Case Study

From Complexity to Clarity: How Trigent Delivered AI at Scale for a leading enterprise

Information Technology (IT) Retail
AI/ML

About the Client​

A global e-commerce enterprise operating in over 80 countries was under increasing pressure to modernize its data and AI infrastructure. With millions of transactions, diverse product categories, and seasonal spikes in traffic, the company relied heavily on machine learning models to forecast demand, optimize inventory, personalize customer experiences, and drive targeted marketing campaigns.

Business Challenge​

However, the company’s existing AI landscape was marked by fragmentation, inefficiencies, and a lack of operational maturity.

Key challenges included

  • Siloed model development across teams, leading to inconsistent methodologies and outputs.
  • Slow model deployment cycles (6–8 weeks) due to manual handoffs and lack of automation.
  • Unreliable performance during peak sales periods, causing forecast inaccuracies and inventory issues.
  • Low trust in AI outputs, as business teams lacked visibility into model decisions and outcomes.
  • Inefficient infrastructure use, with overspending during lulls and performance bottlenecks during spikes.
  • Inconsistent data pipelines, resulting in fluctuating model accuracy.

To turn AI into a strategic advantage, the client needed a standardized, scalable MLOps platform—one that could streamline development, automate deployment, and deliver reliable, business-aligned insights. Trigent was brought in to make that vision real, powered by Databricks.

Trigent Solution

Trigent built a full-stack MLOps platform on Databricks, transforming their ML lifecycle with:

Automated Pipelines across ingestion, training, deployment, and monitoring.

Intelligent Resource Management with predictive autoscaling.

End-to-End Lineage and KPI tracking tied directly to business outcomes.

Advanced Sales Forecasting using time-series modeling, anomaly detection, and market segmentation.

Want to see how Databricks can deliver similar value for your business?

Client Benefits

  • 60% reduction in model deployment cycles (from 6–8 weeks to <7 days).
  • 40% faster data processing and 30% lower operational costs.
  • 35% improvement in sales forecast accuracy.
  • 22% boost in campaign ROI with predictive targeting.
  • 85% of business users reported increased trust in ML decisions.