To add a useful AI feature to your SaaS, start from a user problem that wastes time or money, not from a desire to "add some AI". A good first feature automates a repetitive task, relies on an existing API rather than a home-grown model, and is measured against one clear number. Here is how to do it, step by step.
1. Identify the right use case
An AI feature is only worth it if it solves a real pain point. Look at where your users spend time on manual, repetitive tasks: that is where AI delivers the most value. Avoid the gimmick that dazzles in a demo but that nobody uses twice.
- Automatic summaries of long content (tickets, meeting notes, documents).
- Smart classification or sorting (leads, emails, support tickets).
- First-draft generation (replies, descriptions, email drafts).
- Semantic search and Q&A over your own data.
2. Custom model or API? Almost always the API
For a first feature, training your own model is rarely the right call: it is expensive, slow, and you do not yet have the data to justify it. A provider API (OpenAI, Anthropic, Mistral…) gets you to production in a few days for a few cents per request. Only consider a specialized model once the use case is validated and the volume is there.
The best first AI feature is not the most impressive one: it is the one that saves measurable time from the very first week.
3. Control costs and latency
Two traps kill an AI feature in production: a bill that spirals and a response time that frustrates. Both are managed with a few simple habits, set up from the start rather than in a panic.
- Pick the smallest model that does the job: a fast model often costs 10 to 20 times less than a top-tier one.
- Cache responses to recurring requests to avoid paying twice for the same thing.
- Stream the response (token by token) for near-zero perceived latency, even if the full computation takes a few seconds.
- Set a cost ceiling per user per month, and monitor it from day one.
4. Measure value, not usage
The number of clicks on your AI button tells you nothing. What matters is impact: minutes saved per task, 30-day adoption rate, reduced processing time, or retention of users who adopt the feature. Define that number before you code, measure it after launch, and cut without hesitation anything that does not move the needle.
5. Ship fast, iterate later
A first AI feature does not need to be perfect: it needs to be in production, used and measured. That is exactly Khufu's philosophy: a real V1 in 7 days, at a fixed price (€15,000), AI feature included. We wire up the API, control the costs, measure the value — and iterate on facts, not hunches.