Our process
How we work
We run focused R&D sprints that move from discovery through architecture validation to production deployment. Each phase is designed to reduce risk early and deliver working systems.
Discover
We start by mapping the current data landscape and operational constraints. That means understanding existing pipelines, ingestion patterns, latency requirements, and where the gaps are.
We evaluate system architectures against actual workloads, not theoretical benchmarks. This includes data flow audits, infrastructure reviews, and feasibility assessments for proposed changes.
The output is a concrete technical roadmap with prioritized recommendations and proof-of-concept scope, not a generic strategy deck.
Included in this phase
- Architecture review
- Data flow audits
- Feasibility assessments
- Infrastructure evaluation
- Proof-of-concept scoping
Build
We build in short, focused sprints with working deployments at each milestone. Infrastructure is provisioned with Terraform, pipelines are orchestrated through Kafka and Airflow, and every component is containerized from day one.
Progress is visible through running systems, not status reports. We ship incremental releases, validate against real data, and iterate based on what the system actually does under load.
Architecture decisions are documented alongside the code. We build with reproducibility in mind so that handoffs are clean and the system can be maintained independently.
Included in this phase
- Sprint-based delivery
- Infrastructure as Code
- Pipeline development
- Integration testing
- Architecture documentation
Deliver
Every deployment goes through automated testing against real data volumes before reaching production. We validate correctness, latency, and failure modes, not just happy paths.
Infrastructure is deployed with full observability: metrics, structured logs, and alerting configured from the start. If something breaks at 3am, the system tells you what happened and where.
Post-launch, we provide runbooks and operational documentation so teams can own the system independently. We stay available for support, but the goal is a clean handoff with no ongoing dependency.
Included in this phase
- Load and Integration Testing. Validating against production-scale data volumes, failure scenarios, and real operational conditions before go-live.
- Observability from Day One. Full metrics, logging, and alerting deployed alongside the application. No black boxes in production.
- Clean Handoff. Runbooks, architecture docs, and operational playbooks so your team can own and evolve the system independently.
Our principles - How We Make Decisions
These principles shape how we approach architecture, trade-offs, and delivery. They are practical, not aspirational.
- Production first. Every design decision is evaluated against real operational conditions. If it does not hold up under load, latency, or failure, it does not ship.
- Observability by default. Systems are built to be understood. Metrics, logs, and traces are first-class citizens, not afterthoughts bolted on before launch.
- Reproducibility. Infrastructure, pipelines, and deployments are version-controlled and reproducible. No snowflake environments, no manual steps.
- Incremental delivery. Working software over comprehensive documentation. We ship small, validate early, and course-correct based on real feedback.
- Clean handoffs. We build systems that teams can own independently. Architecture docs, runbooks, and operational context are part of every delivery.
- Honest trade-offs. We name the trade-offs explicitly. Every architecture choice has costs, and we document them so future decisions are informed.
Talk data platforms & AI
Have a data infrastructure challenge or an architecture question? We are happy to talk through it.
Our offices
- SkyAlgorithm Studio
150 00 Prague, Czech Republic