How Indicium and Databricks Delivered a Faster, AI-Driven Customer Service Operation for a Leading Bank
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A leading digital bank partnered with Indicium and Databricks to automate customer service at scale. The goal: build an AI system to classify complaints with precision, accelerate case handling, and raise service quality across every product line.
Indicium centralized complaint data and automated analysis with Databricks Workflows and MLFlow. The solution unified NLP, sentiment analysis, and RAG models in a single governed environment, with an estimate of 25% faster responses, 20% higher productivity, 25% stronger SLA compliance, and 20% more cases closed per day.
The Challenge: Manual Classification. Dispersed Templates. Slow Turnaround.
Before modernization, the company relied on manual complaint review and classification. Analysts read each case, categorized it by product or issue, and drafted responses from templates. This process slowed responses, caused inconsistencies, and increased operational costs.
The bank needed a partner to automate complaint classification with accuracy and transparency. It also wanted to detect customer dissatisfaction early through sentiment analysis. AI-generated summaries and suggested replies would speed up response time. The solution had to have governance, be auditable and scalable across all products.
The bank chose Indicium for its strong track record in building AI solutions on Databricks. Indicium brought expertise in data engineering, NLP, and governance. Databricks provided a unified, scalable platform. Together, we built a secure foundation designed for continuous improvement.
Building an Intelligent, Scalable System with Databricks Workflows and MLFlow
Indicium designed an AI architecture orchestrated on Databricks. The platform became the operational foundation for complaint management.
NLP Classification: Databricks-trained models detect product, topic, and sub-topic for every complaint.
Sentiment Analysis: AI detects dissatisfaction levels to prioritize critical cases.
RAG-Based Responses: Surfaces accurate, governed replies using internal documentation.
Pipeline Orchestration by Databricks Workflows: Automates ingestion, classification, and response generation in real time.
Continuous Model Monitoring with MLFlow: Tracks model performance and feedback loops for continuous improvement.
With all pipelines and models consolidated on Databricks, the bank gained a governed, end-to-end AI environment that reduced manual effort and improved accuracy.
Operational Efficiency. Customer Satisfaction. Proven ROI.
The project established the foundation for better productivity, governance, and customer experience. Key anticipated outcomes include:
- Up to 25% faster response time with AI-powered classification and automation.
- An estimated 20% higher productivity by accelerating daily case handling.
- The potential for a 25% improvement in SLA compliance, with more cases resolved within deadlines.
- An expected 20% increase in daily case resolution, enhancing customer satisfaction.
- Reduced manual errors and operational costs through intelligent process automation.
- Stronger data governance with full transparency via Databricks Workflows and MLFlow dashboards.
Indicium + Databricks: Powering AI-Driven Service Excellence
Indicium delivered measurable impact fast. With the IndiMesh delivery framework, we accelerated every phase, from design to deployment. This ensured speed, scalability, and governance. Our AI expertise on Databricks helped us bring NLP, sentiment analysis, and RAG models together in a single environment.
Databricks unified platform provided the enterprise-grade foundation the project required. It supports ingestion, orchestration, model training, and monitoring with security and scale. Through Databricks Workflows and MLFlow, the client gained full visibility into every step of the AI lifecycle. This ensured transparency, reproducibility, and compliance.
Indicium led the transformation from start to finish. We trained teams to understand AI outputs and built feedback loops to keep models improving. We also set up governance to ensure reliability. Together, Indicium and Databricks replaced a manual workflow with an intelligent AI operation built for scale, accuracy, and continuous improvement.
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