AI Product Development

Our process is designed to manage the unique risks of AI development:• Strategy & Feasibility: We define the business case and test the core AI hypothesis.• Prototyping & UX Design: We design the user interface and how it will interact with the AI.• MVP Development: We build the core product around the validated AI model.• Launch & Learn: We release the product to initial users and gather data.• Iterate: We use the new data to retrain the model and improve the product.

This is a specialized skill called "AI UX." We design interfaces that build trust and manage user expectations. This can include showing confidence scores for a prediction, making it easy for users to provide feedback to correct the AI, and designing graceful "fallback" states for when the AI is not confident in its results.

The success of your product is entirely dependent on having a sufficient quantity of high-quality, relevant data. The specific data needed depends on the product's goal. For a product that predicts customer behavior, we would need a large dataset of historical customer behaviors. The data discovery phase is where we identify what's needed.

You do. Our role is to be your development partner. Your company retains 100% ownership of the final software product and, most importantly, the custom-trained machine learning models and all associated intellectual property.

We measure success on two levels. Technically, we measure the performance of the AI model itself (e.g., its accuracy). From a business perspective, we measure the product's impact on the key metrics it was designed to influence—such as user retention, operational efficiency, or the creation of a new revenue stream.

An AI product requires a more diverse team than a standard app. A typical team includes a Product Strategist, a UX/UI Designer, one or more AI/ML Engineers (for model development), and Software Engineers (for building the user-facing application and infrastructure).

An AI product is a living system. After launch, it begins collecting new data from real users. A crucial part of our long-term partnership is using this new data to periodically retrain and improve the AI model, making the product smarter and more valuable over time. This process is often called MLOps.

Building an AI-powered MVP is a significant R&D effort. The initial Feasibility/PoC phase might take 1-2 months. A full MVP build typically takes an additional 5-9 months, depending on the complexity of both the AI model and the surrounding application.