Enterprise AI solutions:
Turkish-speaking assistants that work with your own data.

What is enterprise AI and what is it used for?

Enterprise AI is the umbrella term for applications that connect techniques such as large language models, speech recognition and image processing to a company's own processes. Unlike a public chat tool, it works with the company's own knowledge and connects to existing systems. It can read the product catalog, contracts, technical documentation or customer history, and write its results to enterprise resource planning (ERP) or customer relationship management (CRM) software. The most common uses fall into three groups. First, assistants that talk to customers: they handle questions by phone or on messaging channels and take orders or appointments. Second, access to internal knowledge: an employee asks a question in everyday language, and the system finds the answer in the relevant documents and shows its source. Third, document processing: fields on invoices, forms or delivery notes are read and passed to the right system. Set up well, these applications cut repetitive work, shorten response times and keep critical knowledge from living in only a few people's heads.

Which tasks do companies use AI for?

AI pays off fastest in high-volume work with clear rules. A customer line that fields the same questions every day, an accounting team keying in hundreds of invoices by hand, or an operations team whose new hires lose time hunting for procedures are typical starting points. Success is easy to measure in this kind of work: calls handled, documents processed, or the time it takes to get an answer. Decisions where every case is different and that depend on judgment and accountability, on the other hand, are not handed over to AI entirely; there, the system acts as an assistant that prepares drafts or summarizes the options. Three questions help when choosing a use case. How many times a month is this task repeated? Is the information behind the right answer kept in a written source? If something goes wrong, who will notice, and how? If all three have clear answers, the task is a good candidate for AI. The list below sums up the areas we see most often.

• Customer service: voice or text assistants that answer incoming calls and messages and respond to frequently asked questions.
• Sales and orders: conversation flows that check stock, give price information and record orders and appointments.
• Internal knowledge: everyday-language search across procedure, regulation and technical documentation archives.
• Document processing: reading information from invoices, forms and delivery notes and entering it into your systems.
• Production and quality: spotting unusual conditions in machine data; we cover this separately on our industrial data analytics page.

How does AI answer from your own documents?

The AI first finds the passages in your documents that relate to the question, then writes its answer based on those passages; this method is called RAG, short for retrieval-augmented generation. In practice, the company's document collection is searched for relevant sections, the passages found are given to the language model as context, and the model builds its answer on them. The answer therefore rests on your current documents rather than on the model's general knowledge, and its source can be shown. The approach has two practical advantages. First, when documents change, the model does not need to be retrained; only the search index is updated. Second, the risk of the model making up answers, known as hallucination, drops noticeably when it has to cite its sources and is allowed to say "this information is not in the documents". Retraining the model itself on company data (fine-tuning) is a separate step, used when a particular language or style is needed. A typical setup consists of these steps:

1. Preparing documents: PDFs, Word files, web pages and database records are read and split into meaningful chunks.
2. Indexing: each chunk is converted into a numerical vector that represents its meaning and written to a searchable index.
3. Retrieval: the chunks closest in meaning to the user's question are found; documents the user is not authorized to access are left out.
4. Answer generation: the language model writes the answer based on these chunks and cites the source.
5. Updating and monitoring: the index is refreshed as documents change, and questions that could not be answered are reported.

Can you use AI without your data leaving the company?

Yes. Enterprise AI can be set up under two main deployment models. In the cloud model, questions and the relevant document chunks are sent to a language model in a provider's data center; setup is fast and you get the latest commercial models. In the on-premise model, the language model, search index and documents stay on your company's own infrastructure, and no data is sent outside. This route suits organizations that handle personal data or trade secrets, or must run on a closed network, but it adds hardware and maintenance overhead. The choice depends on data sensitivity, obligations under Türkiye's Personal Data Protection Law (KVKK), expected usage volume and budget. Many projects use both: sensitive documents are processed in-house while general-purpose work runs in the cloud. The systems BYT Digital builds can run on your own servers (on-premise), so sensitive data never leaves your organization; cloud deployment is also an option. We decide together at the start of the project which one suits you. The table below compares the two models.

What does BYT Digital do in this field?

BYT Digital is a boutique technology studio at İzmir Technology Development Zone (Teknopark İzmir), working in AI, custom software and industrial automation. On the AI side we build three kinds of systems: voice assistants that speak Turkish with your customers, smart search that answers from your enterprise documents, and image recognition that reads invoices or forms. We work with Turkish-first models; depending on your needs, the system can run in the cloud or on your own servers so that sensitive data stays in-house. We can combine voice commands, data read from images and text in a single assistant, so a phone call, a photo of an invoice or a WhatsApp message is answered from the same data and in the same tone. We have tested this architecture in our live products and adapt it to client projects. We have experience integrating with existing ERP, CRM and communication platforms; before every integration we carry out a compatibility and risk analysis and make a clear recommendation. You work directly with the founding engineer.

• Turkish-speaking voice assistant for phone, WhatsApp and web
• Smart search that answers from enterprise documents
• Image recognition that reads data from invoices and forms
• Cloud or on-premise deployment
• ERP (SAP, Logo, Mikro, Netsis), CRM (Salesforce, HubSpot, Zoho) and WhatsApp Business integration

How does an AI project run?

The soundest way to run an AI project is to start with a single, well-defined use case, measure the result and then expand. At BYT Digital the process has six steps. During discovery (1–2 weeks) we work out your needs, users and business processes together; the first meeting and pre-analysis are free. By the end of planning and design (1–3 weeks in total) you have the roadmap, technical architecture, timeline and price in writing. Development takes 4–16 weeks; we work in short cycles (sprints), show a working version at the end of each one, and you keep access to the code repository. Testing and delivery (1–2 weeks) covers automated tests, manual quality checks, documentation and user training. Bug fixes are free for the first 30 days after launch; after that, maintenance continues under a monthly service level agreement (SLA). Projects with a clear scope are fixed-price; projects that require research run on time and materials. See the project process section on our home page for each step in detail.

Related product: Bi Call AI

Bi Call AI is a voice AI assistant developed by BYT Digital that works with Turkish natural language processing. It answers incoming calls and handles conversations such as sales, stock inquiries, appointments and frequently asked questions in Turkish. It works across phone, WhatsApp and web channels and connects to ERP and CRM systems. It is used most by e-commerce, healthcare, automotive service and enterprise software (B2B SaaS) companies. You can explore it as a live example of the voice assistant and multichannel architecture described on this page.

Bi Call AI on our home page Visit the Bi Call AI site

Frequently asked questions