What we mean by AI integration
Plugging an off-the-shelf chatbot into a website is easy, but that kind of bot answers with generic phrases and can make up things you don't actually offer. Real integration means the AI works with your data, follows your rules, and is built into a process where someone actually uses its output.
Technically, this usually combines several parts: a language model via API, your knowledge base, logic that decides what to send to the model and when, a user interface, and answer quality control.
Where AI helps and where it doesn't
The biggest strength of language models is working with text: answering common questions, searching documents, summarizing and structuring information, sorting requests. They are good at taking routine work off people who do the same thing every day.
AI performs worse where mistakes are costly and there's no one to check: precise calculations, legally significant answers, decisions about a customer's money. In those cases we either keep a human in the loop or advise against using AI at all. For forecasts based on numerical data, classic ML models are a better fit.
How an assistant answers from your documents
Most useful assistants rely on an approach called RAG. First, the system searches your knowledge base for passages relevant to the question, then passes them to the language model along with the query. The model builds its answer from what was found, not from its general memory.
The upside: answers are based on current data, and updating the knowledge base changes the answers immediately, with no retraining. The downside: quality depends on how well organized your documents are. Outdated or contradictory guidelines produce contradictory answers.
Hosted models via API or your own server
Models via API (OpenAI, Claude, Gemini, and others). Pros: the highest answer quality, a fast start, no hardware of your own. Cons: you pay per request, data goes to an external provider, and you depend on its terms and pricing.
Open models on your own server. Pros: data never leaves your infrastructure, and there's no per-request fee. Cons: you need powerful GPU servers, setup, and maintenance, and quality on complex tasks is often lower.
A sensible approach is often to start with an API to validate the idea and decide on self-hosting once volumes and data requirements are clear.
Risks we discuss openly
- Made-up answers. A language model can confidently state something false. We reduce this risk by restricting topics, answering only from sources, and linking to the document the information came from, but it can't be eliminated entirely.
- Confidentiality. Personal and business data sent in requests must be controlled. We decide what can be sent and what should be masked or processed locally.
- Costs. Request costs grow with the number of users and the length of context. We set limits and track spending from day one.
- Model changes. Providers update and retire models. That's why we design the architecture so the model can be swapped without rewriting the product.
Common AI integration mistakes
Starting with the technology instead of the problem. "Let's add AI" without a specific process usually ends with a chatbot nobody uses.
Not testing quality systematically. If you test an assistant with a few random questions, your customers will be the ones to find its weak spots. You need a standing set of test cases.
Leaving users alone with the bot. When the assistant doesn't know the answer, the user should reach a human agent quickly; otherwise AI just frustrates people.
Forgetting the knowledge base. An assistant answers only as well as its documents are up to date. Someone at the company has to own keeping them current.
What to prepare to get started
- a description of the process you want to improve, plus examples of real questions, requests, or documents;
- a knowledge base: guidelines, terms, catalog, FAQ answers, even if it's currently scattered across files;
- data requirements: what can and can't be shared with external services;
- a person who will review answers during testing and point out where the AI gets things wrong.
How we measure results
Before launch, we build a set of test questions with correct answers and measure how many the AI handles correctly. We rerun the same set after every change.
After launch, we look at business metrics: what share of requests the assistant resolves without a human, how much time agents spend per reply, and how often users ask to be connected to a person. If a feature doesn't have a noticeable impact, it's better to admit it and rework it than to keep paying for it. We also track request costs per resolved inquiry.
What affects the cost
- Feature type: semantic search, an assistant, document processing, or several scenarios combined.
- Size and state of the knowledge base: whether documents are ready or need to be gathered, cleaned up, and structured.
- Integrations: connecting to your CRM, catalog, help desk, or internal systems.
- Data requirements: external models via API or deployment on your own server.
- Load: the number of users and requests, which also drives ongoing model costs.
Frequently asked questions
Will AI replace our support agents? In most cases, no. It takes over common questions and drafts replies, while complex cases stay with people. That way agents get more done and focus on what really needs their attention.
Will the model be trained on our data? In most projects the model isn't retrained; it receives your data as context for a specific answer. Data usage terms vary by provider, and we choose settings that meet your requirements.
Can AI be added to an existing product? Yes, that's the most common scenario. We build the feature into your website, service, or app via API without rewriting the product.
How soon can we see results? A pilot on your data lets you judge fairly early whether the idea works, before you invest in a full rollout.
Why Promodex
Promodex has been in business since 2015, with 750+ projects delivered. Related work in our portfolio includes Zoomerang. What matters for AI integration is that we have an in-house team of developers, designers, and marketers: we handle not just the model connection but also the interface, integrations with your systems, and ongoing support. We're upfront about where AI makes sense and where it's simpler and more reliable to go without it.
