Nasdaq-listed · 20,000+ specialists · ~€3B revenue
Nordic technology services
Enterprise AI R&D, customer discovery and solution architecture across the Nordics and Baltics.
Applied AI consulting & leadership
Decide.
Build.
Operationalise.
BRAIN helps organisations turn consequential AI decisions into systems their teams can run - from first architecture through production handover.
Founder-led consultancyDubai AI Campus, DIFCDirect engagement from decision to handover
Experience shaped inside
Complex organisations.
Practical AI work.
Operating context behind BRAIN’s judgment: engineering, delivery and customer-facing architecture inside organisations where AI had to work with real systems, budgets and teams.
4,000+ person engineering hub · major telecom group
European telecommunications
AI infrastructure, incident anomaly detection, automotive soft sensors and technical direction.
Silicon Valley-founded · Hitachi group · 30,000+ specialists
Digital product engineering
Deep-learning R&D across healthcare, automotive and media, including enterprise-facing prototypes.
High-growth operator · ~€20M to ~€35M revenue during tenure
European e-commerce
Pricing, forecasting, anomaly detection and commercial ML. Company growth shown as operating context, not attributed impact.
01 / What BRAIN delivers
Senior judgment that stays through delivery.
AI programmes usually fail between the strategy and the operating reality. BRAIN works across that gap: clarifying the decision, shaping the architecture, directing implementation and preparing the client team to own what follows.
Decide what is worth building
Turn an opportunity, operating problem or investment question into a defensible technical direction - before committing a team and budget.
Discovery · technical diligence · build versus buy · roadmap
Design the system
Define the architecture, data flow, model strategy, evaluation and operating constraints needed to move beyond a promising demonstration.
Solution architecture · PoC design · evaluation · risk
Lead the implementation
Give engineering and architecture teams senior technical direction while keeping executives and stakeholders aligned with what delivery requires.
Technical leadership · delivery oversight · team enablement
Transfer ownership
Leave behind a production path, documentation and a team able to maintain and extend the system without permanent consultant dependency.
MLOps · workshops · documentation · handover
02 / Industry range
Different sectors.
The same hard questions.
AI systems cross sector boundaries.
These are sectors already worked in, not the limit of BRAIN’s scope. The domain changes; the delivery constraints repeat: data quality, integration, evaluation, operating cost, security and team ownership.
01
Government & infrastructure
Construction data, public systems and delivery across multiple technical stakeholders.
02
Telecom & cloud
Anomaly detection, cloud-edge systems, AI infrastructure and workload orchestration.
03
Retail & commerce
Pricing, forecasting, recommendations, production models and company-wide MLOps.
04
Industry & healthcare
Incident resolution, regulatory knowledge systems, computer vision and real-time inference.
03 / Selected work
Real systems.
Real constraints.
Representative engagements across research, public infrastructure and enterprise delivery. The common thread is turning ambiguity into an architecture and an accountable path to production.
Pan-European cloud-edge programme
IPCEI-CIS · sustainability workstream
Lower-carbon workload placement across cloud and edge.
A year-long IPCEI-CIS engagement to design and build a multi-agent recommender that reasons over carbon intensity, infrastructure capacity and placement constraints across a distributed European compute environment.
AI architecture · multi-agent design · implementation · workshops in Germany · documented handover
Public construction-data platform
Public sector · construction and infrastructure
Turning complex infrastructure data into a deliverable ML programme.
Customer discovery and solution architecture translated a public-sector construction need into an R&D plan that could be delivered across the client, technical partner and AI team.
Consultation · technical pre-sales · solution architecture · R&D direction
Enterprise retail ML platform
Retail · enterprise analytics
A company-wide MLOps environment built for internal ownership.
Cloud infrastructure and production ML models delivered alongside the retailer’s analytics team, followed by onsite workshops so the internal team could operate and extend the environment.
Cloud MLOps · production models · customer workshops · knowledge transfer
Semiconductor incident resolution
Semiconductors · enterprise operations
Machine learning applied to recurring software incidents.
An automated ML workflow designed to classify and accelerate resolution of enterprise software incidents in a large US semiconductor environment.
Problem framing · ML pipeline · enterprise integration
Reusable enterprise GenAI platform
Automotive · electronics · enterprise functions
A faster route from GenAI idea to production decision.
A containerised, API-first foundation for enterprise GenAI proofs of concept, reused across automotive, electronics and internal business use cases, with several continuing toward production.
Platform architecture · RAG · reusable services · production planning
04 / Ways to engage
Bring in the right level of ownership.
BRAIN is a founder-led, Dubai-registered consultancy. Clients work directly with Michal throughout the engagement.
Technical advisory
For founders, boards, investors and technical leaders facing an important AI decision.
Architecture review, technical diligence, model and vendor evaluation, roadmap and hiring input.
Fractional AI leadership
For organisations that need senior ownership before -or instead of- a permanent leadership hire.
Technical direction, team enablement, stakeholder alignment and delivery accountability.
Scoped delivery
For a defined opportunity that needs to be tested, designed or moved into production.
Discovery, architecture, working prototype, production plan, documentation and handover.
05 / Founder authority
Engineering depth.
Executive range.
One accountable lead.
BRAIN brings together experience from enterprise engineering, independent delivery and the daily reality of building and operating AI products.
Since 2018
Applied AI in production
Since 2021
Technical and delivery leadership
MSc AI
Cybernetics & Artificial Intelligence · 2020
01
Enterprise foundation
Hands-on ML engineering and data science grew into technical leadership and customer-facing architecture across large organisations.
02
BRAIN delivery
Direct ownership of technical discovery, architecture, implementation direction, stakeholder workshops, documentation and handover.
03
Founder in production
An end-to-end conversational AI product spanning real-time voice and video, retrieval, persistent memory, streaming architecture and GPU inference.
04
AI-native operations
A seven-agent system coordinates specialist AI agents through Telegram across hosted and private infrastructure, selected around latency, cost and data sensitivity.
STARTUP ECOSYSTEM
Active across both sides of the market.
Michal stays active across Dubai’s founder ecosystem and global technology programmes, giving BRAIN direct context for delivering with enterprises and startups alike: Dubai AI Campus, DIFC Innovation Hub, FinTech Hive, Ignyte, Antler and Dubai Founders HQ.
06 / Technical range
The stack follows the system.
Capabilities are selected around the operating problem - not added to a proposal because they are fashionable.
AI product & solution architecture
Problem framing, system design, technical discovery and production planning.
LLM, agent & retrieval systems
Multi-agent workflows, RAG, memory, evaluation and engineering guardrails.
Applied ML & real-time inference
Forecasting, anomaly detection, computer vision and production inference.
Cloud, MLOps & integration
Deployment, observability, model operations and integration across Azure, AWS and GCP.
Representative working set
Python · PyTorch · TensorFlow · LangGraph · RAG and vector search · MLflow · Docker · Kubernetes · Azure · AWS · GCP · MLOps · LLMOps
07 / Start with the real question
What are you trying to decide, build or lead?
Write with the initiative, decision or leadership need.