Bringing GPT and LLMs into Business Products: Opportunities and Risks

Over the past two years, integrating large language models (LLMs) has moved from an enthusiast experiment to a real budget item. More and more clients come to OneDev asking us to «add some AI» — but behind that phrase there is usually not an understanding of the technology, but an expectation of magic. In this article we cut through the marketing fog: what LLMs can do, what they cannot, what they cost, and what to look at before you sign off on a budget.

Where LLMs genuinely add value

Language models excel wherever you need to work with unstructured text: understanding it, rephrasing it, classifying or generating it. This is not magic — it is a very powerful language-processing tool. The use cases that pay off best in business today look like this:

• Customer support. A bot answering common questions from the company knowledge base removes 40–70% of routine work from agents. The key phrase is «from the knowledge base», not «from the model's head».
• Document processing. Extracting data from contracts, invoices, applications and acts; summarising long threads and meeting notes.
• Internal search and assistants. An employee asks in natural language, the system answers based on corporate policies and instructions.
• Content and communications. Drafts of emails, product descriptions, social replies — always with a human edit.
• Conversation analytics. Tagging thousands of dialogues, extracting topics and sentiment — work no one would ever do by hand.

Notice that in every strong use case the LLM works not as a «source of truth» but as a processor of information you already have. That is precisely the line between value and trouble.

Accuracy: the model is confidently wrong

The key trait clients underestimate is that the model does not know when it is wrong. It delivers a correct fact and an invented one with equal confidence (this is called a «hallucination»). For a draft that is tolerable; for telling a customer their outstanding balance or warranty terms it is unacceptable.

The solution is the RAG architecture (Retrieval-Augmented Generation): the model answers not from memory but based on your documents, which the system injects into the prompt. This anchors answers to facts, makes them verifiable and lets you show the source. For critical operations — payments, legal wording, medicine — you add human confirmation or strict scripted constraints.

Data: where it ends up and who sees it

This is the most underestimated topic in Uzbekistan. When you send a request to a cloud API (OpenAI, Anthropic, Google), the data leaves the country. For some businesses that is fine, but for banks, healthcare, the public sector and any personal data you must separately assess compliance with the regulator's requirements and the personal data law.

Cost: not just a subscription

Clients often think the price of an LLM is a flat subscription. In reality cloud models are billed by tokens (roughly fragments of words): you pay for both the input request and the output. So the real cost depends on traffic volume, context length and how carefully your prompts are built.

The cost structure of an implementation usually breaks down as follows:

• Development and integration — the main one-off item: wiring it up, RAG, interface, testing.
• Model usage fees — variable, growing with load; easily runs out of control without limits and caching.
• Infrastructure — a vector database, servers; for local models, expensive GPU hardware.
• Maintenance — updating the knowledge base, monitoring answer quality, fine-tuning.

A cheap model with poor prompts often costs more than an expensive one with a sound architecture, because it burns extra tokens and forces re-prompts. Savings start with engineering, not with picking the cheapest plan.

What to consider before you start

Before ordering an «AI feature», answer a few questions — they determine whether the project succeeds or becomes an expensive toy:

• What specific problem are we solving and how will we measure success? «Implement AI» is not a task. «Cut support response time by 50%» is.
• Do we have the data the model will rely on, and what state is it in? Chaos in the knowledge base equals chaos in the answers.
• What happens if the model is wrong? Where is human oversight mandatory?
• What data must never leave the company perimeter?
• Are we ready to maintain the system, not just launch it?

A sound approach is to start with a narrow pilot on a single process, measure the result on real data, and only then scale. It is cheaper, more honest and gives you understanding before you commit a large budget.