Adding AI features to a Vue or Nuxt app is rarely a model problem. The model is a fetch call. What breaks is everything around it — a UI that freezes while tokens stream, no way to cancel a bad answer, a token bill nobody forecast, and output the team cannot prove is correct. That is frontend engineering, and it is where most AI features quietly stall.
I build AI features inside production Vue 3 and Nuxt applications: in-product copilots, RAG search with citations, agent flows that touch real data, and generation UIs that people use daily. I am doing exactly this on a live SaaS product right now, on top of ten years of frontend architecture work for teams including Adidas, OPAP and About You. Vue AI integration is the specialism; the model provider is an implementation detail.
Concretely, that means: an in-product copilot that can act on the user’s own data, RAG search over your documents with citations users can click, AI chat that is part of the product rather than a bolt-on chatbot widget, structured extraction that turns free text into typed data the rest of your code can trust, semantic search that finds by meaning instead of keywords, and agent workflows that chain tools under rules you set.
Most AI features ship as a demo and stay a demo. The prototype works on the happy path, then meets real users — long documents, slow responses, dropped connections, confident wrong answers. Without streaming, cancellation, retries, guardrails and evals, the feature gets switched off two months after launch and quietly written off as “AI didn’t work for us”.
The alternative is an AI feature built like any other production feature: typed end to end, cheap to run, observable, and shippable behind a flag to 5% of users first. Already on an older stack? Start with a Vue 2 to Vue 3 migration or a Nuxt 2 to Nuxt 3 upgrade — a supported framework is a precondition for streaming UI, not a nice-to-have.
What an AI feature build delivers
- A feature users keep using — streaming, cancellable, and honest when it does not know
- Predictable cost per user, measured and capped before it reaches production
- An eval set, so prompt changes become measurable instead of vibes
- TypeScript strict end to end, plus documentation and a 30-day warranty after go-live
How much does it cost to add AI features to an existing app?
It depends on whether the feature needs your own data. A well-scoped copilot or generation UI on top of an existing API typically runs 3–6 weeks. Retrieval over your own content, evals and a staged rollout push it further. The feasibility audit gives you a fixed number before you commit.
Which model or provider should we use?
Whichever fits the latency, cost and privacy constraints we establish in the audit — the integration is written provider-agnostic, so swapping OpenAI, Anthropic, Google or a self-hosted model behind an OpenAI-compatible endpoint is a config change, not a rewrite. Locking your product to one vendor’s SDK is the mistake to avoid.
Will our data be sent to a third-party model?
Only if you decide it should. The audit covers exactly which fields leave your infrastructure, which stay, what gets redacted, and whether a self-hosted or EU-region model is required. I work with EU and US teams and treat this as an architecture constraint, not a checkbox.
How do you stop the AI from making things up?
You cannot eliminate it, so the design has to survive it: retrieval with citations users can click, schema-validated structured output instead of free text where the answer feeds other code, confidence and empty states that say “not found”, and an eval set that catches regressions before your users do.
Do you build chatbots?
I build in-product assistants — something that can see the user’s data, call your API and act, with the answers cited. That is a different thing from a support chatbot widget dropped on top of an FAQ, which a hosted tool will do faster and cheaper than I can. If a widget is what you need, take the widget. If the assistant has to understand your product and do work inside it, that is this.
Do you do the backend too, or only the frontend?
Frontend-led, full-stack where the feature needs it — Node or Nuxt server routes for the streaming endpoint, retrieval and tool calls. Pure backend-only AI work is not what I take on; the value here is that the AI actually reaches the user well.
We are still on Vue 2. Can we add AI features anyway?
Technically yes, practically it is bad economics. Streaming UI, Suspense and modern tooling assume Vue 3. In most cases the honest sequence is migration first, AI second — and the migration usually pays for itself in delivery speed regardless.
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Athens, Greece — working remotely with EU & US teams
info@offline-web.com
