The Challenge
A financial services firm was sitting on mountains of valuable data but lacked the resources to analyze it effectively:
- Manual data analysis consuming 20+ hours weekly
- Inconsistent reporting across teams
- Missed opportunities due to delayed insights
- Analysts spending time on data preparation instead of analysis
The Solution
DeepSnow Labs built an automated analytics ecosystem:
- Automated Data Pipeline: ETL processes that clean, normalize, and prepare data automatically
- AI-Powered Analysis: Machine learning models that identify trends, anomalies, and opportunities
- Natural Language Queries: Stakeholders can ask questions in plain English and get instant answers
- Automated Reporting: Dashboards and reports generated and distributed automatically
The Results
The firm transformed from data-rich to insights-driven:
- 20+ hours saved weekly on manual data processing
- Real-time insights instead of weekly reports
- New revenue opportunities identified through AI analysis
- Unified view of business metrics across departments
- Self-service analytics for non-technical stakeholders
Technologies Used
- Python (pandas, numpy)
- OpenAI API for natural language queries
- Custom ML models
- Tableau for visualization
“We now have insights we never knew existed. The AI finds patterns our analysts would have missed.”
— Director of Operations