AI integration for web applications

AI features for Vue and Nuxt apps — an in-product copilot streaming an answer with cited sources

AI is most useful when it becomes a real product function rather than a disconnected demo. The work starts with the user action the feature needs to support, the data it can access and the limits that keep behavior and cost under control.

AI as a product function

Assistants, retrieval and agents should be designed around a real workflow inside the product rather than added as a generic chat box.

Not every feature needs AI

If a normal rule, search or form solves the problem more reliably, that is usually the better solution.

Models and technologies

The model and provider are selected for the use case. The architecture should make it possible to change providers or models without rebuilding the whole product.

Data access and control

Access rules, tenant boundaries, cost controls and safe tool usage must be part of the feature design from the beginning.

01
Define the use case
The work starts with the user action the feature needs to support and whether AI is actually the right solution for it.
02
Define data and access
Access rules, tenant boundaries, cost controls and safe tool usage are defined as part of the feature design.
03
Implement and evaluate
The feature is built and evaluated against real cases, so behavior and quality are measured before release.
04
Put the feature into production
The feature goes into production with monitoring and limits on behavior, latency and cost.

What can be added

  • AI assistants
  • Search across data and content
  • Content processing
  • AI agents and automation
  • AI functions on top of existing business data
Before and after: a blocking fetch replaced by a streaming copilot response with cancellation and citations in Vue
LLM evaluation suite and runtime dashboard showing pass rate, p95 latency and cost per request
Can AI be added to an application that already exists?
Yes. If the existing application and its data allow it, an AI feature can be added without rebuilding the whole system.
No. The choice is made based on the function that needs to be implemented, the data, the required quality, the speed and the cost.
Yes, as long as there is a clear way to access it and the right limits have been defined. Depending on the case, search, RAG or a direct connection to specific data sources can be used.
AI features should not be designed on the assumption that every answer will always be correct. Depending on the use, validation, limits, evaluation of results or human confirmation before critical actions can be added.
The running cost depends on the model, the number of requests, the size of the data and how the feature is used. These are taken into account during design so the cost is understood before the feature goes into production.
No. Different features can have different requirements. A different model or provider can be used depending on the quality, speed and cost each case needs.

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