Kiandra Insights

At Kiandra, we work closely with Product Owners to bridge the gap between their organisation’s needs and our delivery team’s technical expertise. This collaboration is crucial for keeping the project aligned to business goals, managing scope effectively, and ensuring value is delivered.

“How do we make sure our AI systems behave responsibly, not just accurately?” We get this question a lot. Usually after something has already gone a bit sideways. Here is the short answer: You build responsibility into AI from the very beginning. Guided by our B-Corp principles, we see responsible AI as a balance of purpose and effectiveness.

When working with clients in the earliest stages of a project, speed matters. The faster we can turn ideas into something visual, the sooner we can test assumptions, get feedback, and align on a direction. That’s where product ideation tools like Lovable come in.

AI is reshaping how software is built, used and maintained but most organisations aren’t starting from scratch. They’re working with what they already have: legacy platforms, off-the-shelf SaaS, or custom tools that still perform core business functions.

When projects succeed, it’s rarely by accident. Clear, accurate requirements are the foundation of great software but they’re also the part most prone to misunderstanding. At Kiandra, we use AI prototyping and a multiple LLM workflow to make this step faster, more accurate, and more collaborative.

Travel businesses need systems that do the work with faster quotes, smarter pricing, and better traveller experiences. We’ve mapped the common travel challenges in the industry, and exactly how AI can solve them.

AI isn’t some futuristic toy we’re tinkering with on the side. It’s already woven into the way we get work done at Kiandra. Whether it's helping sift through mountains of invoices or modernising stubborn legacy code, we’re using AI to tackle the headaches that come with real-world software delivery.

In transport and logistics, most operators don’t need to be sold on the benefits of AI, they just need a clear path to making it work. This blog post outlines the most common challenges we see across logistics businesses, and how AI, when implemented correctly, can solve them and improve performance.

As a Software Delivery Team Lead, I’ve experienced firsthand how our strength lies in pairing deep technical expertise with a culture that genuinely values people. We’re not here to chase hype. We’re here to build things that matter, with teams that are empowered, curious, and supported.