Next.js AI app development, tested and costed before launch

Yes. We work inside your current repo, auth, database and deploy setup, through pull requests your team reviews, so the feature lives where the rest of your product lives. If the app runs an older Next.js version, we check early whether the feature needs an upgrade and, if it does, scope the upgrade in. If the feature started as a v0 prototype, we move it into your codebase rather than running it on the side.

Yes, for AI features inside a web product. Route handlers and server functions keep model calls and API keys on the server, answers stream to the screen as they are written, and the Vercel AI SDK gives one interface across OpenAI, Anthropic and other providers. It is widely used: 20.8% of respondents in the Stack Overflow 2025 Developer Survey use Next.js. Heavy machine learning, such as training your own models, fits better in a separate service. For AI work outside a Next.js product, see our AI development services.

The one that passes your eval set at a running cost you accept. In the pilot we run the same questions through more than one OpenAI or Claude model and compare quality, speed and cost per conversation; simple steps often suit a smaller model. Because we build with the Vercel AI SDK, switching later means a configuration change, prompt tweaks where needed and an eval re-run. A fallback model can cover a provider outage. Before any real data is sent, we go through each provider's data settings with you.

Both. An agent calls your product's own APIs as tools, and a person approves any step that is hard to undo, such as a refund or a data change. We also say when a fixed workflow would do the job instead: Gartner predicted in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027. Agent runs longer than your hosting allows move to a background job.

We instruct it to answer only from your documents, show its sources and say it does not know when nothing relevant is found. Nobody can promise zero wrong answers, so we measure them: evals check your real questions against the expected source before launch and on every change. When the model is unsure, or a person owns the decision, the case goes to someone on your team. In McKinsey's State of AI trust in 2026, 74% of respondents called inaccuracy a highly relevant AI risk.

Price is agreed on the free call, once the feature is scoped. We do not publish prices, because a chat assistant over clean documents and an agent across five internal APIs are different jobs. You start with a paid pilot with one agreed deliverable, not a full build. Running cost is separate: conversations × tokens per conversation × model price. Our token counter, prompt caching calculator and RAG cost calculator give rough numbers now; the pilot gives a figure at your volume.

It depends on the feature and the state of your data, so length is agreed on the call; a pilot usually takes CONFIRM: typical pilot length. The order does not change: a paid pilot on your real data that ends in a one-page readout, then the build through pull requests with a demo and a written update every week, then handover with docs, runbooks and training. Real data comes early, because ten hand-picked demo questions say little about the thousandth real one.

You own the code, the IP and the docs, including prompts and eval sets, all in your repo; NDA on request. After handover your team runs it, or we agree upkeep on the call: CONFIRM: patching commitment. Upkeep matters here: npm shows AI SDK versions 5, 6 and 7 shipped between July 2025 and June 2026, and a critical React Server Components flaw (CVE-2025-55182), disclosed in December 2025, hit Next.js 15 and 16 App Router apps until patched.