YourStaffKnow.com (now R-ai) was set up with a very simple purpose: to help democratise decision making in large organisations. In part because that's a good thing in itself, but also because we believed that that leads to better decisions being made.

The core idea was that, often, the people with the most important information in a company are the people in touch with day to day operations, lower down in a hierarchical structure. The people making the major strategic decisions are often at the top, and sometimes they can be insulated from that critical information. They're insulated, partly because in very large organisations, it can be difficult for junior employees to feedback directly to the executive team, and partly because if everyone did feedback, there'd be an enormous, unmanageable, quantity of information to process. The goal was to bypass the social hierarchy by talking to everyone equally and anonymously, and to bypass the information processing bottleneck with automated analysis. The technology I worked on allowed us to talk to every member of an organisation, and then synthesise that into quantitative data and actionable advice, often in the direct words of staff themselves.

The underlying tech focussed on small data rather than big data. My underlying thought was that Shannon information theory is a really powerful tool for understanding quantity of information, but when it comes to language a lot of what's relevant isn't quantity, but meaning. Different bits of information are more important or relevant than other bits, partly because of their relation to us, and partly because of their relation to other bits of information. In this way the work owed more of a conceptual debt to Wittgenstein than to more traditional titans in computer science.

My solution therefore focussed on thoughtful feature encoding so that the semantic relationships between linguistic tokens were captured. This then fed through an interacting set of clustering algorithms and feed-forward neural networks (acting as classifiers). The approach allowed us to get powerful results on very small datasets, and meant we could promise absolute anonymity - data was never kept and fed into an all-hungry LLM - the analysis was local, fast, and bespoke.

I loved the work - it felt like a really interesting problem, and it meant a lot to feel like this process - properly listening to people, made a difference. We were really happy with the impact the company had, we got to work with some fantastic organisations, Danone, B-LAB, USAID, Kraft, and the FCO, and we received some really lovely testimonials. We were credited with helping to achieve 'a growth in turnover of $150M to $1B' by Lorna Davis, then CEO of Kraft China, and of catalysing a 'turnaround to double digit growth' by Thomas Kunz, then global head of Danone Dairy/Danone Waters. It felt like an incredible vindication to see companies benefit so significantly from paying careful attention to the trapped knowledge in their teams, and we're really glad we were able to play a role in emancipating that knowledge.

After 5 years as lead developer, I was appointed as a co-director of the company in 2024. In 2026 I made the difficult decision to exit the company - partly to focus on public benefit applications of the technology and partly to free up time for my crippling addiction to boats!

I still really believe in the R-ai mission, it was a big part of my life for a long time. I keep a friendly toe dipped in the R-ai waters in a consultancy capacity, and I still use some of the principles I developed at the company in my advisory work with the Canal and River Trust and National Historic Ships. If you've got a natural language information processing problem you'd like to discuss please do get in touch! I'll almost always make time for a chat about it.

If you'd like to know more about R-ai you can head to their main site here, if you'd like to read about some of those public benefit applications, have a look at my advisory work. If you'd like to read about some other, more frivolous coding projects, checkout this wordle solver. Or just explore the map below to find other connections. :)