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Adding AI to Existing Products

5 min read

Backend

Start with one endpoint. Chat completions, embeddings, or search. Don't rewrite everything.

Platform

Your infra already has logs and metrics. AI adds new patterns — plan for latency and cost spikes.

Data Eng

Pipelines feed RAG. Your existing ETL is the ingestion layer. AI sits on top.

Adding AI to Existing Products

TL;DR

  • Brownfield AI = bolt-on, not rebuild. Find one high-impact, low-friction insertion point.
  • Start with "enhance" not "replace." Autocomplete, suggestions, summarization — additive wins.
  • The hard part isn't the model. It's prompt management, fallbacks, and cost control.

You're not building ChatGPT. You're making your existing product smarter. That's a different game.

Where to Insert AI First

PatternEffortImpactRiskExample
Search enhancementLowHighLowSemantic search on docs, support tickets, or product catalog
Autocomplete / suggestionsLowMediumLowCode hints, search suggestions, form fill
SummarizationLowMediumMediumMeeting notes, long threads, ticket summaries
Chat interfaceMediumHighHighFAQ bot, internal assistant
Full agentHighHighHighAutonomous task execution — save for later

Rule: Pick one. Ship it. Learn. Then add more.

The Bolt-On Architecture

[Your Existing App] → [AI Gateway / Adapter] → [LLM API]
                          ↓
                   [Prompt templates]
                   [Fallback logic]
                   [Cost / rate limits]

You don't touch core logic. You add a thin layer that:

  • Takes user input from your existing UI
  • Calls an LLM (or RAG pipeline)
  • Returns structured output your app can render
  • Handles timeouts, errors, and "I don't know" gracefully

Practical First Steps

  1. Search. If you have a search box, add semantic/vector search alongside keyword. Embed user query, search your indexed content, return ranked results. Users get "fuzzy meaning" search without learning anything new.

  2. Summarization. Long-form content? Add "Summarize" button. One API call. Cache the result. No UX change except a new button.

  3. Suggestions. Forms, filters, or config? "Suggest values based on similar users" or "Complete this based on context." Non-blocking. Falls back to nothing if API fails.

What Not to Do

  • Don't replace core flows with AI. If it breaks, your product breaks.
  • Don't assume AI is always available. Network hiccups. Rate limits. Budget overruns.
  • Don't skip the fallback. "Sorry, try again" or "Show traditional results" — always have a path.
// Your existing app stays. Add a thin AI gateway:
async function enhanceSearch(query: string): Promise<SearchResult[]> {
try {
  const embedding = await embed(query);
  const results = await vectorSearch(embedding, { topK: 5 });
  return results;
} catch (err) {
  // Fallback: traditional keyword search. Never dead-end.
  return keywordSearch(query);
}
}

Quick Check

You're adding AI to an existing product. Where's the best first insertion point?

Do This Next

  1. Map one user flow in your product. Where would "smarter" help? Search? Suggestions? Summarization?
  2. Pick the lowest-effort insertion point and sketch the API contract. What goes in? What comes out?
  3. Prototype in 2 hours — a single endpoint, mock or real LLM, no UI change. Validate the data shape before building UI.