The stack behind Europe's e-grocer
Real software meets the physical world — warehouse robotics, ML forecasting and an AI-native engineering culture, across 342 active repositories in five markets.
Proud of the leverage, clear-eyed about the misses — and trusting it: some merges already ship on AI review alone.
Sources: GitLab API scan (June 9, 2026) + Mesmer delivery analytics + Datadog DORA (prod deploys)
What we build with
89 technologies across 7 domains, detected from 625 repositories and curated by the engineers who own them. Starred items are the paved road — our company-wide defaults.
Languages
9Two centres of gravity — Python for ML, data and agents; Java for the transactional core. Python is shown as two estates: ML/data and services.
Backend
14Spring Boot on the JVM, FastAPI on Python — integration-tested against real databases, resilient by default.
Frontend
12React everywhere — storefronts, admin tools and the fleet hub, increasingly on Next.js, Vite and Tailwind.
Mobile
6Kotlin-first Android with Compose and KMP, Swift on iOS — plus on-device ML for quality inspection.
Data & Databases
14MySQL and Postgres up front, Snowflake behind, RabbitMQ in between — and DuckDB federating across it all.
AI & ML
21Models in production, agents in the workflow, ML in every forecast — the AI-native layer of the stack.
Infrastructure & DevOps
13GitOps on GKE — 442 repos ship through GitLab CI into ArgoCD canary rollouts, watched by Datadog.
Where software meets the physical world
Groceries are unforgiving — fresh food, tight slots, real robots. These are the problem spaces the stack exists to solve.
Warehouse automation
AutoStore robotic grids across our fulfillment centers. In-house services bridge the WMS to physical robots — decanting, automated picking, conveyor routing and dispatch. A pick-balancing engine uses mixed-integer programming to decide which SKUs move to the grid.
Supply chain optimization
End to end: ML demand forecasting feeds purchase orders feeds supplier delivery reservations. The labour planning engine turns predicted demand curves into optimal shift patterns with linear programming.
Last-mile logistics
Route planning via in-house solver engines, real-time fleet tracking and courier capacity optimization. ML predicts handling times; five markets run different delivery models, from 60-minute express to eco slots.
Payments & fraud
Adyen, PayPal and bank transfers across markets, with real-time fraud scoring built in.
AI-native engineering
MCP is an API surface across 15+ services. Devin, Claude Code and PR Agent are embedded in the daily workflow, and Sandstorm runs agents in isolated sandboxes for operational analysis. 85% of contributions are agentic, and some MRs already merge on AI review alone.
Multi-market architecture
Every service is multi-tenant — isolated data per market with a shared codebase. Regional identity servers, market-specific feature flags and tenant-aware routing are infrastructure primitives, not afterthoughts.
Built by small cells
Product cells own their problem end to end — small enough to move fast, accountable enough to be woken up by what they ship. ML, AI and OR engineers sit inside the cells, not beside them.
The structure is still in motion — some cells are still establishing their North Star because the measurement pipeline isn't there yet. We'd rather tell you that than pretend.
How we structure Rohlik Tech