Artificial Intelligence Solutions

We use and integrate artificial intelligence not because it's trendy. The goal is always the same – clear, practical, and measurable value.

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Situations

When an artificial intelligence solution is needed.

An artificial intelligence solution becomes meaningful when you need not only to transfer data or execute predefined tasks according to clear rules, but also to understand content, assess a situation, or choose the right action.

Information is abundant, but hard to find

Documents, procedures, contracts, and technical information are scattered across different files and systems. Employees know the answer is somewhere, but searching for it takes longer than the actual work.

Each case needs to be evaluated individually

The process repeats, but standard "if-then" rules don't work. You need to read text, compare documents, understand context, or evaluate multiple criteria simultaneously.

Decisions rely on historical data

You have enough sales, orders, usage, or operational data, but forecasts are still prepared manually or based on intuition.

The team constantly performs the same actions

Experienced people spend a lot of time reviewing documents, summarizing information, preparing proposals, or checking responses. AI can prepare the first version, and a person can approve it.

Clients or employees need faster answers

Information exists within the company, but only a few specialists can explain it. The system can provide an answer based on verified sources and show what it's based on.

Deviations are noticed too late

An unusual change in orders, costs, demand, or system usage only becomes apparent in an annual or quarterly report. With AI assistance, deviations can be detected much earlier.

Artificial Intelligence

Solution Types

AI integration into existing systems

Most often, a new separate AI platform is not needed. We integrate artificial intelligence where people already work: in the business management system, customer self-service, CRM, document management, or internal employee system.

Client or project history summary
Draft response, proposal, or project document preparation
Query classification and routing to the appropriate process
Extracting required data from documents
Comparing information against established rules
Suggesting the next action
Explaining data in plain language

Artificial intelligence becomes a system feature, not another separate tool that employees need to remember to open.

Knowledge and document search solutions

We create an internal search or Q&A system based on your company's documents, rules, and other approved information. An employee or client can ask a question in natural language.

Searching related documents
Preparing a clear answer
Citing sources used
Evaluating user access rights
Identifying insufficient information to provide a reliable answer

Such solutions are also called RAG systems. They are suitable for procedures, technical documentation, internal knowledge bases, contract archives, product information, and customer service content.

Data analysis and AI insights

A traditional report shows numbers. AI can help explain what has changed in them, where it's worth paying attention, and what questions should be asked next.

Combining data from multiple sources
Noticing unusual changes and deviations
Segmenting customers, orders, or cases into meaningful groups
Finding recurring patterns
Preparing summaries understandable to management or the team
Data analysis by asking questions in natural language

The most important part is not a convenient answer. The system must clearly show from what data and how it was obtained.

Forecasting solutions

Historical data can help predict likely demand, sales, workload, inventory needs, or other business-critical metrics. We can build forecasts for:

Sales and order volume
Product or service demand
Warehouse inventory needs
Customer churn probability
Work and resource load
Cash flows
Technical equipment maintenance needs
Unusual business changes

Forecast accuracy is evaluated, showing which data has the greatest impact on the result.

Process automation

Simple automation works best when a process can be described with clear rules. AI is needed where each case must first be read, understood, or evaluated. For example, the system can:

Read a received document and determine its type
Extract required data and check what is missing
Compare a proposal, contract, or application against established criteria
Determine the topic, importance, and responsible team for an inquiry
Prepare a draft decision or response
Perform actions in other systems after human approval
Stop the process and hand over to a person when the situation is unclear

The goal is to leave decisions to people that truly require their expertise.

Internal AI Assistants

We create AI assistants for specific employee groups and tasks.

Customer service assistant function
Sales proposal preparation assistant
Technical documentation search
Contract and requirements analysis
Executive data analysis assistant
Employee procedures and rules consultant
Project information summary

The assistant can be integrated into an already-used system and see only the information that a specific employee has the right to see.

Artificial Intelligence Solutions

Frequently Asked Questions

Is a large amount of data necessary for the solution?

Not always. For a knowledge search solution, well-organized documents may be sufficient. For forecasting or building a custom model, you typically need more quality historical data. In the first stage, we evaluate not only the quantity of data but also its structure, accuracy, and relevance to the task at hand.

Will our data be used to train a public model?

It depends on the chosen provider and technical solution. We can build the project using business-oriented cloud services, closed infrastructure, or locally operating models. Data transfer, storage, and usage conditions are agreed upon before selecting the technology.

Can artificial intelligence work within our current system?

Most often, yes. AI can be integrated into customer portals, business management systems, CRM, document management, or other existing environments.

Can AI make decisions without human involvement?

Technically - yes, but it's not necessarily the right approach. The greater the financial, legal, or reputational risk of a decision, the more important human confirmation becomes. The most commonly recommended model is: AI analyzes, provides a recommendation, and prepares an action that is reviewed and approved by a human.

Artificial Intelligence Models

When and which model is best suited.

Multiple models can be used within one system: a more powerful one for complex analysis, a faster and cheaper one for repetitive tasks, and an open model where greater infrastructure control is important.

OpenAI · GPT

Often suitable for universal business solutions requiring strong text understanding, analysis, structured responses, tool usage, or multi-step sequences. Can be a rational choice for AI assistants, document analysis, data interpretation, and more complex system automation.

Anthropic · Claude

Often valued when analyzing large volumes of text, long documents, or substantial amounts of interconnected information. May be suitable for analyzing contracts, technical documentation, requirements, and other lengthy texts, as well as solutions requiring consistent reasoning.

Google · Gemini

A strong choice when a single task requires working not only with text but also with images, audio, video, or large document volumes. May be suitable for document understanding, multimedia information search, and solutions integrated with Google infrastructure.

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