AI integrations

What the AI integration service includes

We design and write the software layer that links language models to the client's real business processes: handling inquiries, parsing documents, searching a knowledge base, classifying leads. First we figure out where in the process time is lost or errors arise, and describe exactly which tasks to hand to the model and which to leave to a human. Then we assemble the integration: we connect the LLM via an API, set up the embeddings store, write the routing and answer-handling logic. If the client has equipment with control — for example a cutting line, a welding station or warehouse machinery — then physically the cutting, welding and hauling is done by their equipment, and we prepare the control program, the task transfer and the result check. In the end the client gets a working loop, not a one-off experiment with a chatbot.

How the integration really works

A language model on its own doesn't know the company's context, so the key part of the work lies around it, not inside it. The client's documents and records are split into fragments and turned into embeddings, that is numerical vectors by which the system finds semantic matches rather than just word matches. On a user's query, the relevant fragments are pulled from the vector store and passed to the model together with an instruction, so the answer is built on the company's data rather than the model's general knowledge. On top of this runs the logic: checking the answer format, constraints, passing the result to the CRM, the database or the equipment control. This scheme reduces the model's fabrications and makes behavior predictable.

Where neural networks and AI grew from

The starting point of applied neural networks is 1958, when Frank Rosenblatt described the perceptron, the simplest model that learned to separate data into classes. For decades development went in waves with periods of decline, until in 2017 a team of Google researchers published the paper "Attention Is All You Need" and the transformer architecture, which abandoned sequential processing in favor of an attention mechanism. On this architecture, BERT from Google came out in 2018, and OpenAI's GPT line took the approach to large generative models. It's important to see that the breakthrough was driven not by the mere fact of AI but by a specific engineering idea of processing sequences as a whole. We rely on this lineage when choosing which model to embed in a process and how.

Why precision of setup decides it

Between a model demo and a working system stands precisely the software part, and in it the cost of an error is higher than it seems. A wrongly assembled context, a weak prompt or a poorly tuned embeddings search produce a plausible but false answer, which is easy to miss in a stream of leads. In integrations with the client's equipment this is doubly critical: a control command must be strictly within tolerance, otherwise the machine will run the wrong trajectory, so we run the logic on a staging environment before handing it to real hardware. Precision here means reproducibility, control of the data format and handling of edge cases, not an abstract promise. That's why in AI integration the main work goes not into plugging in the model but into wrapping and verifying it.

The AI integration stack and tools

The base layer is calling language models via an API: requests to the LLM with control over temperature, length limits and system instructions, which gives text in and text out for a specific task. To understand meaning we use embeddings and vector stores so the system searches by meaning rather than by an exact wording match. We write the connecting code in the languages usual for the backend, more often Python and JavaScript, and embed the calls into existing processes via REST interfaces and data exchange in JSON format. Integration with processes means connecting to the CRM, databases, task queues and, where needed, to the client's equipment controllers that physically do the work. We pick the stack to fit the client's infrastructure, not the other way around.

When the foundational standards appeared

The tools that modern integration rests on have specific dates, not a blurred history. The REST architectural style, the basis of most web data-exchange interfaces, was described by Roy Fielding in his 2000 dissertation at the University of California, Irvine. The current approach to embeddings was set by word2vec, published by Tomas Mikolov's team at Google in 2013, which showed that words can be represented as dense vectors while preserving semantic relationships. Access to large models via a cloud interface became mainstream on 11 June 2020, when OpenAI opened the API to GPT-3 on a "text in — text out" basis. These three pillars — the exchange standard, vector representations and a model API — form the foundation of the service.

Why you can trust this to us

The combined experience of our development team is more than 45 years in IT, and that means the habit of bringing a system to the state where it works under load, not just in a demo. We work as engineers: we fix the requirements, assemble the loop, run it on test data and edge cases, and only then bring it into production mode. Where AI integrations touch equipment, we separately check the control commands on a bench, because there's no other way to be responsible for the result on the client's real hardware. We don't manufacture machines and don't print parts ourselves — we write the program and configure the equipment the client already has, and we're responsible for the correctness of that part. This order removes the risk of silent errors and makes the launch controllable.