Higher-Fidelity Modelling, Delivered Without Slowing Down

The Challenge

A government agency engaged Kablamo for an update to an existing platform that supports operational decision-making for natural disasters such as bushfires, urban fires, and floods. This work centred on integrating new and far more sophisticated predictive models. The leap in fidelity brought a leap in scale: the volume and complexity of data the platform needed to ingest, process, and synchronise grew well beyond what the existing architecture could support.

It was also a brownfield engineering challenge in its own right. Kablamo's engineers needed to get up to speed quickly on a large, complex, pre-existing codebase, understand legacy dependencies, and maintain strict architectural and stylistic consistency with the existing system, all while delivering new capability under time pressure.

The Approach

Kablamo used Claude Code throughout the engagement, not just to build the platform, but to accelerate how the team worked.

Claude Code generated comprehensive architectural documentation, including C4 models, system flow diagrams, and sequence schemas, giving engineers high-fidelity visual assets that clarified legacy dependencies and significantly shortened the knowledge-transfer curve on a complex brownfield system. Existing code patterns and conventions were formalised into bespoke, reusable skills, allowing new components to be generated automatically while remaining rigorously consistent with the established architecture, and Claude Code was integrated directly into the project's development environment and deployed across all repositories, giving every engineer a synchronised, optimised setup.

Claude Code was also used to analyse infrastructure costs and usage patterns for resource-intensive modelling workloads, helping the team pinpoint fiscal bottlenecks and latency issues. Those findings fed into a purpose-built, Claude-powered cost estimation tool that predicts total cloud expenditure from a set of input parameters, breaking down costs across compute, storage, and data provisioning.

The Results

By combining Claude Code with an agentic development workflow, phased execution blueprints, sub-agent-driven parallel development, and recursive sub-agent code review, Kablamo delivered a technically complex, high-fidelity uplift within a compressed timeframe, without compromising quality.

The updated platform gives analysts modelling at a resolution and speed the legacy system couldn't match, putting more reliable data behind decisions that carry real operational consequences.

Looking Forward

This engagement reflects Kablamo's broader work at the intersection of AI-assisted engineering and government platforms, using agentic tooling not only to build critical infrastructure faster, but to make it safer to build.