Accelerating Innovation in Home Loans

The Challenge

In the competitive Australian home loan market, customer experience and continuous innovation are critical to uno Home Loans' success. uno is an online mortgage broker focused on helping customers find and stay on the best value home loan, disrupting the industry by putting the customer first and using data to discover savings opportunities.

Technology moves fast, and in a small team it is hard to maintain expertise across every area. While hosting exclusively in AWS, uno did not have all the skills needed to take full advantage of the cloud's capabilities. They also faced a specific technical challenge in their codebase: the team had adopted a functional programming library that neither uno's in-house developers nor external engineers were fluent in, creating a bottleneck in development velocity and making it harder to onboard new contributors.

uno needed a partner who could plug the skills gaps, increase delivery speed, and help the team make pragmatic technical decisions about their architecture, all without disrupting the pace of product delivery that their competitive position demanded.

The Approach

The first engagement focused on team augmentation. Kablamo engineers embedded within uno's team to evaluate and continue implementation of a core API layer. The team assessed the existing codebase and identified a key technical decision: deprecating the functional programming library that had been creating a bottleneck. Kotlin was retained for the main routing layer due to its lower learning curve, while core logic in services and repositories was migrated to Java to match the team's existing strengths. This gave uno a codebase that any competent Java developer could contribute to, rather than requiring niche functional programming expertise.

The team implemented user management endpoints, built out unit and integration tests as part of the continuous integration pipeline, and wrote acceptance tests that validated the API against real service behaviour. The goal was not just to ship features but to establish patterns that uno's internal team could follow after the augmentation ended. Kablamo delivered a set of recommendations covering areas where manual processes could be replaced with automated pipeline steps, ensuring the team could maintain velocity independently.

The second engagement was a data proof of concept. uno had a large volume of customer phone call recordings but no way to extract structured insights from them. Kablamo used AWS Transcribe to convert call audio to text and AWS Comprehend to analyse the transcripts for sentiment, syntax, and key phrases. The hypothesis was that ML could identify where customers were in their home loan journey based on call content, enabling more personalised and timely follow-up.

The approach followed a structured methodology: investigate and set hypotheses, set up the transcription and analysis pipeline, run hypothesis tests against real call data, and validate results. The proof of concept demonstrated that sentiment analysis and key phrase extraction could meaningfully identify customer journey stages from call recordings.

The Results

The augmentation engagement delivered measurable improvements: increased sprint velocity, fewer bugs reaching production, higher team morale, and reduced risk to the customer experience. The partnership moved beyond a transactional staffing arrangement into a co-invested relationship where uno gained access to a team of engineers they could draw on as needs evolved.

The deprecation of the functional programming library was the most impactful technical decision. It removed a barrier that had been slowing every developer on the team, not just Kablamo's engineers. New contributors could now be productive within days rather than spending weeks learning an unfamiliar paradigm. The automated testing patterns and pipeline improvements established during the engagement continued to deliver value after Kablamo's involvement ended.

The data proof of concept validated that ML-based analysis of customer calls was feasible and useful. AWS Transcribe produced usable transcripts from real call recordings, and Comprehend's sentiment analysis was accurate enough to support the hypothesis that customer journey stage could be inferred from call content. This opened a path for uno to build more personalised customer experiences based on structured call data.

Looking Forward

uno is now better positioned to continue its disruption of Australia's home loan market. The pragmatic codebase decisions, moving from niche functional programming to mainstream Kotlin and Java, mean the team can hire and onboard engineers faster. The automated testing and deployment improvements give the team confidence to ship more frequently without increasing risk to the customer experience.

The data proof of concept laid groundwork for using ML to personalise the home loan journey. With structured call insights, uno can identify customers who may benefit from refinancing, flag accounts where sentiment suggests a service issue, and tailor follow-up communications based on where each customer sits in their loan lifecycle. In a market where the difference between winning and losing a customer often comes down to timing and relevance, the ability to extract structured signals from every customer interaction is a competitive advantage that compounds over time.