Stratorys helps product and data teams select, design, and harden analytical backends, data pipelines, and production AI systems using real workloads, reproducible benchmarks, and production code.
Your analytical workload has outgrown the current database.
You are evaluating ClickHouse, DataFusion, or another architecture that will be expensive to replace.
A pipeline works, but cannot be reproduced, resumed, or operated with confidence.
A data or AI prototype must become an inspectable system your team can own.
Focused engagements for teams that already have strong technical expertise but face a difficult limit or decision.
Best for a decision that is expensive to reverse.
Architecture, engine, and technology choices evaluated against the workload and operating constraints.
A defensible recommendation, trade-offs, and action plan.
Explore architecture reviewsBest for a system your team needs to operate.
Implementation, refactoring, and hardening for pipelines, backends, and performance-critical components.
Production code, tests, observability, and handover.
Explore data systems engineeringBest for analytics that no longer keep up.
Engine selection, architecture, migration, and performance guided by real access patterns.
A measured, explainable, and operable backend.
Explore analytical backendsArchitecture, data modelling, ingestion, migration, performance, and operations: we start with your constraints before recommending the engine.
Our software, design studies, and research make the reasoning and trade-offs inspectable.
An implementation of knowledge as code: organizational facts in git, checked by a compiler in CI.
Read the design studyA pull-request triage system that makes routing and prioritization decisions with an auditable reason.
Read the design studyWorkloads before technologies. Benchmarks before assumptions. Handover before dependency.
Make access patterns, volumes, targets, and constraints explicit.
Test assumptions with reproducible measurements.
Document boundaries, trade-offs, and decisions.
Build and harden the selected system.
Leave code, documentation, and operating knowledge behind.
Tell us what the system must do, where it stops scaling, and which decision has become expensive to get wrong.