The decisions you can't easily
walk back later.
The wrong data model or an architecture decision made too early can cost months to fix years later. We plan system architecture and database design, including vector databases for AI and search applications, before the first line of application code gets written.
Why Zeitcode for system & DB design
Architecture decisions drawn from real projects with high data volumes and strict requirements for traceability.
Architecture for systems that need to grow
Designing storage and integration systems connected to external third-party systems, built for data volumes that increase substantially over time.
Ready for AI and search applications
Building semantic search over large data volumes with vector databases like Qdrant, for use cases where classic databases hit their limits.
Architecture that's documented and explainable
Clear entity relationships, traceable migrations, and a structure a new team member can understand without weeks of onboarding.
Data that's actually usable for analysis
Data models that don't just serve the application but also enable clean reporting and dashboards, for example with Power BI.
A foundation that grows with you
You don't notice good architecture right away. You notice it two years in, when new requirements land and the system still holds.
Cleanly normalized data models
PostgreSQL and other relational databases with thoughtful normalization, indexing, and constraints that ensure data integrity instead of data chaos.
Embeddings and semantic search planned in
When AI-powered search or recommendations are on the roadmap, the data model is prepared for that from the start instead of being retrofitted painfully later.
Clear interfaces between services
A clean split of which system owns which data and how services talk to each other, so a change in one place doesn't put the whole system at risk.
Performance even as data volume grows
Indexing strategies, partitioning, and caching applied where they actually help, instead of premature optimization in the wrong place.
How your architecture project runs
Four phases, from requirements analysis to documented hand-off.
Phase 1
RequirementsBusiness domain & data flows
- ✓Capture business entities and relationships
- ✓Map data flows between systems
Phase 2
ModelingData model & technology choice
- ✓Combine relational and, where useful, vector databases
- ✓Normalization and indexing planned, not guessed
Phase 3
ValidationPrototype & load testing
- ✓Test critical queries against realistic data volumes
- ✓Catch bottlenecks before going to production
Phase 4
Hand-offDocumentation & team hand-off
- ✓Architecture and data-model documentation
- ✓Hand-off to your development team
Step by step, with clear outcomes per phase.
Voices from architecture projects
Short excerpts from projects where getting the foundation right saved a lot of effort later.
“We spent a lot of time upfront on data modeling and wondered if it was necessary. Two years and three times the data later, that time paid off many times over — the system never needed a fundamental rebuild.”
“We wanted semantic search from day one but didn't know how to integrate it cleanly into our existing data model. The solution fits in seamlessly instead of feeling like a bolted-on afterthought.”
- ✓A data model that still holds up after significant growth
- ✓Clear documentation instead of knowledge trapped in one person's head
- ✓Vector search and a classic database combined cleanly
Questions about system & DB design
Answers on database choice, vector databases, and the typical process.
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