I made a mistake in production: how to communicate and help resolve it
A practical playbook for critical moments: how to contain immediate impact, communicate with facts without panic, and turn incidents into technical maturity.
Focused on real business outcomes.
Working from strategy to execution to lower costs, boost reliability, and simplify complex operations through the Czanix ecosystem.
Focus areas
I work across infrastructure, data and technical leadership. The mix depends on the problem, the team and the stage of the company.
Work in this area
AI agents and automation pipelines integrated with your company’s systems and data. Practical intelligence applied to daily operations, with secure architecture and measurable productivity.
Career track record
Real initiatives and high-complexity systems engineered or led throughout my technical and executive career.
−50%in cloud costs
Architecture review across Azure and GCP, costs split by application and right-sized databases.
Cloud cost (baseline = 100)
National multi-unit food service and retail group with workloads spread across Azure and GCP. The bill did not show what each application cost, which made it impossible to decide where to cut.
Architecture review on both clouds, cost categorization by application and database right-sizing. Financial governance became part of the team’s engineering standard.
50% reduction in cloud costs compared with the period before the project. Absolute values withheld for confidentiality.
Cloud costs only fall sustainably when every application has an owner and cost becomes part of the engineering routine.
99.99%system availability
Unified observability, elimination of single points of failure and automated recovery across plant billing.
System availability
Recurring outages in billing and dispatch services stalled factory floor logistics during peak hours. Incident triage relied on scattered manual server logs.
Deployed unified telemetry (metrics, logs, traces), circuit breakers around ERP integrations, database read replicas, and automated health-check recovery.
95% historical availability over the prior 6 months. Stabilized at 99.99% uptime with a 70% drop in production incidents.
Reliability improves when alerts track real operational bottlenecks before dispatch queues come to a halt.
2 daysdelivery lead time, down from 21 days
Automated CI/CD pipelines, pull-request test gates and reproducible deployments with instant rollback.
Delivery lead time
Manual release windows every three weeks caused merge bottlenecks, production regressions, and hesitation to ship critical improvements.
Standardized CI/CD pipelines with automated quality gates, Terraform infrastructure as code, ephemeral preview environments, and canary releases.
21-day average lead time from commit to production with 30% test coverage on core flows. Cut to 2 days with 85% automated coverage.
Shrinking batch size is the safest way to accelerate product delivery while reducing engineering stress.
R$ 140ksaved every month
Consolidated fragmented pipelines into a Lakehouse architecture, replacing weekly batches with streaming ingestion.
Data infrastructure cost
Clinical and financial reporting took up to seven days to consolidate due to heavy overnight batch jobs and duplicated analytical storage.
Re-architected into Bronze, Silver, and Gold Lakehouse layers with incremental CDC ingestion, smart partitioning, and decommissioning of redundant clusters.
Monthly spend of R$ 320k/month on legacy data infrastructure, reduced to R$ 180k/month with real-time executive dashboards.
Clean data governance lowers storage spend while giving leadership live operational visibility.
Recent articles
Project decisions, operational problems and what I learned while solving them.

A practical playbook for critical moments: how to contain immediate impact, communicate with facts without panic, and turn incidents into technical maturity.

Companies are sitting on mountains of data and doing very little with it. How to turn raw data into real decision leverage over the coming years.

How to choose a process, test an AI agent with oversight, and measure whether it belongs in production.
Simulate separately your direct cloud cost reduction (financial savings) and the recovered engineering capacity (hours redirected to core product).
Cash reduction: 30% scenario on idle resources and overprovisioning
Economic value of technical hours redirected to product (non-cash)
The simulator separates direct cash reduction (30% reference scenario on idle and overprovisioned cloud resources) from recovered operational capacity (hours valued at your team's hourly cost). Total economic impact combines both effects compared against the reference 12-month investment.
* Illustrative simulation based on real diagnostic scenarios. Final outcomes depend on infrastructure and architecture audits.
Direct Triage · Cesar Zanis
Run the quick technical triage to filter your scenario or speak directly with Cesar Zanis on WhatsApp.
No commitment. The first step is understanding the problem.