AI has gone from a buzzword to something you can genuinely add to your product in weeks — often at surprisingly low cost. But "add AI" means different things, so let's cut through the hype and look at what's actually useful and realistic.
The most useful AI features (that pay off)
- A smart chat assistant — answers customer questions using your content (docs, FAQs, product info), 24/7.
- Smart search (RAG) — instead of keyword search, users ask a question and get a real answer from your knowledge base.
- Document & data processing — extract, summarise or classify invoices, forms, emails and reports automatically.
- Content & workflow automation — draft replies, tag tickets, generate summaries, or trigger actions from natural language.
How it actually works
Most modern AI features are built on large language model (LLM) APIs like OpenAI's GPT or Anthropic's Claude. Your app sends a request, the model responds, and your code turns that into a useful feature. For answering from your own content, we use a technique called RAG (retrieval-augmented generation) — the AI is grounded in your data, so it stays accurate and on-brand.
What it costs
Two kinds of cost: build (one-time engineering to integrate it) and usage (a per-request fee to the AI provider, usually cents). A focused feature — like a documentation chatbot — can often be built quickly and runs for a modest monthly cost. The key is scoping it to something that delivers clear value rather than "AI for the sake of it."
Start small, prove value
The smart approach is to pick one high-impact use case, ship it, measure it, then expand. That keeps cost and risk low while you learn what your users actually want.
I build these AI features into real products — see AI & LLM Engineering under services, or tell me what you'd like to automate and I'll suggest the most valuable place to start.