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Practical perspectives on AI, data strategy, and building technology that works in the real world—especially when that world has unreliable connectivity and tight budgets.

Why your AI project keeps failing (and how to fix it)

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AI Strategy•December 2024•8 min read

Why your AI project keeps failing (and how to fix it)

It's not the technology. It's the gap between what AI demos promise and what your actual workflow needs. Here's a framework for projects that ship.

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Data Engineering•November 2024

Data pipelines for teams of one

You don't need Airflow. You might not even need a database. Here's what you actually need when you're building data infrastructure solo.

6 min readRead →

Conservation Tech•November 2024

Field data collection that works offline

Lessons from building systems for game reserves with spotty (or no) connectivity. Offline-first isn't just nice to have—it's essential.

10 min readRead →

African Tech•October 2024

AI for African SMEs: Starting with what you have

Limited budget, unreliable power, scarce talent. Sound familiar? Here's how South African businesses can leverage AI without importing Silicon Valley solutions.

7 min readRead →

Data Strategy•October 2024

The 'build vs buy' framework that actually works

Stop defaulting to 'let's build our own.' A practical decision framework for data and AI tools that considers total cost of ownership.

5 min readRead →

AI & LLMs•September 2024

LLM integration beyond the chatbot

Everyone's building ChatGPT wrappers. Here's how to actually use large language models for automation, analysis, and internal tools.

9 min readRead →

Conservation Tech•September 2024

Conservation data: From field notes to funder reports

How automated data pipelines are changing the way NGOs demonstrate impact. A case study from the Lowveld.

12 min readRead →

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