AI implementation
Artificial intelligence deployed into your processes. From first idea to operations.
Together we pick the tasks where AI delivers a measurable result, build the solution on your data and connect it to the systems you already use. Models and data stay yours.
What we deliver
Assistants and agents
Chat, voice or e-mail. They answer customers and staff, handle routine and hand complex cases to people.
Document automation
Extraction from invoices, contracts and orders, checks against your systems, hand-off for approval.
Prediction and classification
Models on your data: demand, customer churn, failures, request triage.
When it makes sense
Routine eats people’s time
Hundreds of identical e-mails, documents or queries every month.
You have data but no answers
History in ERP, CRM or production that nobody uses to look ahead.
Customers are waiting
Queries outside office hours, seasonal peaks, queues on the phone line.
You want to start, but safely
Sensitive data, regulation, the need to keep everything in the EU or on-premise.
How we work
Discover
We walk through processes and data, pick the tasks with the highest return and confirm the data is sufficient.
Design
Solution, interfaces, security and price. A fixed scope both sides sign.
Deploy
A pilot on real data, measured against the current state, then production and integration with your systems.
Operate
We track accuracy, retrain, handle changes. With round-the-clock monitoring if you need it.
Questions
How much does AI implementation cost?
Smaller deployments start in the low hundreds of thousands CZK; larger projects run into millions. After the initial consultation you receive a fixed quote with scope and timeline.
Link to this questionWhere does our data go?
Wherever you say. We build in European regions or directly on your infrastructure. For sensitive data we deploy open models in your environment.
Link to this questionWhat if the model gets it wrong?
A person stays in the loop for risky decisions. We measure accuracy continuously and the solution has fallback rules for errors.
Link to this questionWhat data do we need?
It depends on the task; a few months to a year of history is usually enough. If data is missing we start with pre-trained models and collect it in operation.
Link to this questionRelated content
Want to talk it through?
Talk it through with someone who does this. No slides, just specific questions about your environment.
Book a consultation