How We Built an AI Feature in 6 Weeks: A Real Project Breakdown

Six weeks. That is what a founder gave us to go from concept to App Store. The ask was straightforward: take an existing iOS app, add a meaningful AI feature, and ship something users would actually pay for. No whiteboard experiments, no “phase two” promises — a real, live, working feature in production.

This post is a breakdown of exactly how we did it. If you are a founder trying to build an AI feature fast MVP-style, this timeline, these decisions, and these tradeoffs are relevant to your project right now.

Week 1: Discovery — Getting Specific About the Problem

The biggest mistake teams make with AI features is jumping to the solution before the problem is sharp. We spent the first week doing nothing but asking uncomfortable questions.

What user behavior were we actually trying to change? Where did the current app create friction? What would a user need to do differently after this feature shipped for it to count as a success?

By day 3 we had a one-sentence problem statement: “Users abandon the app because they don’t know what to do next — the AI feature needs to tell them.” That clarity is everything. It ruled out a dozen appealing-but-wrong directions: a chatbot, a search upgrade, a recommendation carousel. None of those solved the stated problem as directly as a context-aware next-step assistant.

Discovery outputs we required before moving on:

A single “job to be done” sentence the AI feature must fulfill
Two or three user scenarios written in plain language (no wireframes yet)
A list of what the AI must NOT do in v1 — this is often more valuable than the feature list
A rough signal on data availability: what does the model need to know, and is that data already in the app?

Discovery is also when we size the work. A feature that requires fine-tuning a custom model is a different project than one that wraps a hosted LLM API with a well-structured prompt. In this case, the answer was clear: use a hosted API, ground it with the user’s in-app data via a lightweight RAG pattern, ship fast.

Week 2: Scoping and Architecture Decisions

With the problem locked, week two was about making the decisions that would determine whether we hit six weeks or twelve.

The three decisions that mattered most:

On-device vs. cloud inference. On-device ML (Core ML, Apple Intelligence) is powerful but adds integration complexity and limits model capability. For a feature that needed nuanced reasoning over variable user data, cloud LLM was the right call. On-device made sense for the latency-sensitive parts — local embeddings and caching.
Build vs. integrate. We did not build a model. We integrated one. There is no business reason to train from scratch when OpenAI, Anthropic, and Google offer production-grade APIs with function calling, context windows large enough for the data we had, and per-token pricing that made the unit economics work.
Scope discipline. We wrote a one-page scope document with a strict “in / out / later” table. Every item that was not directly tied to the core job-to-be-done went into “later.” This is the single most important discipline in a fast build. Founders often want to add a settings panel, a feedback loop, a secondary use case. Not in week six.

By end of week two we had: a confirmed API provider, a data model for how user context would be passed to the LLM, a UI skeleton, and a shared definition of “done” that both sides agreed on.

Week 3–4: The Build

This is where the timeline lives or dies. Two parallel tracks ran simultaneously:

Track A — Backend and AI layer:

Prompt engineering and iteration (more time-consuming than most people expect)
Context assembly: pulling the right user data and structuring it for the model
API integration, error handling, and rate-limit management
Basic evals: a small set of test cases to catch regressions as the prompts evolved

Track B — iOS frontend:

SwiftUI views wired to the new API endpoints
Loading states, error states, and empty states (these take longer than the happy path)
Streaming response handling so the UI felt responsive, not frozen

The trickiest part was prompt engineering. The first version of the prompt produced answers that were technically correct but tonally wrong for the app’s audience. We iterated through six prompt versions over three days before locking one that felt native to the product. This is normal — budget for it.

By end of week four, the core feature was functional in a test build. Not polished. Functional.

Week 5: QA, Edge Cases, and Cost Reality-Check

A working demo and a shippable feature are not the same thing. Week five was about the gap between them.

What we tested:

Edge cases in user data (empty states, corrupted inputs, users who had just signed up and had no history)
LLM failure modes: hallucinated responses, off-topic outputs, refusals
Latency under real network conditions
Cost per user interaction — at scale, a few extra tokens per call adds up fast

The cost audit is often skipped in demo builds. We ran projections at 1,000, 10,000, and 100,000 monthly active users. That number has to be acceptable before launch. If it is not, you need to redesign the feature, not the pricing.

Monthly Active Users	AI calls/user/month	Estimated API cost
1,000	20	~$80–$150
10,000	20	~$800–$1,500
100,000	20	~$8,000–$15,000

These are rough estimates — actual cost depends heavily on the model, token count, and caching strategy. Caching repeated context chunks cut our cost by roughly 30% in this project.

Week 6: Launch Prep and Submission

The final week was less about code and more about the ship checklist.

What went into launch week:

App Store review preparation (AI features get extra scrutiny — data usage disclosures, privacy labels)
In-app onboarding for the new feature (users need to understand what the AI can and cannot do)
A rollout plan: staged release to 10% of users first, not a full launch
Analytics instrumentation so we could measure engagement and failure rates from day one

The app was submitted on day 40. It cleared App Store review in two days. The staged rollout went live on day 43.

What Made This Timeline Possible

Speed does not come from working faster. It comes from eliminating the things that slow you down.

No meetings without decisions. Every call had an agenda and an output.
One decision-maker on the client side. Approval chains that involve three stakeholders double your timeline.
Scope discipline held under pressure. Three good ideas were parked for v2. None of them made it into the six-week build.
We had done this before. Experience with iOS, LLM integration, and App Store submission processes means we do not rediscover the same problems. You can see more of what we have shipped at /#work.

What a Project Like This Actually Costs

A six-week AI feature build at a boutique studio like ours sits in the $15,000–$45,000 range depending on complexity — that maps to our standard-tier pricing. A simpler wrapper feature (less custom logic, simpler UI) can come in under that. A feature with on-device ML, custom fine-tuning, or real-time data pipelines pushes toward $45,000 and above.

Hourly rates at boutique studios typically run $60–$120. If you are comparing to a large agency, expect $150–$250/hour for comparable work. That difference compounds quickly on a multi-week engagement.

See our services for a clearer picture of how we scope and price AI feature work.

Common Questions

How do I know if my idea can be built in 6 weeks? The honest answer: it depends on scope discipline. A focused feature with a clear job to be done, no custom model training, and a single platform target can almost always be scoped to six weeks. Features that require data infrastructure work, custom ML pipelines, or multi-platform support take longer. We tell you this in discovery, not after week four.

What if the AI outputs are wrong or embarrassing? Every AI feature needs guardrails — explicit prompt constraints, output validation, and graceful fallbacks when the model produces something off. This is not optional. We build these into the architecture in week two, not as an afterthought. A failed AI response that surfaces a clean error message is far better than a hallucinated answer your users screenshot and post.

Do I own the AI feature, or am I locked into your vendor choices? You own everything we build. We pick API vendors that have no lock-in clauses and are easy to swap. If OpenAI raises prices next year, you can migrate the integration without rebuilding the product.

Ready to Move Fast?

If you have an app, a product idea, or an AI feature you have been putting off because it feels overwhelming — it does not have to be. We have done this repeatedly, across different product categories, for clients in the US, Europe, and Central Asia. We also apply this same discipline to our own products, including Launchcast and Clove AI.

The fastest way to find out if your idea is buildable in six weeks is a single conversation. Reach out at /#contact — we will tell you honestly what is possible, and what to park for later.

More posts on AI product strategy on the blog.

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