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Our work

From receivers to real-time cockpit view.

We build aviation data systems where telemetry, real-time reliability, and production-grade AI converge. Each project is grounded in a real operating constraint, not a hypothetical use case.

Delivery model
Applied R&D sprints
Typical stack
Kafka, Trino, Terraform, Kubernetes
Deployment stance
Edge plus cloud
01

Real-Time Flight Tracking UI

SkyTrace

Cockpit-style flight tracking with replay, traffic context, and a front end designed around telemetry clarity rather than generic dashboard tropes.

Read case study

What ships

  • Live telemetry views with route and traffic context
  • Historical replay for investigation and product iteration
  • Operational UI patterns shaped by aviation workflows

Operational proof

Telemetry UX / Replay / Front-end surface

02

Aviation Data Infrastructure

Telemetry Platform

End-to-end data foundations for ADS-B receivers, weather feeds, stream processing, lakehouse storage, and production observability.

What ships

  • Receiver ingest with validation and buffering
  • Kafka and orchestration layers with replay support
  • TimescaleDB and Trino for operational and analytical access

Operational proof

Ingest / Streaming / Lakehouse / Observability

03

Applied AI for Fleet Reliability

Predictive Maintenance

Operational AI systems that combine telemetry, maintenance history, and monitored ML workflows to surface earlier signal for maintenance planning.

What ships

  • Anomaly detection on sensor and inspection data
  • Decision-support views for maintenance teams
  • Versioned ML workflows with monitoring and drift review

Operational proof

Applied AI / Maintenance support / Reliability

04

Edge to Cloud in One Operating Model

Hybrid-Cloud Infrastructure

Infrastructure as Code, CI/CD, and observability patterns that keep edge and cloud deployments reproducible and supportable.

What ships

  • Terraform-led infrastructure delivery across OCI and AWS
  • Kubernetes orchestration with controlled rollout paths
  • Operational automation and rollback-aware deployment routines

Operational proof

Infra as code / CI-CD / Operability

How we work - Operational proof before theater

The studio approach is to prove the system boundary first, then scale toward product surfaces and applied AI once the data plane is stable.

  • Map the operational constraint. We start with the real system boundary: where events originate, where timing matters, and where human decisions need better signal.
  • Prove the data plane. The first milestone is usually a trustworthy ingest, replay, and storage model that can support product and analytical work without hand-waving.
  • Ship the decision surface. Only after the foundation is stable do we push outward into telemetry UX, automation, and applied AI where it will survive production.

Talk data platforms & AI

Have a data infrastructure challenge or an architecture question? We are happy to talk through it.

Get in touch

Our offices

  • SkyAlgorithm Studio
    150 00 Prague, Czech Republic