Your company's most valuable data is trapped in separate systems. We connect them so you can finally use it.
Conservative assumptions. Same-year payback. No headcount reduction required.
$1.26M
Combined Annual ROI
5
Detailed Use Cases
100+
Platform Integrations
Jump to Use Case:
Sales & Marketing Alignment: Stop Losing Deals in the Handoff
HubSpotSalesforceZendesk
3-5x
Annual ROI
18-25%
Sales cycle reduced
30-45%
Lead conversion improved
10+ hrs/week
Sales rep productivity
The Problem
Your marketing team generates leads in HubSpot. Sales works them in Salesforce. Customer data lives in Zendesk. Nobody sees the full picture.
Current State:
- •Sales reps spend 8-12 hours/week manually updating records and chasing down lead context
- •30-40% of marketing qualified leads go cold because sales doesn't have the full story
- •Deals take 20% longer to close because reps are flying blind on customer interactions
- •You're spending $50K-$150K/year on tools that don't talk to each other
Board View Summary
Annualized Hard-Dollar Impact:
- Sales cycle time reduction:18-25%
- Lead-to-customer conversion improvement:30-45%
- Sales rep productivity gain:10+ hrs/week
- Total:3-5x ROI in first year
Payback:
Most clients see 3-5x return in first year from time savings and faster deal velocity
Risk Profile:
No rip-and-replace, APIs and middleware, role-based access
Success Metrics:
- Sales cycle time (target: 18-25% reduction)
- Lead conversion rate (target: 30-45% improvement)
- Sales rep admin time (target: 10+ hours/week reduction)
- Marketing ROI visibility (target: 100% campaign attribution)
What This Looks Like in Practice
Week 1-2:
- →Sales reps see complete lead history in CRM
- →Every email opened, content downloaded, support ticket filed
- →No more "let me look that up"
Week 3-4:
- →Automated lead scoring based on actual behavior across all systems
- →Reps stop wasting time on tire-kickers
- →Focus on buyers showing real intent
Month 2:
- →Marketing sees which campaigns are generating revenue, not just leads
- →Double down on what works, kill what doesn't
Month 3+:
- →Sales cycle reduced by 18-25% (close deals faster)
- →Lead-to-customer conversion up 30-45% (better targeting, better context)
- →Sales rep productivity up 10+ hours/week (less admin, more selling)
- →Marketing ROI visibility for the first time (finally prove what's working)
All responses are citation-backed and role-restricted based on data access permissions.
IT Operations Efficiency
AWSServiceNowSlack
~$189K
Annual ROI
$63K
Incident response efficiency
$66K
Ticket routing improvement
$60K
Engineering interruption reduction
The Problem
IT teams spend excessive time manually diagnosing cloud infrastructure issues, routing tickets, and keeping stakeholders informed—creating reactive firefighting instead of proactive management.
Current State:
- •Engineers manually check AWS CloudWatch, logs, and dashboards during incidents
- •ServiceNow tickets lack cloud context, requiring back-and-forth investigation
- •Teams waste time in Slack asking "Is this a known issue?" or "Who's handling this?"
- •Root cause analysis happens in silos across tools
Board View Summary
Annualized Hard-Dollar Impact:
- Incident response efficiency:~$63K
- Ticket routing improvement:~$66K
- Engineering interruption reduction:~$60K
- Total:~$189K/year
Payback:
Within the same fiscal year
Risk Profile:
Internal-only, role-based access, real-time monitoring
Success Metrics:
- Mean Time To Resolution (MTTR) (target: 4 hours → 2.5 hours)
- Engineering interruptions (target: 50% reduction)
- Ticket escalation rate (target: 30% reduction)
- First-time resolution rate (target: +20%)
What This Looks Like in Practice
In Slack:
- →"Is the API gateway service healthy?" → Real-time AWS CloudWatch status
- →"Show me all EC2 instances in us-east-1 over 80% CPU" → Instant AWS query
- →"Have we seen error code 502 before?" → ServiceNow historical context + resolution steps
- →"Who owns the payment-processing Lambda function?" → AWS resource tagging + ServiceNow CMDB
In ServiceNow:
- →Tickets auto-enriched with AWS context: Instance ID, region, CPU/memory usage
- →Recent deployments or configuration changes shown automatically
- →Related CloudWatch alarms linked to tickets
- →Suggested runbooks from past incidents provided
For Engineers:
- →"Summarize today's incidents" → ServiceNow tickets + AWS events + Slack context
- →"Draft a postmortem for incident INC-12345" → AWS logs + timeline + affected services
- →"Alert me if any production Lambda function exceeds 5% error rate" → Proactive monitoring
All responses are citation-backed and role-restricted based on data access permissions.
