Case Studies

Real results from real implementations. See how organizations across industries are using AI to transform their operations.

* Sample scenarios based on industry workflows

Patient Intake Automation
Healthcare

Patient Intake Automation

Regional Medical Center reduces intake time by 70%

70%
Reduction in intake time
15 min
Average processing time
95%
Data accuracy improvement
$280K
Annual labor savings

The Challenge

A 300-bed regional hospital was spending 45+ minutes per patient on intake paperwork. Staff manually entered data from forms, insurance cards, and referral documents into multiple systems. Errors led to billing delays and patient frustration.

The Solution

We deployed an AI-powered intake system using computer vision for document scanning and natural language processing for form interpretation. The system integrates with their existing EHR and automatically validates insurance information in real-time.

Technologies Used

Computer VisionNLPEHR IntegrationOCR
Loan Document Review Automation
Finance

Loan Document Review Automation

Community bank accelerates loan approvals by 60%

60%
Faster approval process
2 days
Average review time
40%
More loans processed
$450K
Additional annual revenue

The Challenge

A regional bank was taking 3-5 business days to review loan applications. Loan officers manually reviewed hundreds of pages of financial documents, tax returns, and business plans. The backlog was causing them to lose deals to faster competitors.

The Solution

We built a custom AI system that extracts key data points from loan applications, analyzes financial documents, checks for completeness, and flags risk factors. The system provides loan officers with a comprehensive summary and risk assessment in minutes.

Technologies Used

Document AIRisk Assessment ModelsFinancial AnalysisCompliance Checking
Predictive Inventory Management
E-commerce

Predictive Inventory Management

Online retailer eliminates stockouts and reduces overstock by 35%

90%
Stockout reduction
35%
Overstock reduction
18%
Margin improvement
$1.2M
Annual profit increase

The Challenge

A growing e-commerce company was struggling with inventory management. Frequent stockouts lost sales, while overstock tied up cash and led to markdowns. Their spreadsheet-based forecasting couldn't handle 10,000+ SKUs across seasonal patterns.

The Solution

We implemented a machine learning system that analyzes sales history, seasonal trends, marketing campaigns, and external factors to predict demand 30 days out. The system automatically generates purchase orders and alerts for anomalies.

Technologies Used

Time Series ForecastingMachine LearningDemand PlanningAnomaly Detection
Social Media Monitoring for Public Engagement
Government

Social Media Monitoring for Public Engagement

City government improves citizen response time by 80%

80%
Faster response time
8 hours
Average response time
95%
Issue detection rate
3x
Citizen engagement increase

The Challenge

A mid-sized city government needed to monitor social media for citizen concerns, emergencies, and feedback. Manual monitoring across multiple platforms was time-consuming, and many mentions went unnoticed. Response times averaged 48+ hours.

The Solution

We deployed an AI system that monitors social media platforms, categorizes mentions by department and urgency, detects sentiment, and routes issues to appropriate staff. The system provides real-time alerts for emergencies and weekly sentiment reports.

Technologies Used

Social Media API IntegrationSentiment AnalysisNLPReal-time Alerting
AI-Powered Customer Support
Startup

AI-Powered Customer Support

SaaS startup scales support without headcount growth

75%
Tickets auto-resolved
5 min
Average response time
90%
Customer satisfaction
$180K
Annual savings

The Challenge

A fast-growing SaaS startup was overwhelmed with support tickets. Response times were stretching to 24+ hours, and customer satisfaction was declining. Hiring more support staff wasn't sustainable given their burn rate.

The Solution

We fine-tuned an open-source LLM (Llama 3) on their product documentation, previous support tickets, and knowledge base. The AI handles tier-1 questions automatically and provides suggested responses for tier-2 issues. Complex issues are escalated to humans.

Technologies Used

Fine-tuned Llama 3RAG (Retrieval-Augmented Generation)Zendesk APIAWS Deployment

Consistent results across industries

Our Value Sprint methodology delivers measurable impact regardless of industry or use case.

Average ROI Timeline
4-6 mo
Implementation Time
6-8 wk
Average Annual Savings
$350K

Ready to create your own success story?

Let's discuss your operational challenges and explore how a Value Sprint can deliver similar results for your organization.