Why 2 weeks matters
The biggest risk in AI product development isn’t technical — it’s building something nobody wants. Two weeks is enough time to build a functional prototype, put it in front of real users, and learn whether your hypothesis has legs. It’s not enough time to build a production system, and that’s the point — you’re investing minimally to learn maximally.
We’ve launched over 100 products, and the pattern is consistent: the projects that start with rapid validation succeed more often than those that spend months perfecting a plan. GroupDoLists started as a note-taking app inspired by Evernote, pivoted to to-do lists, pivoted again to incident management, and finally found product-market fit. If we’d spent six months building the first version perfectly, we’d have built the wrong thing perfectly.
Choosing the right prototype approach
For a prompt-only prototype, you don’t need custom development at all. Build a well-crafted system prompt, wrap it in a simple UI (Streamlit, Gradio, or even a shared Claude/ChatGPT conversation with specific instructions), and test with users. Timeline: 2–3 days. Cost: near zero. This validates whether users want AI assistance for this task at all.
For a RAG prototype, you ingest your core content (even a subset — 100 documents is enough), build a basic retrieval pipeline, and create a simple search interface. This validates whether AI can produce useful results from your specific data. Timeline: 1–2 weeks. Cost: $10K–$20K with an experienced team.
For a workflow prototype, you build a functional agent that executes a specific multi-step task — contract review, document processing, research workflow. This validates not just whether AI can help, but whether it can handle the full workflow. Timeline: 2–3 weeks. Cost: $15K–$30K.
What the prototype should prove
Define your success criteria before building. The prototype should answer specific questions: do users actually want this? (Measure engagement, repeat usage, qualitative feedback.) Is the AI accurate enough? (Measure against a test set — even 20 examples is enough for a prototype.) Does this save meaningful time? (Compare time-to-completion with and without the AI.)
Don’t measure perfection. A prototype with 75% accuracy that users love is a stronger signal than a 95%-accuracy system that nobody engages with. Accuracy can be improved. Product-market fit can’t be engineered.
After the prototype
If the prototype validates the hypothesis, the next step is an MVP (minimum viable product) — a production-quality implementation of the core feature with proper error handling, security, and monitoring. This typically takes 4–8 weeks and costs $30K–$80K, depending on complexity. For rates and stage-by-stage prices, see how much custom AI development costs in 2026.
If the prototype invalidates the hypothesis, you’ve spent $15K–$30K and 2 weeks instead of $100K and 3 months. That’s a successful outcome — you’ve learned something valuable cheaply.
If the prototype partially validates — users want the capability but not in the form you built — iterate on the prototype before committing to an MVP. This is the most common outcome and the most valuable one. It’s exactly what happened with GroupDoLists: multiple iterations of the hypothesis before finding the right product.
The most important thing I’ve learned from launching 100+ products is that the first idea is almost never the right idea. But the first idea, tested quickly and honestly, leads to the second idea, which leads to the third. The companies that succeed are the ones willing to give up hypotheses that haven’t been confirmed and formulate new ones.”
How we run it: the prototype sprint
Our version of this is the prototype sprint: a fixed scope agreed up front, then a working, deployable prototype on your documents and systems in 5 business days, for a fixed $20k. What you get: a functional prototype that runs on your data, accuracy measurements, and a clear recommendation on whether and how to proceed.
That leaves the second week for what the prototype is for: put it in front of 3–5 real users, measure it against the success criteria you defined, and iterate. If you are not yet sure which feature to prototype, start with the AI opportunity audit: 2 weeks, $5–10k fixed.
Have an AI product idea you want to validate? Contact us — we’ll scope a fixed-price prototype sprint (5 business days) and help you decide whether to invest further.