How to Add AI to an Existing App Without Rebuilding It

How to add AI to an existing app is mostly a question of where to put it. The app already works: it has users, data, permissions and a release process. The goal is to add a feature that uses a language model without disturbing any of that, and without betting the product on a component that can be slow, expensive or confidently wrong.

The good news is that existing apps already know how to live with that kind of dependency. A payment provider can be down, an email service can time out, a shipping rate can come back strange. Apps cope with these through ordinary engineering, and the same engineering is most of what adding AI takes.

#Choose the first AI feature: one job people already do by hand

The best first AI feature is a task people in or around the app already do manually, where the output can be checked quickly and a wrong answer costs little. Five kinds of task fit that description in most business apps:

• Summarising: a long ticket history, a call note or a contract, shown next to the original.
• Classifying: routing a request, tagging a document, setting a priority someone can change.
• Extracting: pulling fields from an invoice, an email or a form into structured data for a person to confirm.
• Searching: finding records or answers by meaning rather than exact words.
• Drafting: a first reply, description or report that a person edits before it is sent.

Each one sits inside a flow the app already has, and each one can be judged against what people do today. Avoid starting with a feature whose output nobody can easily verify, or one that acts on its own; the difference between a chatbot and an agent is mostly a difference in what a mistake costs, and the first feature should sit at the cheap end.

#How to integrate AI into an app: four patterns

There are four common ways to integrate AI into an app, and the right one depends on whether the user is waiting for the result.

Pattern	How it works	Good for	Watch out for
Inline request	The app calls the AI service while the user waits and shows the result	Drafting, rewriting, answering inside a screen	Response time; needs a clear loading state and a timeout
Background job	A queue sends records to the AI service and writes results to new fields	Classifying, extracting, summarising in bulk	Results arrive later; mark them as AI-generated until confirmed
Side panel	A separate assistant view reads the record on screen and suggests, without changing the main flow	Help, explanations, suggested next steps	Easy to ignore; measure whether people use it
Search over your data	Records are indexed so the model answers from your content	Help centres, internal knowledge, product catalogues	Answers are only as current and correct as the index; see how retrieval works

The background job is the gentlest start. It touches no existing screen, writes to fields the app did not have before, and can be switched off without anyone noticing anything but the missing suggestions.

#How to add AI to an application without coupling it to one provider

Put one AI service between the application and the model provider, and let nothing else in the app call the provider directly. That service is the only place that knows which model is in use, and it carries the work every call needs:

• Prompts, versioned. Stored with the code, reviewed like code, with a version recorded on every result so a bad output can be traced to the prompt that produced it.
• Timeouts and retries. A firm limit on how long the app waits, and retries that give up rather than stacking up.
• Structured output. Ask for a defined format and validate it in code: the right fields, allowed values, sensible lengths. A response that fails validation is treated as a failure, not passed on.
• Logging. The input, the output, the prompt version, the time taken and the cost, stored where you can search them.
• Caching and limits. Identical requests answered from cache, and limits per user and per feature.

Because the rest of the app talks only to this service, changing model or provider later is a change in one place, and testing the app does not need a live model at all.

#Decide what data the model can see

Send the model the least data that does the job, and only data the current user is already allowed to see. An AI feature that summarises a customer record should receive that record, fetched with the user's own permissions, not a query that can reach every customer. The same rule applies to search: an index built over all documents must still filter results by what the person asking can open.

Check the provider's terms on data retention and use before anything personal or confidential leaves your systems, and record the decision. If some data must not leave at all, that shapes the choice of model and hosting more than any feature does.

#Keep the old path: fallbacks for when the model fails

Keep the app's current behaviour as the fallback, and route to it whenever the AI step fails. Failures will come in four forms: the provider is down, the request times out, the output fails validation, or a limit is reached. In each case the user should get what they got before the AI existed: keyword search instead of semantic search, an empty draft instead of a suggested one, the manual tag field instead of a predicted tag.

This is why the first feature should improve an existing flow rather than replace it. The old path is your fallback, so do not delete it. It also gives you a comparison: if people do as well without the AI as with it, you have learned something cheaply.

The model is the one part of the system that can return a confident wrong answer with a success code. Validation catches wrong formats, not wrong facts, so anything a person will rely on should be shown as a suggestion they can see the source of, and confirm or change.

#Test prompts the way you test code

A prompt is code: it changes the system's behaviour, so it needs a test. Build a small set of real cases from your own data, with the output you expect for each, and run it in the pipeline whenever the prompt, the model or the provider changes. Score what can be scored automatically (the right category, the right fields extracted) and have a person review a sample of the rest.

Keep the cases that went wrong in production and add them to the set. Over time it becomes the most accurate description of what the feature is meant to do. The wider question of what to check before launch, including guardrails and where people review the output, is covered in what an AI feature needs before it ships.

#Control the cost per use

Measure what each use of the feature costs, not only the monthly bill. Model pricing is usually per amount of text in and out, so cost grows with use and with how much context each request carries. A feature that looks cheap in testing can become the largest line in the bill once every user runs it on every record.

• Send less. Trim the context to what the task needs rather than the whole record history.
• Cache. Many requests repeat: the same document summarised twice, the same question asked by many users.
• Match the model to the task. Classifying and extracting often work on smaller, cheaper models; keep the larger ones for tasks that need them.
• Set limits. Per user, per feature and per day, with an alert well before the ceiling.
• Prefer batch work. Background jobs can run in batches at quieter times; inline requests cannot.

#Treat what the model reads as untrusted

Anything the model reads can try to instruct it. If the feature processes customer emails, uploaded documents or web pages, someone can hide instructions in them. OWASP lists prompt injection as the top risk for LLM applications and recommends, among other things, giving the application its own narrowly scoped credentials, keeping privileged actions in ordinary code rather than in the model's hands, marking outside content as untrusted, and asking a person to approve high-risk actions (OWASP, LLM01:2025 Prompt Injection).

For a first feature that only suggests, the damage is limited to a bad suggestion. That is another reason to start there.

#Roll it out behind a flag

Release the feature behind a flag, to a small group first, with the old behaviour one switch away. Watch three things in the logs: how often the fallback fires, how often people accept, edit or discard the output, and what each use costs. Widen the group when those numbers are steady, and keep the flag after launch; a feature you can switch off in a minute is a feature you can afford to experiment with.

If you have an app that works and an AI feature you are weighing, send us the idea and we will review it with you: which flow it belongs in, which pattern fits, what the fallback is and what it is likely to cost per use. Reviewing ideas like yours is the first step in our AI and automation service.