AI app MVP development

The useful distinction for an AI MVP is between using a model and training one. Using a frontier model through an API is fast, cheap and completely viable in a weekend. Training or fine-tuning your own model is a research project measured in months, and no honest developer will tell you otherwise.

Almost every AI product idea worth testing sits in the first category. A tool that reads a contract and flags unusual clauses, a chat assistant grounded in your own documents, something that turns a messy spreadsheet into a clean report, an agent that drafts replies in your voice — all of these are API calls wrapped in a good interface and a well-engineered prompt.

The hard part is rarely the AI. It is the interface, the streaming, the error handling when a model returns nonsense, the cost controls so one user cannot run up a bill, and the evaluation loop that tells you whether the output is actually good. That is where the 48 hours goes.

The three features worth starting with

1. 1

   The AI workflow itself

   Input, a properly engineered prompt, streamed output, and sensible handling for when the model gets it wrong.

2. 2

   Your own data in the loop

   Document upload with text extraction, or retrieval over your content, so the answers are grounded in something real.

3. 3

   Usage limits and cost control

   Per-user caps and logging, so a single enthusiastic user cannot generate a four-figure API bill.

A worked example

The mistake to avoid

Shipping a bare chat box. A chat box makes the user do all the work of figuring out what to ask. Constrain the interface to the job and the product feels ten times smarter for the same model.

Common questions

Can you build a AI product in 48 hours?

A product built on the OpenAI or Anthropic APIs — chat, document analysis, extraction or generation — working and deployed in 48 hours for a fixed $3,500 AUD.

What features should a AI product MVP start with?

The AI workflow itself: Input, a properly engineered prompt, streamed output, and sensible handling for when the model gets it wrong. Your own data in the loop: Document upload with text extraction, or retrieval over your content, so the answers are grounded in something real. Usage limits and cost control: Per-user caps and logging, so a single enthusiastic user cannot generate a four-figure API bill.

What is left out of a AI product MVP?

Training or fine-tuning a custom model; Multi-step autonomous agents that run unsupervised; Voice products needing sub-second response times (a separate, harder build); Guarantees about model accuracy — you get an evaluation method, not a promise. Each of these is deliberately deferred to a later build rather than half-finished in the first one.

What is the most common mistake when building a AI product?

Shipping a bare chat box. A chat box makes the user do all the work of figuring out what to ask. Constrain the interface to the job and the product feels ten times smarter for the same model.