Case Studies
Real-world projects demonstrating measurable impact across industries.
Healthcare
Result
Reduced operational risk by 35% and improved early detection rates by 50%.
Healthcare
AI Healthcare Risk Platform
Problem: Hospitals lacked predictive analytics for patient risk detection, leading to delayed interventions.
Solution: Designed AI-driven data pipeline and risk prediction dashboards with real-time alerting.
Analysis: Conducted data audit across 3 hospital systems and facilitated stakeholder workshops with clinical staff.
Reduced operational risk by 35% and improved early detection rates by 50%.
View Case Study PythonSQLPower BIAzure ML
SaaS
Result
Increased conversion rate by 60% and freed 20 hours/week per rep.
SaaS
AI Outbound Sales Automation
Problem: Sales teams spent 60% of time on manual lead qualification and outreach.
Solution: Built AI-powered lead scoring model and automated outreach sequences.
Analysis: Mapped sales workflows and identified automation opportunities through time-motion analysis.
Increased conversion rate by 60% and freed 20 hours/week per rep.
View Case Study PythonSQLJiraHubSpot
FinTech
Result
45% faster product decisions and 30% improvement in feature adoption tracking.
FinTech
Product Analytics Dashboard
Problem: Product team lacked centralized metrics, leading to gut-based decisions.
Solution: Created real-time analytics dashboard with custom KPI tracking and automated reporting.
Analysis: Audited existing data sources and conducted KPI workshops with stakeholders.
45% faster product decisions and 30% improvement in feature adoption tracking.
View Case Study SQLTableauPythonJira
Enterprise
Result
Reduced procurement cycle from 14 days to 3 days.
Enterprise
Enterprise Process Automation
Problem: Manual approval workflows caused 2-week delays in procurement.
Solution: Designed automated approval workflow with role-based routing and audit trails.
Analysis: Process mapped end-to-end procurement flow and identified 12 bottlenecks.
Reduced procurement cycle from 14 days to 3 days.
View Case Study ConfluenceJiraPower BILucidchart
SaaS
Result
Reduced churn by 40% within 6 months of implementation.
SaaS
AI Customer Churn Prediction
Problem: Monthly churn rate of 8% with no early warning system.
Solution: Built predictive churn model with proactive retention workflows.
Analysis: Analyzed 2 years of customer behavior data and identified 15 churn indicators.
Reduced churn by 40% within 6 months of implementation.
View Case Study PythonSQLTableauNotion