E-commerce Operations Intelligence
ShopifyNetSuiteZendesk
~$185K
Annual ROI
$108K
Operations efficiency
$52K
Customer support efficiency
$25K
Revenue leakage prevention
The Problem
E-commerce operations teams manually reconcile orders, inventory, and customer support data across disconnected systems—causing fulfillment delays, stockouts, and frustrated customers.
Current State:
- •Operations manually exports Shopify orders into NetSuite for fulfillment
- •Inventory discrepancies between Shopify and NetSuite cause overselling
- •Customer support can't see order status without toggling between systems
- •Finance lacks unified view of revenue, returns, and COGS
Board View Summary
Annualized Hard-Dollar Impact:
- Operations efficiency:~$108K
- Customer support efficiency:~$52K
- Revenue leakage prevention:~$25K
- Total:~$185K/year
Payback:
Within the same fiscal year
Risk Profile:
Internal-only, role-based access, real-time inventory sync
Success Metrics:
- Order processing time (target: 3 hours/day → 30 min/day)
- Inventory accuracy (target: 99%+ sync between Shopify/NetSuite)
- Average handle time (AHT) for order inquiries (target: 25% reduction)
- Oversell rate (target: <0.1%)
What This Looks Like in Practice
Operations:
- →"Show me all Shopify orders awaiting fulfillment" → Real-time order queue
- →"Which products have inventory discrepancies?" → Shopify vs NetSuite comparison
- →"Update inventory for SKU-12345 to 50 units" → Syncs across both systems
- →"Generate daily fulfillment report" → Orders, shipments, and exceptions
Customer Support:
- →"Where is order #45678?" → Shopify order details + NetSuite fulfillment status + shipping tracking
- →"Has this customer's refund been processed?" → NetSuite refund status linked to Shopify order
- →"Show me all orders from this customer" → Unified customer order history
Finance:
- →"What's our GMV for Q4?" → Shopify sales + NetSuite returns/refunds
- →"Show me COGS by product category" → NetSuite cost data linked to Shopify sales
- →"Which products have the highest return rate?" → Cross-system analysis
All responses are citation-backed and role-restricted based on data access permissions.
Manufacturing Supply Chain Intelligence
SAP ERPJiraConfluence
~$345.6K
Annual ROI
$135K
Production delay reduction
$57.6K
Quality engineering efficiency
$78K
ECR processing efficiency
$75K
Supplier quality improvement
The Problem
Manufacturing and supply chain teams manually track production issues, quality incidents, and engineering changes across disconnected systems—causing delays, rework, and missed root cause prevention.
