You shipped the chatbot to take pressure off your support team and give users instant answers. The dashboards looked healthy at first. Sessions were happening. Then you looked closer at what those sessions actually contained, and the picture changed. Most people open the chat, type one or two messages, and leave. Seven out of ten […]
You are two weeks from shipping the AI feature. The model is trained. The endpoint is deployed. The product manager has written the release notes. And then someone asks: “has anyone actually checked whether the rollback plan is documented?” That question, asked two weeks before launch, is manageable. Asked two days after launch when the model starts producing […]
You ran the assessment. You sat down with your team, worked through the questions honestly, and found something you did not expect to find. Maybe it was the data, years of records with no labelling, no outcome signal, nothing a model can learn from. Maybe it was the use case, three people in the room […]
The conversation in your last board meeting probably included the phrase “we need to be doing more with AI.” Your investors are asking about it. Your competitors are announcing it. And somewhere in the middle of all that, you are trying to figure out whether your organisation is actually in a position to build something that works. The honest […]
Somewhere in the last eighteen months, a decision got made in a lot of product teams that sounded reasonable at the time. The question was “should we add AI to this feature?” and the answer was “yes, we should use one of the big language models.” Nobody in the room pushed back. GenAI was what everyone was […]
There is a distinction that most SaaS teams building GenAI features do not make early enough, and it costs them significantly when they discover it in production. The distinction is between users trusting your feature and your feature being trustworthy. A GenAI feature can earn user trust quickly. The outputs sound confident. The interface feels […]
There is a particular kind of product decision that feels unambiguously right at the time. Deploying an AI support chatbot usually falls into that category. Reduce ticket volume, cut response time, free up your human agents for complex issues, improve support coverage to 24/7. The case is easy to make and the demos always look convincing. The problem […]
Shipping an AI feature is not the same as shipping a standard software feature. A standard feature either works or it does not. An AI feature can work perfectly from an engineering standpoint and still produce results that are wrong, expensive, or impossible to explain, without triggering a single error alert. This is why AI systems need a dedicated […]
Shipping a model feels like the hard part. For most SaaS teams, it is the part they prepared for. They ran experiments, evaluated accuracy, tested edge cases, and deployed. The model went live. Nothing caught fire. Then, three months later, something is quietly wrong. The recommendation feature is surfacing less relevant results. The churn prediction […]
Most SaaS teams celebrate when their model hits target accuracy. They tune hyperparameters, run evaluation passes, review the confusion matrix, and ship. That moment feels like the finish line. It is not. It is the beginning of a completely different set of engineering problems, and most early-stage teams are not set up to handle them. This article […]