Ship It Live — $1,999 flat
Design + build + deploy · kickoff in 24h · limited slots
Ship It Live · AI sprint
An AI product people can actually use, live in 30 days.
Wrapping a language model in a chat box is a weekend. Designing an AI product people trust enough to use daily is the actual job — and it is what this month is for.
Quick answer
An AI application can be designed, built and deployed in 30 days from a fixed $2,500. The work is mostly product design rather than model work: deciding what the AI does, how it shows confidence and what happens when it is wrong. The model itself is an API call.
The plan
Day one to day 30, written down.
You get this schedule before you commit, not after. If a phase slips, that is mine to absorb — the price and the date were agreed before anything started.
- 1
Days 1–4
Define the job
What does the AI actually do, and how will we know it did it well? Most AI projects fail here rather than in the engineering.
- 2
Days 5–13
Design for uncertainty
AI outputs are probabilistic, so the interface has to show confidence, cite sources and fail gracefully. This is the design work that separates trusted products from toys.
- 3
Days 14–25
Build
Model integration, streaming, prompt iteration against real examples, and the surrounding product — auth, history, billing.
- 4
Days 26–28
Evaluate and harden
Testing against real inputs, tightening prompts, adding guardrails and capping runaway costs.
- 5
Days 29–30
Launch
Deployed on your domain with usage monitoring, so you can see what people ask for and what it costs you.
What from $2,500 gets you
Everything needed to launch — nothing padding the invoice.
Who books this
Built for
Founders with a genuine workflow to automate, not a demo to build
Businesses sitting on data or documents nobody has time to read
Teams who need something real in front of investors this quarter
Real situations
Five ways this shows up, and what actually fixes it.
Not hypothetical — the shape of the problem before someone books this sprint, and the specific part of the build that resolves it.
Situation 1
A weekend chatbot wrapper, not a trusted product
A founder built a quick demo wrapping a language model in a chat box over a weekend, but it has no memory of what 'good' looks like, no cost controls, and nobody trusts it enough to use daily.
The month goes into product design — confidence states, sources, graceful failure — the difference between a demo and something people return to.
Situation 2
Sitting on documents nobody has time to read
A business has years of internal documents, manuals or client records that answer most support questions, but finding the right one still means asking a person who is busy doing something else.
The product is designed around that real workflow — reading the documents and answering from them — rather than a generic chat interface bolted on top.
Situation 3
Afraid a viral week will bankrupt the API bill
A founder is hesitant to launch an AI feature because a previous side project got unexpected traffic and the model API bill for that month wiped out the margin.
Cost controls — per-user limits, caching, smaller models for smaller tasks — are part of the build, with usage monitoring so a spike is visible before it's an invoice.
Situation 4
Needs something real in front of investors this quarter
A team has been talking about 'the AI feature' in every investor update for two quarters without shipping anything an investor could actually try.
By day 30 there's a deployed product on their own domain with real model integration — something to demo, not describe.
Situation 5
The AI is confidently wrong and nobody designed for that
An early AI feature gives answers with total confidence even when it's guessing, and users have started distrusting it after a few visibly wrong answers.
Interface patterns for uncertainty — shown confidence, cited sources, honest empty states — are built in specifically because a model that admits doubt gets trusted more than one that fakes certainty.
The honest part
When 30 days is the wrong answer.
A fixed deadline only works when the scope genuinely fits inside it. These are the cases where I will tell you so rather than take the booking.
Projects needing a custom-trained or fine-tuned model — that is research with a research timeline, not a 30-day build.
Ideas where the AI is decoration on a product that would be better without it. I will say so rather than take the work.
Anything requiring regulatory approval for automated decisions, which is a legal timeline before it is a technical one.
Inside the 30 days
- Product design for AI-specific states — loading, confidence, failure
- Model integration with Claude, GPT or Gemini, whichever fits
- Prompt design and evaluation, so quality is measured not guessed
- Streaming responses and the interface patterns that make waiting bearable
- Sensible cost controls so a viral week does not bankrupt you
- Auth, billing and deployment on your domain
- Source code and design files yours at handover
- 30 days of post-launch support, included
Quoted as one number before day one. A week running long is mine to absorb.
