Case Studies
A selection of engagements across industries — each one a real business problem, a tailored solution, and a measurable outcome. Client names are confidential.
01
Sales & Inventory Forecasting
Global Technology Manufacturing
Classic MLDeep LearningTime-Series
Challenge
- ›Complex inventory management across multiple international markets
- ›Pricing optimization for aged and slow-moving inventory
- ›Sales forecasting accuracy well below business requirements
Our Solution
- ›Time-series forecasting models for multi-market sales prediction
- ›Multi-variable inventory optimization algorithms
- ›Dynamic pricing models for aged stock liquidation
Results
- ›50% accuracy improvement over prior PhD-led manual analysis
- ›Significant cost reduction through better demand forecasting
- ›Architecture designed to scale across additional business segments
02
Competitive Product Review Intelligence
Global Technology Manufacturing
NLPSentiment AnalysisWeb Scraping
Challenge
- ›No scalable way to monitor how products compared to competitors in customer reviews
- ›Employees spending excessive manual hours reading and categorizing online reviews
Our Solution
- ›Automated news and review web scraping pipeline targeting key sources
- ›Sentiment analysis to score and classify customer opinion at scale
- ›Periodic intelligence reports delivered directly to the client team
Results
- ›Eliminated manual review monitoring — process now runs automatically
- ›Faster, data-driven competitive insights with consistent coverage
- ›Freed up employee time for higher-value analysis work
03
Healthcare Worker–Patient Matching
US Home Health Care Technology
ML RecommenderPredictive ModelingReal-Time Optimization
Challenge
- ›Matching the right healthcare workers to the right patient needs at scale
- ›Optimizing workforce allocation across a large distributed network
- ›Maintaining quality of care standards while improving operational efficiency
Our Solution
- ›Multi-factor matching algorithm integrating worker skills, availability, and patient requirements
- ›Predictive modeling for worker–patient compatibility scoring
- ›Real-time optimization engine integrated into the existing platform
Results
- ›Measurable improvement in matching accuracy and patient satisfaction scores
- ›Operational efficiency gains across workforce scheduling
- ›Demonstrated that specialized AI — not off-the-shelf solutions — was essential given the integration complexity
04
Intelligent Billing Process Automation
US Home Health Care Technology
Intelligent AutomationCompliance MonitoringHuman-in-the-Loop
Challenge
- ›Highly complex billing processes prone to errors and delays
- ›Government compliance requirements (EVV — Electronic Visit Verification)
- ›Heavy administrative burden consuming staff time and introducing risk
Our Solution
- ›Automated billing validation and processing pipeline
- ›Compliance monitoring and reporting system aligned with EVV requirements
- ›Exception-handling workflow with human-in-the-loop for edge cases
Results
- ›Significant time savings and reduction in billing errors
- ›Compliance assurance in a highly regulated industry
- ›Provided a blueprint for change management when automating critical workflows
05
AI-Powered Newsletter Automation
US Home Health Care Technology
n8nMLNLPContent Curation
Challenge
- ›Limited client engagement due to infrequent and resource-intensive newsletter production
- ›Small team unable to maintain consistent, high-quality content output
Our Solution
- ›Automated news scraping from targeted industry sources via n8n
- ›AI-powered sentiment analysis to filter for relevant, positive signals
- ›Intelligent content curation ranking articles by relevance before generation
- ›End-to-end pipeline: scrape → analyze → curate → generate → email
Results
- ›80–90% reduction in time spent producing each newsletter
- ›Higher content quality through automated curation and filtering
- ›Scalable communication capability without additional headcount
06
Agent Performance Behavioral Analytics
Insurance / Financial Services
Behavioral DataCustom MetricsPerformance Analytics
Challenge
- ›No way to measure individual agent performance beyond final sales volume alone
- ›Missing behavioral data: abandonment rates, completion times, correction frequency
- ›Could not identify which agents excelled at quoting versus actual conversions
Our Solution
- ›Custom efficiency coefficient formula: Volume / Error rate
- ›Tracked granular metrics: application duration, abandonment rate, corrections per application
- ›Monthly performance reports ranking all agents by efficiency score
Results
- ›Clear identification of top and lowest-performing agents through behavioral metrics
- ›Key insight: agents with high quote volume did not always drive high sales conversions
- ›Enabled data-driven decisions for hiring, training, and performance evaluation
- ›Improved customer satisfaction through better agent quality standards
07
Bot Detection via Behavioral Fingerprinting
Insurance / Financial Services
Pattern RecognitionBehavioral DataAnomaly Detection
Challenge
- ›Personally identifiable information (PII) leaking through automated bot attacks
- ›Internal tools unable to distinguish bots from legitimate applicants
- ›Urgent need to protect customer data from coordinated automated activity
Our Solution
- ›Visualized bot actions step-by-step from raw application event data
- ›Identified attack patterns: attempt count, targeted form fields, timing signatures
- ›Discovered bots specifically targeted driver license and VIN number fields
- ›Isolated two distinct bot timing profiles: under 35ms and 1,000–3,100ms per field interaction
Results
- ›Enabled real-time bot detection using pattern-based threshold alerts
- ›Identified the highest-risk form fields requiring priority protection
- ›Gave the development team concrete patterns to block at the application layer
- ›Uncovered correlation between number of attempts and time-per-field — a new detection signal
08
COVID-19 Impact on Driver Sign-Ups
Gig Economy / Mobility
Time-Series AnalysisRegional AnalysisTrend Visualization
Challenge
- ›No visibility into how daily driver application trends were shifting during the pandemic
- ›No internal data science team to perform the analysis
- ›Required market-specific understanding across multiple geographic regions
Our Solution
- ›Daily trend analysis of applications started versus applications submitted
- ›Regional pattern mapping across all active markets
- ›Correlation analysis tied to the March 26, 2020 emergency declaration
Results
- ›Largest single drop correlated precisely with the March 26, 2020 announcement
- ›Markets 2 and 4 bucked the trend and increased sign-ups while others fell
- ›Market 3 declined until mid-April; all other markets began recovering after April 2
- ›Gave leadership market-specific data to inform resource and communication strategy
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