Adding AI features without breaking your product
Where model calls belong in the stack, how we handle failure states, and why the boring parts — validation and cost control — matter most.
AI is an ingredient, not the dish
The best AI features feel obvious in hindsight. They solve a specific user problem inside an otherwise normal workflow. The model is hidden behind clear inputs and outputs; the user never thinks about tokens or prompts.
Before adding AI, we ask: what would the user do if the model failed? If the answer is ‘nothing useful’, the feature is too fragile.
Where the model lives
- Server-side for anything involving private data, heavy computation, or cost control.
- Client-side only for low-risk, offline-friendly experiences with small models.
- Never in the critical path of a transaction unless there is a deterministic fallback.
Design for failure
Models are probabilistic. They hallucinate, rate-limit, and occasionally refuse valid requests. We design every AI feature with explicit failure states: cached responses, graceful degradation, and clear error messages that do not blame the user.
We also cap usage per user and per account from day one. Nothing kills a startup faster than an unexpectedly large AI bill.
Validation beats novelty
The flashy demos get attention, but the reliable features keep users. We spend more time on input validation, output parsing, and guardrails than on prompt engineering.
If you cannot explain what the model will do in three bullet points, your users will not trust it either.
AB Technologies Team
Published June 2026