Demo Case Studies
What This Looks Like in Practice.
These are illustrative examples showing the types of outcomes AI automation delivers across different industries. They are representative scenarios, not documented client engagements.
National Logistics Co.
The Challenge
A 400-person logistics company was processing 12,000+ invoices per month across 12 warehouse locations — manually. A team of 8 accounting staff spent 60% of their time on data entry, matching POs to invoices, and reconciliation. Error rates were running at 4.2%, causing payment delays and vendor relationship strain.
The Solution
We built an end-to-end invoice processing AI that ingests invoices via email and upload, extracts line items with 98.6% accuracy, matches them against open POs in the ERP, and routes exceptions to staff for review. The system handles 94% of invoices fully automatically.
Technology Used
- Document AI / OCR
- ERP API Integration
- Exception routing workflow
- Audit trail and compliance logging
Regional Healthcare Group
The Challenge
A multi-location healthcare group was losing patients to competitors with faster intake processes. New patient intake averaged 45 minutes, heavily paper-based, with staff manually re-entering data into 3 different systems. Scheduling was a full-time job for two coordinators.
The Solution
We built an AI-powered patient intake system with a digital pre-intake form, natural language scheduling assistant, and automated EHR/billing system sync. Staff were retrained as care coordinators rather than data entry clerks. The scheduling AI handles 78% of appointment requests without human involvement.
Technology Used
- Conversational AI scheduling
- EHR integration
- HIPAA-compliant data pipeline
- Multi-system sync
Direct-to-Consumer Brand
The Challenge
A $12M DTC brand was struggling with customer service at scale — support tickets were taking 48+ hours to resolve, personalization was nonexistent, and inventory decisions were driven by gut rather than data. The team of 6 in customer service was overwhelmed.
The Solution
We deployed a three-component AI system: an intelligent customer service agent handling 70% of tickets autonomously, a recommendation engine delivering personalized product suggestions with a 4.1× lift in conversion, and an inventory forecasting model that reduced stockouts by 41%.
Technology Used
- AI customer service agent
- Recommendation engine
- Inventory forecasting model
- CRM integration
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