How Lendi Group improved time to market for AI features by 40% with MongoDB

Powered by its Artificial Intelligence (AI) platform, Lendi Group helps Australians to find, buy and own through an integrated ecosystem spanning property search, buyer advocacy, mortgage broking, conveyancing, and ownership tools.

To achieve this, Lendi Group rethought its data architecture. Working with MongoDB, the team moved away from legacy infrastructure and built an operational data layer (ODL), creating the foundation for the next generation of Lendi Group’s AI-powered services.

Legacy data architecture complexity not suited for AI 

One of the first challenges Lendi Group took on was to address the fragmented data infrastructure it inherited from the Lendi-Aussie Home Loans merger. Over time, the combined organisation’s infrastructure had bloated to more than 500 deployable components, built on a mixture of relational, like PostgreSQL, and non-relational databases.

This architecture had become complex, time-consuming, and expensive to maintain. However, that was only half the problem. More importantly, the group’s data infrastructure lacked the consistency and agility required to build and deliver AI services.

For Lendi Group’s vision to succeed, its AI agents needed a complete, real-time picture of the customer. This meant combining complex, diverse data sets, including property data (e.g, real-time valuations, suburb trends, and geospatial information), finance data (e.g., credit reports and Open Banking feeds), and behavioural data (e.g., customer goals, interactions, and platform usage patterns).

Furthermore, as mortgage broking is tightly regulated, Lendi Group was obligated to meet the highest security and privacy compliance standards. This meant building an AI platform that was compliant from day one and which could easily adapt to future regulations.

Managing complexity, building AI features at scale, and keeping high security compliance standards

Within the first week of the ODL project, Lendi Group had already determined what would power their infrastructure: MongoDB Atlas.

As Will Hargan, Senior AI Systems Engineer at Lendi Group, noted, “There simply wasn’t another option that offered the flexibility of the document model and the power of MongoDB’s integrated, AI-ready data platform.”

Four key requirements influenced Lendi Group’s decision to choose MongoDB Atlas:

• Managing complexity – the organisation needed the ability to manage complex data structures; Lendi Group chose a ‘document first’ approach to create a unified schema strategy that standardises data contracts across domains.

• AI native features – MongoDB’s flexibility, coupled with built-in AI features like MongoDB Vector Search, enabled Lendi Group to rapidly prototype and iterate on the development of AI applications—all without having to introduce the complexity of a separate vector database.

• Scalability – While Lendi Group already operates at scale, it has ambitious growth plans. MongoDB’s native horizontal sharding enabled Lendi to scale without creating an operational burden and to adapt to the massive data growth anticipated from the future expansion of its AI capabilities.

• Security & compliance – MongoDB provides Lendi Group with critical, built-in security. This enables the company to build an AI business that is secure and compliant by design. A continuous audit trail, which is baked into the database layer, ensures Lendi Group maintains complete lineage and accountability, supporting governance controls, traceability, and regulatory compliance requirements.

40% increase in developer velocity and set-up for an AI-native future

By consolidating its fragmented data infrastructure onto a unified ODL powered by MongoDB Atlas, Lendi Group has built the foundation for its strategic shift from a human-motion to an agentic-motion business. With MongoDB, Lendi Group can now automate the time-consuming, low-value parts of the mortgage process such as document checks, follow-ups, and rate monitoring. By using AI agents, human brokers are freed up to dedicate their expertise to the high-value, relationship-heavy work of complex structuring and customer guidance.

The ODL is helping Lendi Group’s developers bring new features to life 40% faster than with the legacy data architecture. The first example of this speed of AI innovation was Lendi Guardian. This was built and delivered in a focused 12-week development cycle.

“MongoDB has given us operational simplicity and incredible developer velocity for AI features. The successful launch of Lendi Guardian demonstrates the speed and quality of what we’re able to do now,” said Devesh Maheshwari, Chief Technology Officer for Lendi Group.

Looking ahead, Lendi Group is using its MongoDB-powered ODL to not only become AI-native, but also to define what that means for financial services. The company is moving beyond simple AI-automation to create an “elastic workforce” where AI agents handle routine processes. This frees up human brokers to focus on high-value empathy, complex structuring, and trust, transforming the industry’s economic model.