In-company · live
Training for those planning an AI transformation, and for those delivering it. Taught by people who have planned such transformations and delivered them.
AI is already in the room
of security incidents involved AI used outside IT’s control. A year earlier there were half as many.
IBM, Cost of a Data Breach Report 2026 (602 organisations, 17 industries)
Deliberate and secure
Off the radar: shadow AI
More than two-thirds of the organisations that suffered a breach had no processes in place to limit shadow AI.
IBM, Cost of a Data Breach Report 2026
Offer
Programs designed for different audiences. We start each with a conversation to adapt it to your organisation’s real needs.
A portfolio of AI initiatives, priorities, ROI counted with the full cost of change in the organisation, and the calls: fund it, redesign it or stop it, on your own initiatives.
Live training, in-company · 1 day
See the programFrom how the models work to rollout, including AI-assisted coding, agent system architecture (tool calling, MCP, multi-agent systems), RAG on company data, and criteria for judging quality, cost and risk.
Live training, in-company · 2 days
See the programWhat an Engineering Manager should change in how the team works when engineers use AI and results do not improve: working standards, people, measures of effect, bottlenecks.
Live training, in-company · 6 h + breaks
See the programKeeping AI systems running in production, including measuring quality and catching its decline, safeguards, and predictable cost and response time under real load.
Live training, in-company
Coming soonCompetency map
It spans five layers of an organisation, from infrastructure to the board, and four stages: from data, through information and knowledge, to decisions. The layers depend on each other. A team can build a working agent system, but funding it is decided by the board, which looks at what is invisible from the team level: the cost of changing processes, regulatory risk, and the other initiatives competing for the same budget.
Preparing for the training
We fit the program to your processes, your organisational structure and the level of the participants. Modules get extended where needed.
We get to know your processes, stack, team level and real cases.
The competency map: where you are and where to start.
We tune the modules and emphasis to your specifics.
Live, on your real cases, with a document you can use.
AI investment based on ROI you have counted and assumptions you can test in a pilot, not on optimism.
In the program
You’ll leave with answers to:
Outcome
Priorities, a call on each initiative and a business case in one document, ready to take to the board.
Decisions
Criteria
Format
Live training, in-company
Duration
1 day
Group
your team
Pricing
on scope
From using AI ad hoc to building with LLMs on purpose.
In the program
You’ll leave with answers to:
Outcome
The questions and thresholds you use to judge your own ideas and vendors’ offers: when an LLM, when an agent, when RAG, and what to demand.
Scope
Evaluation criteria
Format
Live training, in-company
Duration
2 days
Group
your team
Pricing
on scope
From measuring the team’s activity to measuring the effect for the recipient.
In the program
You’ll leave with answers to:
Outcome
The team diagnosis, agreed working rules, support for people, measures of effect and one change to make, collected in a single document across the whole programme.
Format
Live training, in-company
Duration
6 h + breaks
Group
Your team
Pricing
scope-based
Who leads
Every one of these stages is led by someone who has been through it themselves. Four perspectives in one team.
AI/ML, data science, production systems
Architecture + production codeCEO of Processifier and assistant professor at Warsaw University of Technology. Former Data Science Manager at mBank and CTO at inhire.io. Combines business practice with deep technical and academic expertise in AI and machine learning.
AI, data, technology strategy
Data & strategyProfessor at Warsaw University of Technology, former Chief Data Scientist at mBank and co-founder of Polidea (acquired by Snowflake). Brings together academic, technological and business perspectives.
AI engineering, data science, product development
Architecture + production codeCo-founder and CTO of Processifier. Over ten years in data science in large organisations, in recent years on agentic systems and production deployments of language models. Lectures on the Big Data postgraduate programme at Warsaw University of Technology.
Transformation, operating model, value and sourcing
Board decisionsOver 20 years at Roche, Citi and Samsung leading technological and organisational transformations, designing operating models, governance and vendor collaboration. Combines executive, transformation, academic and coaching perspectives.
A team whose experience was gained at, among others:
Contact
Tell us who will be in the room and what you want to achieve. We’ll come back with a short diagnosis of your organisation on the competency map: where you are today and which fields to address first.
