AI can be challenging. We break down three AI integration tiers—proprietary models, open-source solutions, and custom-built systems—to help you choose the right approach. From quick MVPs to scalable solutions, discover how to leverage AI effectively for your product.
Building an AI startup demands specialised roles like data scientists, engineers, and analysts to drive innovation. Discover what roles you need to hire first for a strong foundation to success.
ETL (Extract, Transform, Load) is vital for data-heavy businesses but often begins with manual workflows. Companies should identify inefficiencies, prioritise automation, and design a scalable ETL roadmap that integrates human reviews and evolves with business growth.
Large language models (LLMs) transform problem-solving by enabling natural, iterative conversations, ideal for fields like healthcare and legal services. They scale expertise and accessibility but face challenges like reliability and cost.
A technical moat is often seen as a product's defensive edge, but does every product really need one? For AI products, the choice between building proprietary tech or leveraging existing solutions like OpenAI is complex. True value lies in solving customer problems—not just in owning the technology.
Many product companies are eager to leverage tools like ChatGPT. But how do you go from experimenting to running in production? Let's explore choosing the right large language model (LLM) to understand hosting options, ensuring an efficient and sustainable AI implementation.
Strava's new AI-driven Athlete Intelligence brought me back to Addapp, the startup I co-founded nearly a decade ago. We used data from devices like Apple Watch to offer insights, like how your run affected your sleep, using traditional data science. Being ahead of time is not always good.
How to integrate AI into your engineering team. This guide explains how AI can make your team more productive.