AI & Data Science that automates work and organizes decisions
We implement AI solutions, LLM integrations, data automation and analytics that shorten teams' work time and help make better decisions.
Professional AI implementations for companies
AI in a company makes sense when it solves a specific problem: it classifies queries, summarizes documents, supports customer service, accelerates data analysis or automates repetitive decisions.
We design AI implementations from the process and data, not from the model itself. We check the quality of sources, risks, security requirements and the way of measuring the effect.
We build integrations with GPT/LLM models, tools for working on documents, recommendation systems, reporting automation and solutions combining AI with CRM, CMS, ERP and helpdesk.
- GPT/LLM integration with company systems, CRM, CMS and knowledge bases
- Chatbots and AI assistants based on company data, not general answers
- Automation of classification, summarization, data extraction and ticket handling
- Data analysis, reporting, dashboards and predictive models
- Secure architecture: permissions, logs, source control and model constraints
- Proof of Concept and MVP AI before larger production implementation
What AI solutions do we create?
We choose the type of implementation depending on the data, process and real business effect you want to achieve.
AI assistants for teams
Tools that help employees answer questions faster, find information in documents, prepare summaries and perform repetitive tasks.
Chatbots and knowledge bases
Chatbots based on documents, FAQ, regulations, offer or technical knowledge of the company. We design source controls, response limits, and handoff scenarios to humans.
Automation of ticket handling
Message classification, prioritization, data extraction, routing to the appropriate department and response proposals. Particularly useful in sales, support and back-office.
Document analysis and OCR
Processing invoices, contracts, forms, offers, CVs and operational documents. We extract data, validate it and transfer it to the company system.
Reporting and BI with AI
Dashboards, automatic summaries, alerts and periodic reports. We combine data from many sources and show conclusions, not just raw tables.
Recommendations and personalization
Recommendation models for e-commerce, content, offers or next sales steps. We help tailor communication to user behavior.
Prediction and scoring
Decision support models: lead scoring, demand forecast, churn risk, customer segmentation or anomaly detection.
AI integration with CRM and CMS
AI as part of the existing system: generating descriptions, tagging, suggestions, summaries of customer history and automatic data completion.
AI and PoC audit
We check where AI makes sense, what data is needed and how to measure the effect. We build proof of concept before you invest in full implementation.
Selected data, portals and automation projects
See projects that can be the basis for AI, automation and working with data.
Would you like to talk about implementing AI in your company?
Go to portfolioDo you want to implement AI in your company's process?
Describe where you waste the most time today: documents, customer service, reports, sales or company knowledge. We will come back with a PoC or MVP proposal.
The form will take you less than 2 minutes.
Models, data and security
Good AI implementation requires source control, data quality and clear constraints, which is why we design solutions with production use in mind.
AI as part of the system, not a separate toy
We combine LLM models with knowledge bases, documents, CRM, CMS and company systems. Depending on the needs, we use RAG, embeddings, task queues, response validation and result control panels.
Risk and quality control
- Limit your response to the indicated sources
- Query logs and the ability to evaluate responses
- Roles, permissions and data separation
- Monitoring costs and API limits
- Quality tests on real examples from the company
What does the AI implementation process look like?
We start with the problem and data, then build small PoCs that can be measured before full implementation.
AI and data analysis workshop
We choose the process that has the greatest potential for automation. We check data sources, document quality, risks and success criteria.
PoC and prototype
We build a small prototype based on real data. We test answers, costs, limitations and ease of use.
Production MVPs
We integrate AI with the company system, add roles, logs, monitoring and exception handling scenarios.
Optimization and development
We measure effects, improve prompts, sources, models and automations. We develop the solution with additional processes.
AI implementation price list
It's safest to start with a limited PoC or MVP that can be quickly measured in a real process.
Audit and PoC AI
Analysis of the process, data and a quick prototype showing whether AI makes real sense in the selected area.
- Workshop and process selection
- Data and risk analysis
- Prototype on sample data
- Report with implementation recommendation
AI MVP for the company
A working AI tool connected to company data, ready for testing in a small team.
- RAG / knowledge base / API integration
- Test panel and query logs
- Roles or access restrictions
- Quality and cost monitoring
Production implementation of AI
A solution integrated with the company's systems, processes and quality monitoring.
- Integration with CRM/CMS/ERP or helpdesk
- Advanced roles, logs and data control
- Automation and task queues
- Maintenance, optimization and development of models
The amounts are indicative. The final quote depends on data quality, integration, security requirements and the scope of automation.
Frequently asked questions about AI & Data Science
We answer the questions that most often arise before implementing AI in a company.
Can AI use our documents?
Yes. Most often, we build solutions based on the company's knowledge base, documents, regulations, FAQ, offers or data from systems.
Is the data safe?
We design access, roles and logs so that the user only sees what he has permissions to see. We select the architecture to meet confidentiality requirements.
Is it worth starting with PoC?
Yes. PoC allows you to quickly check the response quality, costs and real business value without a large investment.
Will AI replace employees?
Most often, AI supports people: shortens search time, prepares sketches, classifies data and automates repetitive tasks.
Will you integrate AI with CRM or email?
Yes. We integrate AI with CRM, CMS, helpdesk, forms, email and databases.
How to measure the effectiveness of AI implementation?
We establish KPIs before the start: time saved, number of automatically classified tickets, quality of responses, service costs or increase in conversions.
Request a free AI implementation consultation
Briefly describe the project and we will come back with a technology recommendation and an indicative quote.
Request a free quoteLet's start cooperation
Fill out the form or write directly. We will analyze your idea and come back with an action plan.