alerts.

• Test and preview: Simulate research scenarios, validate message formatting with confidence indicators, ensure conditional logic, verify routing to appropriate teams, and test throttling mechanisms.

• Deploy: Go live with continuous monitoring, automatic retry, rate limit handling, escalation rules for critical contradictions, and comprehensive event logging.

• Expand: Add AI-generated explanations, include recommended actions, integrate with calendar tools, create two-way Slack interactions, or build threaded discussions.

Outcome: Proactive research-informed decision making, immediate awareness of insight evolution and confidence changes, reduced research-to-action lag time, improved team responsiveness to user findings, and enhanced research visibility across product organization.

3. Build a Stakeholder Research Dashboard Using Emergent with Gleanly + Google Sheets Integration

Export Gleanly insights, confidence scores, evidence summaries, and research recommendations to Google Sheets for executive reporting, cross-study analysis, and strategic planning presentations.

How It's Built with Emergent?

• Write your prompt: "Sync Gleanly insights organized by product area to Google Sheets weekly with confidence scores, evidence counts, and recommendations."

• Declare integrations: Pick Gleanly + Google Sheets Integration.

• Share credentials securely: Connect Gleanly credentials and Google Sheets OAuth access.

• Select data objects and map fields: Map insights to Sheets rows with confidence scores, evidence counts, recommendation summaries, product area tags, experiment sources, researcher attributions, and auto-calculation formulas.

• Configure schedules and triggers: Schedule weekly Monday syncs, enable month-end reports, create on-demand exports, trigger updates for major insights, or set continuous syncs.

• Test and preview: Validate confidence accuracy, check evidence aggregations, ensure pivot table updates, verify trend analysis, test filtering logic, and review visualization compatibility.

• Deploy: Activate scheduled reporting with validation checks, error notifications, version control, access controls for sensitive insights, and automated archival.

• Expand: Add automated visualizations, integrate with OKR tools, build predictive models, connect with budget planning, or create custom stakeholder views.

Outcome: Executive-friendly research visibility without requiring Gleanly access, data-driven strategic planning based on evidence strength, simplified quarterly and annual research impact reporting, improved resource allocation for research initiatives, and enhanced stakeholder confidence in research-backed decisions.

4. Build a Research-to-Roadmap Connector Using Emergent with Gleanly + Jira Integration

Link Gleanly research insights and recommendations directly to Jira product backlogs, epics, and user stories to ensure evidence-based feature prioritization and maintain research traceability through development.

How It's Built with Emergent?

• Write your prompt: "Connect Gleanly high-confidence insights and recommendations to Jira epics with evidence links and update priorities when support strengthens."

• Declare integrations: Choose Gleanly + Jira Integration.

• Share credentials securely: Connect Gleanly account access and Jira API token or OAuth credentials.

• Select data objects and map fields: Map recommendations to Jira stories, link insight confidence to priority levels, sync supporting facts to descriptions, connect product tags to components, preserve researcher attributions, and maintain bidirectional links.

• Configure schedules and triggers: Trigger automatic Jira issue creation at actionable thresholds, update priorities when confidence increases, sync daily for evolving research, create grooming reports, or enable manual sync actions.

• Test and preview: Validate issue creation logic, verify confidence-to-priority mapping, check evidence descriptions, ensure product routing, test bidirectional sync, and review custom field population.

• Deploy: Enable production research-to-roadmap integration with monitoring, error notifications, automatic retry, detailed audit trails, and rollback capabilities.

• Expand: Add AI-powered feature proposals, integrate with sprint planning, build impact tracking, connect with customer feedback platforms, or create research coverage reports.

Outcome: Evidence-based product roadmaps with clear research provenance, reduced prioritization debates through confidence score visibility, maintained research traceability from insight through shipped feature, improved cross-functional alignment between research and engineering, and measurable research impact on product decisions.

5. Build a Collaborative Research Synthesis System Using Emergent with Gleanly + Miro Integration

Sync Gleanly insights, facts, and evidence relationships to Miro boards for collaborative research synthesis workshops, affinity mapping sessions, and cross-study pattern identification.

How It's Built with Emergent?

• Write your prompt: "Export Gleanly insights and supporting facts to Miro boards organized by themes with sticky notes and visual evidence connections."

• Declare integrations: Choose Gleanly + Miro Integration.

• Share credentials securely: Connect Gleanly credentials and Miro API access token.

• Select data objects and map fields: Map insights to sticky notes with color-coded confidence, sync supporting facts as connected notes, represent contradicting evidence distinctly, preserve tags as frames, include researcher notes, and maintain links to Gleanly.

• Configure schedules and triggers: Trigger board creation for synthesis workshops, schedule pre-workshop syncs, enable real-time updates during sessions, create post-workshop exports, or set ad-hoc syncs.

• Test and preview: Validate visual organization, check confidence color coding, ensure sticky note formatting, verify tag-based grouping, test bidirectional sync, and review board templates.

• Deploy: Activate workshop preparation automation with monitoring, error notifications, version control, team access provisioning, and automated archival.

• Expand: Add AI-powered theme detection, integrate with video conferencing, build automated synthesis reports, connect with project management, or create research pattern libraries.

Outcome: Enhanced collaborative sense-making from distributed research findings, visual evidence exploration enabling pattern recognition across studies, inclusive workshop participation leveraging Gleanly's organized evidence, accelerated synthesis cycles through automated board preparation, and preserved synthesis artifacts documenting team reasoning and decision evolution.