Most AI projects I've seen fail quietly. Not dramatically — no press release, no post-mortem. Just: the feature shipped, nobody used it, it got quietly disabled six months later.
The pattern is always the same. Someone saw a ChatGPT demo, figured it was time to "add AI", wrapped a few API calls around a text box, and called it a feature. Engagement was fine for a week while the novelty lasted, then flatlined.
That's the wrapper problem. A wrapper is just a relay. User input goes in, LLM output comes out, and at no point does the LLM know anything about your business, your customers, or your actual data. The result is generic output that your users could get for free at chat.openai.com, but with your logo on it.
What actually works
Every AI project I've built that actually stuck had one thing in common: the LLM was one component in a larger system, not the whole product.
The LLM's job is to understand unstructured content and make judgment calls in ambiguous situations — things that would require hundreds of edge-case handlers if you tried to do them with rules. Its job is not to be your database, your business logic layer, or your reliability guarantee. For any action that has to be reliable, deterministic code handles it. The LLM just decides what to do.
RAG in plain language
RAG (Retrieval-Augmented Generation) sounds complicated but the concept is simple: before you ask the LLM anything, look up the relevant information from your own data and include it in the prompt. The LLM answers based on your actual content instead of whatever it learned in training, which may be a year out of date.
An LLM asked to answer a customer question about your return policy will hallucinate something plausible. An LLM that's been given your actual return policy document as context will just answer it correctly. That's the whole idea.
The mechanics: embed the user's question as a vector, search your document store for similar content, inject the matches into the prompt, generate. Four steps. The hard parts are getting your documents into the store in a useful format and deciding how much to inject without bloating the context.
A real example: invoice processing
I built this for a logistics company in Armenia. Their accounts team was spending 4+ hours a day pulling data out of supplier invoices — three different formats, sometimes handwritten, sometimes scanned PDFs with bad OCR.
A generic chatbot couldn't solve this. The problem wasn't understanding language, it was extracting structured data from messy documents and validating it against known rates.
The pipeline I built: document comes in → OCR extracts the text → classification step identifies which supplier template it matches → structured extraction pulls the specific fields → validation checks the totals against known rates → if everything looks right, it writes to the ERP. The LLM handles the classification and the ambiguous extraction cases where rule-based code would need constant maintenance as supplier formats change.
Result: 4 hours down to a 10-minute exception review. Same headcount, three times the invoice volume.
Before you build anything
Four questions I ask before adding AI to anything. If you can't answer all four, don't build it yet.
Can a simpler, deterministic solution work? If yes, use that. AI adds complexity and running costs that are hard to justify if you could just write an if-statement.
Do you have the data to ground it? An LLM without domain data is expensive autocomplete. If you're asking it to answer questions about your product and you haven't fed it your product data, the answers will be wrong in subtle ways that are hard to catch.
What happens when it's wrong? It will be, sometimes. You need a validation step and a human review path. "The AI made a mistake" is not an acceptable error state for anything that touches real decisions or money.
How will you measure whether it's better? Define the metric before you write any code. Hours saved, error rate, throughput — pick something concrete. If you can't measure it, you can't manage it, and you won't be able to justify the API costs when finance asks.
AI earns its cost when it's embedded in a system that controls its inputs, validates its outputs, and can actually tell you whether it's working. Everything else is a demo that ships and dies quietly.
If you're looking to build something that actually works — a RAG pipeline, an AI automation workflow, or an agent with real tool access — let's talk.