Hiring an AI app development company

What applied AI is genuinely good at

Every stack has a shape. Choosing well means matching the shape of the technology to the shape of your product, not to whichever framework your agency happens to staff most cheaply.

Here is where applied AI earns its place.

• Real leverage on document-heavy, triage-heavy and search-heavy workflows
• On-device models (Core ML, ML Kit) keep sensitive data off the network
• Retrieval over your own content beats generic model knowledge every time
• Measurable outcomes when you instrument the workflow, not the model

The honest limitations

No agency volunteers this list, which is exactly why you should ask for it. If a AI app development company cannot describe the downsides of its own recommendation in specific terms, it is selling capacity rather than judgement.

• Non-deterministic output needs evaluation infrastructure, or you cannot ship safely
• Token cost scales with usage and can quietly destroy unit economics
• Latency budgets are tight on mobile — streaming and caching are mandatory

When to pick it — and when not to

Use AI where a human currently reads, sorts, summarises, matches or drafts something repetitive, and where a wrong answer is recoverable rather than catastrophic.

Do not put a generative feature on a path where an incorrect output causes clinical, legal or financial harm without a human in the loop and a full audit trail.

What a healthy codebase looks like

Our applied AI baseline: a golden dataset and an automated evaluation run on every prompt or model change, retrieval over your own content with citations, structured output validated against a schema before it touches the UI, prompt-injection defences on anything reading untrusted text, per-user and per-tenant rate and spend limits, streaming responses with an interruptible UI, and full logging of inputs, outputs and cost so you can answer 'why did it say that' six weeks later.

Ask to see a repository structure, a pull request, and a CI pipeline before you sign. Ten minutes reading real code tells you more than a two-hour capabilities deck.

What it costs

An AI feature inside an existing product usually starts around $35,000 to $90,000 including evaluation infrastructure. An AI-native product is a full build. Budget monthly inference cost as a line item from day one.

Cost is driven by four things in roughly this order: the number of integrations, the depth of the backend, the amount of custom interface, and the compliance regime. Screen count is a weak predictor. Any estimate produced without a conversation about those four inputs is a number, not an estimate.

• Discovery & product strategy — 2 to 3 weeks
• Design system and core flows — 3 to 5 weeks
• Build and QA — 8 to 14 weeks for a substantial v1
• Store submission, launch monitoring and iteration — ongoing

How to evaluate a AI app development company

Use this as a checklist in the first call. Every item is answerable in a sentence by a team that has actually shipped, and evasive for a team that has not.

• Ask to see their evaluation harness, not their demo
• A stated position on data retention, training opt-out and PII handling
• Cost-per-request modelling before a line of code is written
• Fallback behaviour when the model is slow, wrong or unavailable
• Someone who will tell you when the answer is a database query, not a model

Ownership terms that protect you

Whatever stack you choose, the contract matters as much as the code. Your company should own the repository from the first commit, hold its own Apple Developer and Google Play accounts, own the cloud tenancy and the domain, and receive a documented handover at the end of every engagement.

If a firm resists any of those, the technology discussion is irrelevant — you are being asked to rent your own product.

• Repository owned by your organisation from commit one
• Store and cloud accounts in your company's name
• Design source files delivered, not screenshots
• Written exit and handover terms in the master agreement

Working with WVE Labs

WVE Labs is a digital product company founded in 2015. Product strategy, design and engineering sit under one roof, and mobile has been at the heart of the studio for more than a decade — it remains one of our deepest areas of expertise. We have delivered for startups, growth companies and established organisations including Sony, Honda, Guardian, Marriott, USC, Maui Jim and California State University. Engagements start at $25,000.

We recommend by fit, not by preference — native Swift and Kotlin, React Native, Flutter and progressive web apps all live in our portfolio, and the recommendation follows the product. You get a small senior team, weekly demos on a live build, and named engineers in your repository from sprint one.