Artanis #13: First SaaS customer (again)
Helping companies build AI that actually works. Previous updates at https://artanis.ai
🙋 Ways You Can Help - Advice on Consulting → SaaS 🙋
We’re starting to move from a pure services business to SaaS. This has thrown up some challenges, both strategic and operational. Connecting with founders who’ve transitioned from services to SaaS for advice calls would be helpful.
📉 Progress in April - first SaaS customer 📈
Our metrics from April are…
SaaS customers: 1 (+1)
Total customers: 5 (-2)
Monthly Revenue: £72k (+£10.5k)
Team Size (proxy for cost): 5 (+1)
Our main metric has changed to customers on our SaaS platform. We went from 0 to 1 in April, with a first customer now using Artanis to write Policy and label their data! As a reminder, here’s our post on how Policy is the missing core of the AI stack. We still deliver the rest of the AI stack as a service, but it’s much smoother when customers can label their data without us.
The strong revenue numbers (technically a $1m run rate!) include wraparound service. We started with an enterprise customer that requires more integration work, so we needed to expand our capacity with a contractor to support this. It’s not clear yet whether these revenues are recurring, although it’s a good sign!
We lost a couple of customers in April, one due to their financial situation, and the other due to changes in their roadmap. We couldn't see a path for them becoming SaaS customers in the near term, so we’re not concerned, and it has enabled us to narrow our focus.
🔬 New posts in How to Build AI that Actually Works 🔬
We’ve written new posts on:
How to build AI when the ground truth is subjective. Labelling data is often the hardest part of creating an AI product. The solution to this is treating both Policy and labelling as an iterative process called the Policy-Data Loop.
Don’t write prompts. Write Policies. Prompt engineering is arguably a workaround for the current shortcomings of foundation models. As they improve, prompts will become less important. Instead, the enduring IP of your AI will come from the Policies that you write.
🤔 Challenges - aligning Sprint planning with customers 🤔
We’ve found it hard to align our Sprint planning with customer Sprints. We try to do our weekly planning first thing on Monday. However, this is out of sync with customer Sprints, which can often be later in the week. It would be helpful to hear from anyone who’s faced a similar challenge!
🏹 Goal for May - continue transition to SaaS 🏹
Our main goal is to get more customers onto our AI platform. In May, we’d like to go from 1 to 2. Pretty simple in theory!
In practice, not all of our current customers will be a good fit for this, as a lack of high-quality labelled data needs to be block their AI projects. Some are still early in the development cycle, or other problems are more pressing.
🙏 Shout-outs 🙏
Special thanks for April go to:
Christine F - for the opportunity to speak at Experian
Lloyd H - for the intro to Dimitris
Greta A - for the invite to teach the new Balderton Launched cohort about AI
Shabana - for helping out at short notice
Stephen W - for helpful perspective over lunch
Dom/Hannah/Tom - for being the ideal development partner on the new platform!
Srecko D - for reminding us to be patient
Ibraheem R and Alamin S - for hosting and inviting us to speak about founder life
Oliver W and Mamal A for swapping war stories
Benji F for advice on product tooling
En Taro Tassadar,
Artanis Team