What an AI integration project actually involves
A step-by-step breakdown of scoping, building, and shipping an LLM-powered feature that survives contact with real users — not a demo.
Start with the failure mode, not the feature
Every AI feature request starts as "add a chatbot" or "let users ask questions about their data." The useful first question is never the feature — it's what happens when the model gets it wrong. A support copilot that occasionally gives a slightly-off answer is a minor annoyance. A copilot that occasionally fabricates a refund policy is a liability. The failure mode determines the entire architecture: how much you constrain the model, whether you need retrieval grounding, whether a human needs to approve outputs before they reach a user, and how much logging and evaluation the system needs before launch.
Scoping: retrieval vs. generation vs. automation
Most AI integration work falls into three distinct categories, and they have different risk profiles and different build effort:
- Retrieval-augmented generation (RAG) — answering questions grounded in your own documents or data. The engineering effort here is mostly in chunking, embedding, and retrieval quality, not the generation step; a bad retriever produces confidently wrong answers no matter how good the underlying model is.
- Open generation — copilots, drafting tools, summarization. Lower factual risk since the user reviews the output, but needs strong prompt design and guardrails against the model going off-topic or producing unusable formatting.
- Automation — the model takes an action (sends an email, updates a record, calls an API) without a human in the loop first. This carries the highest risk and needs explicit constraints on what actions are possible, not just what the model is told to do — a system prompt is not a security boundary.
Building it so it doesn't break in production
The gap between an AI demo and an AI feature that survives real usage comes down to a short list of unglamorous engineering work most teams skip when moving fast:
- Structured output validation — never trust the model to return well-formed data; validate and reject/retry on malformed responses.
- Fallback behavior — a clear, tested path for when the model errors, times out, or returns something the validator rejects. "The feature just doesn't work sometimes" is not an acceptable fallback.
- Rate limiting and cost controls — LLM APIs bill per token; a feature without limits can produce a surprising invoice or become a vector for abuse.
- Prompt injection awareness — anywhere the model reads untrusted input (a document, a webpage, a user message that gets passed to a tool), treat that input as potentially adversarial, the same way you'd treat unsanitized SQL input.
Evaluation: how you know it's working
"It looked good in testing" is not evaluation. A real evaluation setup means a set of representative test cases — including deliberately hard or edge-case inputs — that get run against the system whenever the prompt, model, or retrieval pipeline changes, with someone actually reviewing the outputs for quality, not just checking that the API call succeeded. For anything customer-facing, this needs to happen before launch and continue after, since model provider updates can silently shift behavior.
What a realistic timeline looks like
For a single well-scoped feature — a RAG-based support assistant over an existing knowledge base, for example — a realistic build is measured in a small number of weeks, not days: initial scoping and data prep, a working prototype, then a meaningful chunk of time on evaluation, guardrails, and edge cases before it's ready for real users. Teams that skip the evaluation and guardrail phase ship faster and then spend longer fixing trust problems after launch — the phase that looks skippable is usually the one that determines whether users trust the feature at all.
Working through a decision like this?
Tell us what you're building and we'll give you a straight answer, even if it's not us.