AI readiness assessment
We assess processes, data, systems and team skills. We show what can be automated today and what requires data preparation or integration first.
AI services
AI implementation for business
We take AI implementation for business from the first conversation to a working solution: AI consulting, AI readiness assessment, AI strategy, a Proof of Concept on your data, integration with your systems and team training. Securely, step by step and with measurable results.
Who AI implementation is for and what problem it solves
Generative AI and AI agents are a real competitive advantage today – provided they are implemented well. Many companies start with individual experiments in ChatGPT or Claude but never turn them into lasting change in their processes. What is missing is a plan, knowledge of which use cases will pay off, and confidence that company data will stay secure.
That is why we start AI implementation with the business, not the technology. First we establish where the team loses the most time, which decisions need faster access to data and which processes can be automated. Only then do we choose models, architecture and the way to integrate with the systems the company already uses.
This service is for boards and owners who want to plan their AI investment deliberately, for operations directors looking for process savings, and for IT departments that need a partner to design and build AI solutions compliant with GDPR and the EU AI Act.
Typical situations companies come to us with
- The team is testing AI tools, but without shared rules or measurable results.
- It is unclear which process to start with and what the return on investment will be.
- Data is scattered across ERP, CRM, files and email.
- Questions arise about data security, GDPR and the EU AI Act.
- The cost of using AI models grows faster than the benefits.
AI consulting and implementation – what the service covers
We tailor the scope to your company’s AI maturity. We can run the whole project or step in at a chosen stage – for example, prepare an AI strategy that your own team will execute.
AI strategy for business
We prepare an AI roadmap with priorities, estimated effort and expected benefits (ROI), and set rules for using AI across the organisation.
Model and architecture selection
We choose models (including OpenAI and Anthropic) and how to run them – in the cloud or on your infrastructure – based on data sensitivity and budget.
Proof of Concept
Within weeks we build a working prototype on your data that proves real-world effectiveness before you invest in a full rollout.
Production rollout
We integrate the solution with ERP, CRM, WMS and document workflows, and design permissions, logging and output quality control.
Training and growth
We train your team, monitor AI quality and costs, and roll out further automations according to the agreed strategy.
AI implementation step by step
We work in stages. After each one you know what has been built, what the results are and whether it is worth going further – without long commitments up front.
Free consultation
In 30 minutes we learn about your goals and challenges and point out where AI can bring the greatest benefit.
Consulting, workshop and process audit
We map processes and data, assess organisational readiness and prepare an AI strategy with an estimated return on investment.
Proof of Concept on your data
We build a prototype for a selected process and measure its effectiveness on real examples.
Integration with your systems
We connect the solution to ERP, CRM, WMS and email via APIs or the Model Context Protocol, with permission control.
Rollout, training and optimisation
We launch to production, train users, monitor quality and costs, and plan the next steps.
Technologies
Technologies we build AI solutions on
We use mature models and tools that let us control quality, security and costs. We usually build AI backends in Python and store company knowledge in PostgreSQL with the pgvector extension. We monitor model quality and usage with Langfuse.
- OpenAI
- Anthropic
- Microsoft Agent Framework
- OpenAI Agents SDK
- Model Context Protocol
- Langfuse
- Python
- PostgreSQL + pgvector
- Microsoft Azure
- AWS
- Google Cloud
Example use case
AI implementation at a distribution company
A distribution company receives dozens of orders every day in different formats: emails, PDFs and spreadsheets. Staff retype them into the ERP by hand, and customers call to ask about order status and stock availability.
During the audit we identify two processes with the greatest potential: order processing and answering repetitive customer questions. In the Proof of Concept an AI agent reads orders, matches products from the catalogue and prepares ERP documents for an employee to approve. An AI assistant answers status questions based on system data. Once its effectiveness is confirmed, the solution goes to production and the team is trained to work with AI.
An illustrative scenario showing the typical course and scope of such a project.
Frequently asked questions
Where should AI implementation in a company start?
With a conversation about business goals and a short process audit. This identifies the use cases that will deliver the fastest, measurable results. We start with a free 30-minute consultation, after which we propose next steps.
How long does AI implementation take?
A Proof of Concept for one process usually takes a few weeks. The full rollout depends on the number of integrations and processes involved – after the audit we prepare a schedule split into stages.
How much does AI implementation cost?
It depends on the scope, the number of integrations and security requirements. We prepare a quote after the consultation and analysis, split into stages, so you can start small and grow the solution step by step.
Is company data safe when implementing AI?
We design solutions in line with GDPR and the EU AI Act. We match the architecture to data sensitivity – from cloud models covered by a data processing agreement to models running on your own infrastructure.
Do we need to replace our existing systems?
No. We connect AI to the systems you already use – ERP, CRM, WMS, email or document workflows – via APIs, databases or the Model Context Protocol.
Can you help with AI strategy if our own team does the implementation?
Yes. We can prepare an AI readiness assessment and an AI strategy, then support your team with consulting, training or architecture reviews.
Experience since 2009, now powered by AI
Since 2009
17 years of building enterprise-class web applications, custom business systems and mobile apps.
Industry know-how
Many projects for automotive, logistics, healthcare, food service and catering, construction and finance.
From idea to maintenance
Consulting, design, development, integrations, rollout, training and ongoing growth in one place.
Data security
Solutions designed in line with GDPR and the EU AI Act, with access control and transparent costs.