How to add AI to your app the smart way

Every founder is being told to "add AI." But AI isn't a feature you bolt on — it's a capability you apply to a real problem. Start with the problem, then choose the smallest AI that solves it.

The four levels of AI in apps

• 1. API integration: call a model (OpenAI, Claude, Gemini) for a specific task — summaries, classification, drafting. Fastest and cheapest to ship.
• 2. Chatbots & assistants: conversational support and in-app copilots trained on your content.
• 3. RAG & custom knowledge: "chat with your data" — the model answers using your documents, with citations.
• 4. Agentic AI: autonomous agents that plan and take multi-step actions (with guardrails) — the frontier, and the highest impact.

High-ROI AI use cases

• Customer support automation — deflect repetitive tickets 24/7 (see our SupportCopilot AI case study).
• Content generation — on-brand copy, images and product descriptions at scale.
• Search & discovery — semantic search and personalized recommendations.
• Document processing — extract, summarize and route data from PDFs, emails and forms.
• Analytics & forecasting — predict churn, demand and user behaviour.

Buy vs. build vs. fine-tune

Most products should start with existing models via API — it's fast, cheap and surprisingly capable. Move to RAG when answers must be grounded in your data. Consider fine-tuning or private models only when you have unique data, strict privacy needs, or scale that justifies the cost.

Rule of thumb: don't train a model when a good prompt plus your data (RAG) will do. Complexity is a cost — add it only when it pays for itself.

Pitfalls to avoid

• AI for its own sake — solve a real user problem, not a buzzword.
• No guardrails — add validation, PII redaction and human-in-the-loop for sensitive actions.
• Ignoring cost — token usage adds up; design for caching and efficiency.
• No evaluation — measure accuracy and user satisfaction, and iterate.