AI solutions and implementation for companies in BiH

What AI implementation means here

We do not sell “AI transformation” as a slogan. We start from the use case: which documents, which users, which systems already exist and what the data risk is. Only then do we choose an approach — API access to OpenAI or Azure OpenAI, RAG over your knowledge base, an OCR flow for invoices and contracts, or a feature inside software you already run.

The stack we use in practice covers LLM integrations, RAG and knowledge assistants, Document AI and OCR, Python and LangChain where orchestration is needed, and private or on-premise options when data must stay inside your infrastructure. This matches the AI service description already on the site — no invented references.

AI can sit next to a new website, for example as an assistant on the site, or alongside ready-made modules and custom applications. The homepage talks about web and AI; the detailed industry modules remain on the solutions hub.

Before choosing a model we take an inventory of the data: where the PDFs are, who maintains them, how current they are and whether they may be sent to an external API. Very often the first job is cleaning and chunking documents, not building a chat interface. Without that step RAG gives weak answers and users lose trust in the first week.

For companies in BiH the local context matters: Bosnian-language documents, a mix of scanned PDFs and Word files, and teams without a data science department. That is why the prototype runs on your own samples and not on a synthetic English dataset, which shortens the road from demo to real value.

Example uses inside companies

An internal assistant that answers questions from procedures and PDF handbooks (RAG). A website chatbot that takes over frequent questions and leaves a contact route for complex ones. Field extraction from scanned invoices into a table or ERP. Classification and routing of support tickets. Contract summaries and deadline reminders — with human review before anything is sent to a client.

Each of these scenarios needs clean data sources and access rules. That is why the brief asks where documents live, who may see the answers and whether an audit log is required. Without that, AI stays a demo; with it, AI becomes part of a process.

In administration it can mean summarising minutes and proposing actions with mandatory human confirmation. In sales, an assistant that suggests the next step based on your playbook rather than generic marketing copy. In operations, OCR over scanned forms with validation of required fields before anything is written into the system. Every scenario has a process owner in your company — AI does not replace ownership.

Security, private and on-premise options

For sensitive industries or internal policies we offer options where data does not leave your infrastructure. That can mean models inside your own cloud tenant or strict rules about what context is sent outside. We do not claim universal compliance with every regulation; boundaries are defined per project.

Hallucination is not treated as the only risk. Measures include grounding on documents (RAG), citing sources where that makes sense and a human in the loop for decisions with legal or financial effect.

We define what may go into a prompt, what stays in the logs and how long conversations are kept. For internal assistants we often disable provider-side training on your data and limit the tools the model may call. These are engineering decisions, not a checkbox on a marketing page.

What an engagement looks like

A short consultation, one chosen use case, a prototype on your sample data, measurement (for example the share of resolved questions or the time to process a document), then production and maintenance. AI engagements are not on the fixed price list of the web packages; those cover websites, while AI is scoped separately after a brief.

If all you need is a chatbot, there is a dedicated page for it. If you need web and AI together, start with a web package and the AI add-on in the same conversation.

After the prototype we agree a bar for “good enough for production” — accuracy on a test set of questions, response time or the share of escalations — and only then roll out to more departments. If you are building a new website in parallel, we align the assistant's design with your brand and the calls to action on the site.

AI changes a business gradually: first one process, then a second department. We avoid parallel AI projects without an owner, because then nobody picks up the phone when an answer is wrong. Prompt policies and knowledge-base versions are documented like any other code. In regulated environments the limits and escalation rules go into the brief. If you already have a Next.js site, the AI layer can share the design system and authentication; if you have legacy tools we often build a thin API bridge first. Either way we measure before and after against an agreed KPI — minutes per document, or the share of tickets solved without an agent. Without measurement AI stays a demonstration. The chatbot is described on its own page so that visitors and search engines can tell the two services apart.

FAQ — AI