Artificial Intelligence
AI integrations that work with your own data
We set AI up to work alongside your organisation’s documents and software. We build RAG systems from scratch and add AI features to the systems you already run. First, we decide together which tasks are actually suited to AI.
Systems that answer from your own documents (RAG)
A general-purpose language model does not know your product catalogue, procedures or contracts. In a RAG (retrieval-augmented generation) architecture, the model searches only your documents before answering and grounds its answer in those sources. The document an answer came from can be shown to the user.
We build these systems from scratch around your documents and your permission structure. Splitting and indexing the documents, the vector database, search and ranking, access control and measuring answer quality are all part of the project. The assistant answers each employee only from the documents that person is allowed to see.
Adding AI to the systems you already run
For most organisations, the quickest gains come inside software that is already in use. We add features to your ERP, CRM or custom-built system that classify incoming requests, summarise long records and prepare a report from a plain question.
Flows that read data from documents such as invoices, orders, delivery notes and quotes and pass it into the right system are also in scope. Records the AI is unsure about are not processed automatically; they go to an employee for review.
Choosing the model, and where the data stays
We work with models from OpenAI (ChatGPT), Anthropic (Claude) and Google (Gemini). For each task we pick the model that fits on accuracy, speed and cost by measuring it on sample questions. We do not tie the system to a single provider; the model can be swapped by changing the configuration.
Where data must not leave the organisation, we run open-source models on your own server. Processing of personal data under Türkiye’s data protection law (KVKK) is planned at the start of the project.
Consultancy first: which tasks suit AI?
Not every process benefits from AI. In AI consultancy we review your processes, identify the tasks where AI will genuinely save time, and set out the expected benefit and cost in writing. A task found suitable starts with a small pilot and is rolled out once the results are measured.
The architecture behind our own product
We use the same architecture in Vesta Partner, a product we built ourselves. Vesta Partner is an AI assistant for Instagram business accounts: it answers incoming messages with RAG from the business’s own product and policy information, takes orders straight from the chat and keeps stock up to date. It runs on Meta’s official messaging platform and can switch between Claude and OpenAI models through configuration.
Scope
What does this service include?
RAG and internal knowledge assistants
Assistants with access control that answer from company documents and cite their sources.
Chat assistants
Assistants that answer customer questions on your website and WhatsApp and hand the conversation to an employee when needed.
Document processing and automation
Flows that extract data from invoices, orders, delivery notes and quotes and pass it into your ERP or accounting system.
System integrations
Summarising, classification and reporting features for ERP, CRM and custom software.
AI consultancy
Identifying suitable tasks, assessing models and cost, and planning a pilot.
FAQ
Questions about AI integration
What is AI integration?
It means setting language models up to work with an organisation’s own data and software. An assistant answering from your product catalogue, incoming invoices being read and entered into your ERP, or CRM records being summarised are all examples of AI integration.
Will our data be used to train AI models?
Under the paid API terms of OpenAI, Anthropic and Google, data sent through the API is not used to train their models by default. For work where data must never leave the organisation, open-source models run on your own server. Which data is sent where is agreed in writing at the start of the project.
What happens if the AI gives a wrong answer?
In a RAG system the model answers only from your documents and shows its source; when the documents hold no answer, it says it does not know. AI can still make mistakes, so human approval and record-keeping are built into critical steps such as replies to customers, orders and accounting entries.
How do we decide which model to use?
By the accuracy, speed and cost the task needs, and by where the data must be processed. Several models are tried on sample questions and the results are measured. Because the system is built so the provider can be changed, switching is easy if a better-suited model appears later.
Let’s talk about your project
Tell us what you need in a free first meeting; then we’ll agree the scope and schedule together
Get in touch