Traditional design thinking, which starts with user empathy, may not always be ideal for building AI-native solutions. Instead, we may need to anticipate future needs—sometimes before users even realize them. AI can introduce innovations that change behaviors, and designing around these ‘unknown’ needs requires a balance of empathy and foresight. We recommend starting with rapid prototyping and testing user behavior to iterate on the solution and unlock new possibilities.

That said, to create a product MOAT that sets you apart from competitors, we combine user research and business impact mapping. We must understand your users, observe their behaviors, and visualize how your AI will shift those behaviors. By mapping user journeys, we’ll spot key moments and touchpoints that maximize value and delight. We’ll also define the business success criteria—the KPIs, and targets to ensure your product meets both user and business goals.

This is not just for building new products, but also for improving your existing AI-native products. Using rapid experimentation and validation, we can prioritize features for maximum user value & business impact.

The core principle of AI-native UX/UI design is Adaptiveness—creating dynamic, responsive interfaces that evolve in real-time based on user interactions. Unlike traditional, one-size-fits-all designs, AI-powered UX/UI is not static; it continuously listens and adjusts, optimizing user experiences and becoming more intuitive over time. For instance, instead of a fixed hamburger menu, the most used item could be the first menu.

For AI-native products, tools like Runway ML track heatmaps and session data, enabling highly adaptive user interfaces and experiences. By analyzing user activity, the product can dynamically highlight frequently used features and adjust layouts to reflect individual preferences. We can also monitor interactions and task completion rates, identifying friction points—such as drop-offs or navigation issues—that inform the UI’s dynamic adjustments. For example, if a commonly accessed feature like “settings” is buried in a menu, the UI can automatically reposition it to a more visible location, enhancing usability.

We diagnose critical processes in your business that directly impact KPIs, analyze the size and complexity of your data, and assess whether custom AI models can simplify workflows and achieve desired outcomes. Our goal is to identify areas where AI can predict, automate, and simplify decision-making, all of which are crucial for business growth.

For instance, fraud detection is an area where your business may need improvement. We build a custom AI solution that identifies unusual spending patterns in banking transactions, flagging potential fraud in real time. This reduces financial risks and enhances security, all while ensuring legitimate user activity remains uninterrupted.

Tools like Hugging Face fine-tune LLMs to deliver context-aware recommendations, PyTorch enables deep learning models to predict failures, minimizing business downtime risks, and TensorFlow provides real-time customer sentiment analysis, helping businesses swiftly adjust and refine marketing strategies.

Our approach to AI-native product development uses MLOps to automate the entire model lifecycle—from development to deployment—ensuring scalable, high-performance AI that operates efficiently in production.

Kubeflow orchestrates pipelines, automating data preprocessing, model training, and deployment across multiple environments. MLflow tracks model performance and versioning, ensuring reproducibility and continuous improvement. TensorFlow Serving powers real-time model deployment, delivering low-latency predictions as the product scales.

For example, an AI-driven logistics service continuously analyzes traffic and delivery patterns, dynamically adjusting routes in real time to reduce transit times and fuel costs, ultimately boosting operational efficiency with every delivery.