Outside the line
- Projects needing a custom-trained or fine-tuned model — that is research with a research timeline, not a 30-day build.
- Ideas where the AI is decoration on a product that would be better without it. I will say so rather than take the work.
- Anything requiring regulatory approval for automated decisions, which is a legal timeline before it is a technical one.
Real work, sized and priced separately. You hear it before you book, not at handover.
Questions
Before you book
Yes, because the model is an API call rather than something you build. The month goes on product design — what the AI does, how it shows confidence, what happens when it is wrong — plus the surrounding product. Fine-tuning or training a custom model is a different project entirely.
The eval is the product (not the chat box)
Wrapping a language model in a chat box is already called a weekend on this page. The 30-day sprint is a product people can use daily: one AI-shaped job, a way to tell good from bad, and an interface that does not fake certainty.
| Lock this | Or it is not this sprint |
|---|---|
| One job the model is hired to do | “ChatGPT wrapper” with no product boundary |
| A small eval set you can actually score | “It feels smart” as the only test |
| Failure UX (confidence, sources, correction) | Happy-path demo with perfect prompts |
| Cost controls on keys you own | Unlimited calls on a shared contractor account |
Kill criteria (examples): custom-trained or fine-tuned model as day one; automated decisions that need regulatory approval; the AI as decoration on a product that would be better without it — this page already says no to those.
If the real product is a paid workflow and the model is optional garnish, use /shipitlive/saas-mvp-in-30-days. If the job is an approval-loop agent that acts in your tools, use /shipitlive/ai-agent-in-21-days.
Failure UX is a shipping gate
The live FAQ already says we design for the model being wrong rather than hoping. Treat that as a gate, not a vibe.
Must be true before launch week
- Primary job has empty, loading, and error states — not only a streaming happy path
- Uncertainty is visible where it exists (confidence, caveats, or “I don’t know”)
- Sources shown when the product is answering from your documents
- A correction / retry path a human can use without a Slack thread to us
- Eval examples from real inputs — not only the founder’s favourite prompt
Soft UX only. No accuracy percentages, hallucination rates, or “never wrong” language belong on this page. For the longer pattern set, see designing trustworthy AI interfaces and beyond the chat box.
Keys, cost, and training (process — not a vendor ad)
Model usage is already billed to your own API account on this page. That is the ownership rule, not a pricing table.
| You own | We do not |
|---|---|
| Provider API keys on your account (bring-your-own) | Keep production keys behind our login after handoff |
| Per-user limits, caching, smaller models where they fit | Invent a monthly API bill for a generic sprint |
| Usage monitoring you can actually open | Quote provider rate-card caps as if they were ours |
| A written default: we do not treat your data as training fuel | Promise a vendor’s “never trains” policy we have not verified for your account |
Provider prices and caps change. Re-check the vendor page before you budget. This expansion does not assert OpenAI, Anthropic, or Google rate cards.
Sprint price on this page is from $2,500 for the 30-day build — model usage stays separate, on your key.
Day 30 is a product handoff, not a model SLA
Day 30 means the product is designed, built, and deployed on your domain with the surrounding product (auth, history, monitoring) in place. It is not a promise that the model never errs, that a provider’s uptime held, or that quality is “done forever.”
Planning rule (not an SLA): evals and cost caps land before launch week so a bad output or a traffic spike is visible. The calendar date is a shipping date for the product — not a service-level agreement on the model.
Sibling sprints (when this slug is wrong)
- Paid workflow, model optional → /shipitlive/saas-mvp-in-30-days
- Bounded agent that acts in your tools → /shipitlive/ai-agent-in-21-days
- Contained bot + handoff → /shipitlive/ai-chatbot-in-21-days
- Phone / voice job → /shipitlive/voice-ai-app-in-30-days
- Prototype already exists → /shipitlive/vibe-coding-to-production
- Map → /shipitlive
Extra questions
Existing FAQ already covers 30-day feasibility, cost, model choice, cost controls, handling wrong answers, and prompt/code ownership.
Who owns the model API keys after day 30?
Does day 30 mean the model is accurate?
Can AI agents replace the design engineer on this sprint?
Other sprints
30 days from now, this could be live.
A 30-minute call decides whether the scope fits. If it doesn't, I'll tell you what would.