In this session, I'll demonstrate how to build Python & AI-powered Streamlit apps, enabling you to create fast and effective automations that dramatically reduce manual work.

No prior knowledge needed — just clear intent, a little Python, and an LLM API key.

You’ll leave knowing how to:

✅ Slash dev time (and costs) on internal tools
✅ Democratise automation across your team
✅ Stay agile in a world where “build fast” beats “build perfect”

I’ll walk through real-world examples:

Alt-Text Wizard: a Python-based solution for scalable, AI-powered alt text generation

URL matchmaker: an app to automate redirect mapping for migrations and 404s

AI can now execute an increasing amount of SEO work. The harder problem is deciding whether the result is actually good enough to trust.

SEO teams constantly use concepts such as “high-quality content”, “strong intent alignment”, “relevant internal links” or “a successful content refresh”. Experienced practitioners can often recognise these things instinctively, but turning that judgment into criteria another person or an AI system can apply consistently is much harder.

This is a problem we’ve had to solve while operating AI-powered content and SEO workflows at scale. Our fact-checking process alone runs across more than 2,500 articles a month, and we’ve learned that evaluating an output often requires more than checking whether an individual fact or instruction is correct. A change in one place can affect conclusions elsewhere, and uncertainty needs somewhere to go.

In this session, I’ll show how we approach turning expert judgment into repeatable quality checks. We’ll look at where deterministic rules work, where evidence is required, where AI-assisted evaluation is useful, how we define pass and fail conditions, and when uncertainty should trigger human review.