Logistics & Supply Chain
Mid-Market Logistics Firm
60%
reduction in manual data lookups within the first month
Engagement
AI Copilot Development
Duration
8 weeks
Company Size
150 employees
Industry
Logistics & Supply Chain
1The Challenge
Operations staff spent hours each day manually looking up shipment statuses, tracking numbers, and delivery exceptions across three separate systems. The information existed but was scattered and hard to access quickly.
- •Data siloed across TMS, WMS, and CRM with no unified view
- •Customer service reps toggling between 3+ browser tabs per inquiry
- •Average response time of 8-12 minutes for routine status questions
- •No API documentation for legacy WMS system
2Our Approach
- Built a RAG-powered internal copilot connected to their TMS, WMS, and CRM
- Integrated the assistant into their existing Slack workspace
- Implemented permissions-aware retrieval so each team member sees only their relevant data
- Trained operations leads on how to refine prompts and flag incorrect responses
Project Timeline
Data Audit
(Week 1-2)Mapped data sources, schemas, and access patterns across all three systems
Integration
(Week 2-4)Built secure connectors with role-based access controls
RAG Development
(Week 4-6)Developed retrieval pipeline with semantic search and query routing
Deployment & Training
(Week 6-8)Slack integration, user training, and feedback loop setup
3The Results
60%
reduction in manual data lookups within the first month
3
disparate systems unified through a single conversational interface
<2 min
average time to answer a shipment status question (previously 8-12 min)
92%
accuracy rate on retrieved information
“Our ops team went from dreading status checks to just asking the bot. It's become part of how we work.”
Technologies Used
Related Case Studies
Ready to achieve similar results?
Every engagement starts with a conversation. Tell us about your challenges and we'll map out the opportunities.