AI Integration and LLM Solutions

What is AI integration and what does it cover?

AI integration is different from using an off-the-shelf chat tool. The goal is for a large language model (LLM) to work inside your product or internal process as a component that knows your data, your rules and your systems. The model on its own is only one part; the real work is connecting it to the right data, the right permissions and the right controls.

UNIT İstanbul runs enterprise AI projects under the Unit Software brand, from the same office as its software team. Because we come from a marketing agency background, every build starts with the same question: what will this feature improve, and how will we measure it? These are the AI solutions we are asked for most often:

• Assistants that answer from company knowledge: systems that draw on policies, product catalogues, technical documentation or contract archives and cite their sources.
• Customer service chatbots: bots on your website, app or messaging channels that handle common questions and hand the conversation to an agent when needed.
• Document processing: workflows that read invoices, forms, applications or emails, classify them, extract fields and summarise them.
• AI features in internal tools: summaries, draft writing and smarter search added to a CRM, support desk or admin screen.
• Workflow automation and agents: systems that carry out several steps with defined tools and ask for human approval at critical points.

Is AI integration the same as AI search visibility?

No. AI integration is a software service that builds models into your own products and processes; getting your brand cited as a source in the answers of AI assistants is a marketing service. For the latter, see our GEO (generative engine optimisation) page.

Needs that don't involve AI, such as process software, admin panels or ERP connections, fall under our enterprise software service. The two often go together: the process and data are put in order first, and the AI feature is then added on top of that solid foundation.

Which tasks suit AI, and which don't?

Not every process is a good fit for a large language model. Models are strong at understanding free text, summarising, classifying and answering in natural language. For exact calculations, rule-based decisions or tasks that need exactly the same output every time, conventional software is more reliable and more economical.

During discovery we assess candidate use cases together for impact, feasibility and risk. We usually recommend starting with a low-risk use case that delivers value quickly and has a measurable outcome; the next steps are planned with the data from that first build.

How does an assistant that answers from company knowledge (RAG) work?

RAG (retrieval-augmented generation) is a method in which the model first finds the relevant passages in your documents and then writes its answer based on them. This lets the model use current, company-specific information that was never in its training data, without the need to retrain it.

1. 1.Sources are selected and cleaned: outdated, contradictory or confidential documents are filtered out.
2. 2.Documents are split into meaningful chunks and added to a vector index for semantic search.
3. 3.When a user asks a question, the most relevant chunks are retrieved, combined with keyword search where useful.
4. 4.The model writes its answer based only on those chunks and shows the source it used.
5. 5.If the sources hold no answer, the assistant says so clearly and points the user to the right person or channel.

What decides the success of a RAG build is less the model itself than the quality of the source documents and the access permissions. A document an employee isn't allowed to see should not become visible through the assistant either, so we enforce permissions in the retrieval layer.

What should you watch for in chatbot development?

A customer service bot speaks in your brand's voice, and when it gives wrong information it affects the customer directly. That is why it pays to start with a narrow, clear scope and to set down in writing what the bot will and won't answer. Before the build we answer these questions with you:

• Which topics does the bot answer, and when does it hand the conversation to an agent?
• After what verification is personal data such as order status, appointments or account details shown?
• Is the conversation history and a short summary passed on at handover?
• How are misuse, prompt injection and off-topic requests handled?
• Which conversations are logged, how long are logs kept and who can access them?

To see the bot's effect on sales, lead capture and customer satisfaction, we connect conversation events to your analytics setup. This measurement layer is built the same way as our CRO and analytics work, so the bot is tracked in the same report as your site's other conversion points.

Closed or open model? Cloud or on-premises?

Choosing a model is not a one-off decision. We design the architecture so that the model is a replaceable part; when a better or more economical model appears, the whole application doesn't have to be rewritten. These are the main options in LLM integration:

We make the decision with you based on how sensitive the data is, the expected volume of use, response-time needs and your team's capacity to run the system. We don't recommend architectures that lock you into a single provider.

How are data privacy and data protection law handled in AI projects?

Every AI workflow that processes personal data must be assessed under the applicable data protection law, such as Turkey's KVKK or the GDPR in Europe. When a cloud-based model is used, the contract should make clear whether data is transferred abroad, whether the provider uses it for model training and how long it is retained.

• Data minimisation: only the fields the task needs are sent to the model; fields such as ID numbers are masked.
• Access rights: the assistant shows only data the user is already authorised to see.
• Logging and retention: how long prompt and response logs are kept and who can see them is documented.
• Transparency: users are told clearly that they are interacting with an AI system.

We carry out the legal assessment together with your legal adviser or data protection team; we put the technical safeguards in place and document them.

How do you reduce the risk of wrong answers (hallucinations)?

Large language models can produce fluent, convincing answers even when they are unsure. The risk never disappears completely, but good design reduces it markedly and makes it manageable. In our builds we combine these measures:

• Grounding answers in approved sources and showing the source to the user.
• Defining "I don't know" as an explicit rule when the sources hold no information.
• Requesting output in a structured format with defined fields and validating it in code.
• Requiring human approval for high-impact actions such as refunds, contracts or emails to customers.
• Testing the system before launch against an evaluation set made of real questions.

How are monitoring and cost control handled after launch?

An AI feature isn't finished on the day it goes live. User questions change, documents are updated and model providers release new versions. That is why every build comes with an evaluation set and a monitoring dashboard.

Monitoring tracks answer accuracy, source citation, user feedback, conversations handed to agents, error and latency logs and usage costs together. Any model or prompt change runs through the same evaluation set before it goes live, so you can see whether an improvement in one place breaks something elsewhere.

Usage costs depend on the volume of text processed, the size of the chosen model, response-time expectations and how the system is hosted. Routing simple tasks to smaller models, caching repeated answers and trimming unnecessarily long prompts keep costs under control. We define budget limits and alerts as part of the build.

Why choose UNIT İstanbul for AI integration?

UNIT İstanbul has managed brands' digital growth through measurement since 2009, and it delivers software with the same discipline. We don't build AI features as showpieces but as business tools with a clear goal, a clear metric and a clear owner. Because strategy, software development and measurement sit in one team, you work with a single point of contact from build to report.

You can see our software work on our software portfolio page. To talk through the use case you have in mind, get in touch via our contact page; in the first conversation we'll assess fit and possible risks together.