Page
Foundry
We design and build digital systems: architecture, performance, automation, and AI integration. Clean engineering, measurable outcomes, and clear documentation.
What “Systems” means at Gins.pro
Systems is practical engineering: how it’s built, why it works, where it breaks, and how to keep it from breaking.
Not “a set of tools”, but a connected chain: requirements → architecture → implementation → measurement → operations.
When you need it
- Launching a product/service where speed and reliability matter
- Scaling load (LCP/TTFB/CPU/RAM/DB) and needing real profiling
- Infrastructure/deploy chaos: you need to normalize CI/CD and environments
- Process automation: scripts, pipelines, integrations
- AI integration: no magic, with clear boundaries and quality control
What we do
System architecture & design
- decomposition and domain modeling
- service/module boundaries, interfaces, contracts
- choosing storage/queues/caches and consistency strategies
- failover/backup/DR aligned with real-world risk
Performance & observability
- metrics, logs, tracing (what/why/how we measure)
- bottleneck profiling: backend, DB, CDN, frontend
- SLO/SLA thinking: what “good” means and what counts as an incident
- load planning and graceful degradation scenarios
DevOps & operations
- delivery pipelines: from manual to repeatable
- environments, secrets, access control, backups
- pragmatic hardening and baseline security
- documentation: “how to run”, “how to update”, “how to fix”
Automation & integrations
- audit/monitoring scripts, reporting, alerting
- API integrations, ETL/ELT, data pipelines
- rules and tools that actually save time
AI integrations (strictly practical)
- where AI fits: classification, search, assistants, generation
- where AI hurts: critical paths without quality controls
- testing, evaluation, hallucination control, observability
Principles
- Measurable: if you can’t measure it, you can’t improve it.
- Simple on the outside: complexity stays inside; the interface stays clean.
- Documentation is part of the deliverable: not “later”, but built-in.
- No noise: minimal, sufficient tech and decisions.
- Predictable production: deploy, rollback, monitoring, backups — mandatory.
How we work
1) Diagnosis
A short, focused review: goals, constraints, risks, pain points, current state.
2) Plan & architecture
We lock the target design, priorities, success metrics, and delivery stages.
3) Implementation
We ship in iterations. Each step must produce a useful outcome.
4) Production & handover
Observability setup, operating rules, documentation, and transfer.
We don’t sell “perfect architecture”. We build systems that work and hold up in the real world.
Typical outcomes
- The service sustains load and degrades predictably
- Deployments become repeatable; rollbacks become safe
- Metrics tell the truth; alerts stop being noise
- Performance improves: TTFB/LCP/CPU/DB time under control
- Documentation exists and the system runs without “tribal knowledge”
What to explore next
- Work — system projects and implementations: /work/
- Tech — notes on architecture, performance, DevOps: /tech/
- Blog — practical thinking without marketing fog: /blog/
FAQ
How long does it take to “stabilize things”?
It depends on the current state. Often the first meaningful wins show up within the first 1–2 iterations after diagnosis.
Do you work with existing code/infra?
Yes — that’s usually the reality. Improvements without stopping the business.
Can we do an audit only, without implementation?
Yes. Audit + action plan + prioritization + risks/metrics is a separate engagement.
Discuss a project
If you want, I’ll propose 2–3 solution paths with risks and sequencing.
Message me on Telegram or email: /contact/