Building the dream team for an AI startup
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.
AI
7 posts
Even ETL needs a roadmap
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.
Revolutionising diagnosis: How large language models can drive a 10x change
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.
Do we even need a moat?
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.
A company's first steps in AI
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.
The fate of being too early
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.
Integrating AI into your engineering team
How to integrate AI into your engineering team. This guide explains how AI can make your team more productive.