Engineering an AI-Native Data Foundation for a BFSI Super AppEngineering an AI-Native Data Foundation for a BFSI Super App

The client is a leading Indian financial services company with a digital ecosystem spanning mutual funds, loans, digital gold, spend management, loyalty rewards, and other BFSI services.

As the client expanded their super app ecosystem, deeper insight into user behavior, spending patterns, and customer preferences to deliver more relevant financial experiences at scale became critical to their business.

The client wanted to strengthen hyper-personalization across their digital ecosystem, but customer data was spread across multiple applications and systems. Teams lacked a unified view of customers, making it harder to personalize offers, improve engagement, and run targeted campaigns with confidence. Fragmented data also created operational pressure. Data duplication, inconsistent records, and limited scalability slowed analytics and made it harder to turn customer behavior into timely business action.

Key Challenges

The client had to solve several data foundation issues before it could support personalization at scale.

1. Customer data was spread across 30+ sources, making it difficult to build a unified customer profile.
2. Duplicate and inconsistent records reduced trust in customer insights and slowed decision-making.
3. The existing setup could not scale easily with growing data volumes and evolving business needs.
4. Data governance, security, and quality management required high manual effort across diverse environments.

The client needed a partner who could integrate data engineering, cloud architecture, governance, and AI readiness into a single scalable foundation for personalization. The goal was not limited to centralizing data. The platform had to make customer intelligence more reliable, easier to govern, and ready for real-time business action.

Coditas brought the experience needed to turn scattered customer data into a governed intelligence layer for digital financial services. The engagement aligned with AI Strategy & Readiness Consulting through data readiness, quality controls, and governance foundations, helping the client improve analytics confidence and support personalized customer journeys across the super app ecosystem.

We built a centralized, governed data ecosystem using Google Cloud Dataplex. The platform consolidated customer data from 20+ touchpoint applications into a unified data lake, giving teams a clearer view of user behavior, spending habits, and engagement signals.

Our team improved metadata management, data lineage, access control, and automated quality checks across the ecosystem. Automated governance policies reduced duplicate and inconsistent records, while BigQuery reporting made data quality easier to monitor. The new foundation helped the client power advanced analytics, personalization intelligence, targeted campaigns, loyalty optimization, nudges, and recommendations at scale.

Google Cloud Dataplex, BigQuery, Dataflow, and Google Cloud

The client achieved measurable outcomes across data visibility, governance, analytics readiness, and personalization enablement.

1. Unified customer data from 20+ touchpoint applications into a centralized data lake
2. Integrated customer information from 30+ data sources to support a more complete customer profile
3. Improved personalization across offers, campaigns, loyalty programs, nudges, and recommendations
4. Strengthened data governance through automated quality rules, lineage tracking, and access control monitoring
5. Accelerated analytics by reducing manual data management effort and improving data reliability

The engagement helped the client move from fragmented customer data to a governed intelligence layer built for personalization at scale. Customer behavior, spending patterns, loyalty signals, and campaign data became easier to discover, trust, and activate across the super app ecosystem.

For the client, the project created a stronger data foundation for AI-led personalization, more reliable analytics, and more relevant digital finance experiences.