We help you make hard architecture decisions before they get expensive.
Stratorys is a system design practice for data-heavy software. We review architecture, select engines, and build when implementation is required. Every recommendation is backed by benchmarks on your data, not opinions.
Most architecture mistakes start with an undefined workload.
When the workload is not written down, the team guesses. The wrong engine gets selected, the wrong boundary gets drawn, and the cost surfaces months later as a re-platform or a rewrite.
We make the workload explicit, test the options, and leave a decision record.
Engagements.
One practice. Two forms of engagement, plus a fixed-scope sprint for AI systems.
Architecture reviews and engine selection
Implementation and hardening
Before building an AI workflow, define the system.
Most AI projects fail because the system around the model was never defined: the workflow is vague, the knowledge is scattered, and nobody can say when the output is wrong.
The AI system design sprint is a fixed-scope consulting engagement that defines the architecture before implementation starts.
Book a sprintAlready building? See the PR Triage system design study.
Knowledge in versioned files.
Most of what a team knows about its data lives in conversations, spreadsheets, and a few people's heads. When they leave, the knowledge leaves with them. We encode that knowledge as schemas, data contracts, transformations, tests, and documentation.
The result is a record that can be reviewed and updated with the code.
View the design studyBuilt on Rust and high-performance data engines.
We favor tools that are fast, predictable, and well-understood, and we go deep rather than wide.
Open source.
Open-source systems you can read, run, and check.
Knowledge as code. Organizational facts live in git and stay true, checked by a compiler in CI.
View on GitHubAutomated pull-request triage that routes, labels, and prioritizes changes, with a reason attached to every decision.
View on GitHubSystems we work on.
Data-intensive systems where architecture is the main constraint.
Observability & telemetry
Incidents where nobody can say which service failed first. We unify logs, metrics, and traces for fast triage and clear ownership.
Analytical & OLAP backends
Analytics that slowed down as data grew. Columnar stores and query engines tuned for interactive speed at volume, with the engine choice backed by workload evidence.
ExploreRealtime ingestion
Field devices on weak networks, data that must arrive exactly once. Resilient pipelines with resume and backpressure built in.
Data governance & compliance
Audits that stall because processing cannot be traced. Service-oriented workflows with auditable ingestion and extraction.
Investigative & graph tooling
Questions that span millions of transactions and entities. Relationship tracing with outputs an analyst can act on.
Security operations
Alerts that outnumber the people reading them. Platforms for triage and response with reliable incident tracking.
Book a review.
Start with the workload, the constraints, and the decision you need to make. You leave the first session with the problem framed and the options on the table.
Questions, answered plainly.
What does Stratorys do?
We review and build data and software systems. Typical work includes query engine selection, architecture reviews, data flows, and system hardening.
What technologies do you specialize in?
Primarily Rust, plus ClickHouse, DataFusion, Ballista, Apache Arrow, Parquet, Kafka, SQL, and observability tooling.
Do you do consulting or hands-on delivery?
Both. Consulting is for reviews and decisions. Delivery is for implementation and hardening with your team.
What do you leave behind?
A decision record, benchmark notes when relevant, an architecture sketch, and a roadmap.
What is "knowledge as code"?
It means keeping operational knowledge in versioned files: schemas, contracts, transformations, tests, and documentation.