Current State:
- •Quality issues logged in Jira, but lack ERP context (batch numbers, suppliers, BOMs)
- •Engineering change requests (ECRs) documented in Confluence, manually synced to SAP
- •Production planners manually check SAP for material availability and Jira for known issues
- •Root cause analysis requires manually correlating data across all three systems
Board View Summary
Annualized Hard-Dollar Impact:
- Production delay reduction:~$135K
- Quality engineering efficiency:~$57.6K
- ECR processing efficiency:~$78K
- Supplier quality improvement:~$75K
- Total:~$345.6K/year
Payback:
Within the same fiscal year
Risk Profile:
Internal-only, role-based access, real-time production data
Success Metrics:
- Mean Time To Resolve (MTTR) for production issues (target: 30% reduction)
- Quality incident root cause time (target: 6 hours → 2 hours)
- ECR processing time (target: 50% reduction)
- Supplier defect rate (target: 15% reduction)
- Production uptime (target: +2%)
What This Looks Like in Practice
Production Floor:
- →"Show me all known issues with Part Number ABC-123" → Jira history + SAP batch data
- →"Has this supplier caused quality issues before?" → SAP supplier data + Jira incidents
- →"What's the approved workaround for this error code?" → Confluence documentation + past Jira resolutions
- →"Alert me if any critical material drops below safety stock" → SAP inventory monitoring
Quality Engineering:
- →"Generate root cause analysis for Jira issue QA-456" → SAP batch traceability + supplier data + historical incidents
- →"Which suppliers have the highest defect rate this quarter?" → SAP quality data + Jira incidents
- →"Show me all open quality issues by severity" → Jira dashboard with SAP context
Engineering:
- →"Validate ECR-789 against current BOMs" → SAP BOM data + inventory impact
- →"What's the lead time impact of this engineering change?" → SAP supplier lead times + inventory levels
- →"Update BOM for Product XYZ per ECR-789" → Automated SAP update with validation
All responses are citation-backed and role-restricted based on data access permissions.
Customer Support & Engineering Knowledge Recovery
ConfluenceJiraZendesk
~$224.5K
Annual ROI
$80K
Support labor efficiency
$67K
Engineering interruption reduction
$40.5K
Customer Success efficiency
$37K
Escalation rate reduction
The Problem
Support, Customer Success, Engineering, and Services teams spend excessive time searching across Confluence, Zendesk history, and Jira to answer repeat questions or diagnose known issues—creating reactive firefighting instead of proactive customer engagement.
Current State:
- •Support spends significant time searching for answers across disconnected systems
- •Engineers frequently interrupted with "Have we seen this before?" questions
- •Customer Success can't quickly find product documentation or known workarounds
- •Zendesk tickets lack context from Jira issues or Confluence documentation
- •Tribal knowledge exists in Slack threads, not captured in systems
Board View Summary
Annualized Hard-Dollar Impact:
- Support labor efficiency:~$80K
- Engineering interruption reduction:~$67K
- Customer Success efficiency:~$40.5K
- Escalation rate reduction:~$37K
- Total:~$224.5K/year
Payback:
Within the same fiscal year
Risk Profile:
Internal-only, role-based access, citation-backed responses
Success Metrics:
- Time-to-first response (target: 30% reduction)
- Escalation rate (target: 25% reduction)
- Engineering hours reclaimed (target: 40 hours/month)
- Average handle time (AHT) (target: 20% reduction)
- Customer Satisfaction (CSAT) (target: +10 points)
What This Looks Like in Practice
Support Team:
- →"Have we seen this error before?" → Zendesk ticket history + linked Jira issues + Confluence documentation
- →"What is the approved troubleshooting flow for login issues?" → Canonical Confluence source with step-by-step guide
- →"Is this a known bug or fixed issue?" → Jira status with context and resolution timeline
- →"Show me all tickets related to the API timeout issue" → Cross-system search with citations
Customer Success:
- →"What's the onboarding checklist for Enterprise customers?" → Confluence documentation
- →"Has this customer reported issues before?" → Zendesk history with Jira context
- →"Draft a summary of known issues for quarterly business review" → Sourced from Jira + Zendesk
- →"What features are in the roadmap for Q2?" → Jira epics and release notes
Engineering:
- →"Which customers are affected by bug PROD-456?" → Jira issue linked to Zendesk tickets
- →"Generate an internal escalation summary for this issue" → Structured report with citations
- →"What's the workaround we documented for this edge case?" → Confluence knowledge base
- →"Show me all P1 issues from last quarter" → Jira query with customer impact from Zendesk
All responses are citation-backed and role-restricted based on data access permissions.
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