Our Approach
Tyber.io is not a generic AI consultancy. We are production engineers who know where AI pays off — and where it does not. We focus on the boring-but-critical layer where AI meets uptime: code review automation, intelligent monitoring, operations bots, RAG over corporate knowledge, and delivery pipelines augmented by intelligence. Every recommendation we make is rooted in systems we have actually run, not hypotheticals from a slide deck. We work hands-on, ship in iterations, and document decisions so the work can survive after we leave.
Postgraduate Credentials
Postgraduate specialization in Applied AI Engineering (Cogna/Anhanguera, 360 hours) — a 12-discipline program covering multi-agent systems, MCP, RAG, fine-tuning, AI for DevOps and AI governance. Faculty includes Google Developer Experts, Microsoft MVPs and Node.js core team members.
Areas of Expertise
AI for DevOps
IaC copilots, GitOps with AI, deploy bots, K8s agents, AIOps and FinOps with intelligent forecasting
DevOps & SRE in Production
9+ years leading infrastructure, reliability and incident management for high-traffic environments
Software Engineering & Systems Architecture
12+ years designing, building and shipping software across web, services, infra and platform
Generative AI APIs & Prompt Engineering
OpenAI, Anthropic, Google Gemini, Hugging Face — chaining, templates, cost-aware orchestration
AI-Driven UX & Static Site Pipelines
Text-to-UI tooling, AI-augmented content pipelines, static site automation with image generation
LLM Foundations
Transformers, embeddings, attention, vector databases, semantic search applied to real systems
MCP — Model Context Protocol
Building and consuming MCP servers, exposing internal services as AI-ready tools, security and governance
Autonomous AI Agents
ReAct, Plan-and-Execute and Reflection patterns, multi-agent systems, LangGraph orchestration
AI for Project Management
Requirements copilots, intelligent backlog prioritization, automation across Jira, Slack and similar
AI-First Architecture
Single and multi-agent design patterns, RAG variants, model routing and tiering, enterprise AI stacks
Fine-Tuning & PEFT
Dataset preparation, LoRA, parameter-efficient fine-tuning, evaluation and domain-specific models
AI Security & Governance
LGPD-aware AI, risk management, interpretability, guardrails, human-in-the-loop and audit trails
Infrastructure We Operate
Real production environments, not slideware. Cloud-agnostic by design — we pick what fits the workload, not what's on the brochure.
Clouds
- AWS (EC2, ECS, Aurora, S3, CloudFront, Lambda)
- Google Cloud (GKE, GCE, Cloud SQL, Cloud Run)
- Azure (AKS, App Service, Storage)
- DigitalOcean (Droplets, Managed DBs, Spaces, App Platform)
- Hetzner Cloud (high-perf bare metal + cloud)
- Cloudflare (Workers, R2, D1, Pages, Tunnel)
Containers & Orchestration
- Docker / Docker Compose
- Kubernetes (vanilla, GKE, EKS, AKS)
- Helm charts + Kustomize
- Argo CD (GitOps deploys)
- k3s / k3d (edge & lightweight)
- Container registries (GHCR, ECR, GCR, Docker Hub)
Infrastructure as Code
- Terraform / OpenTofu
- Pulumi (TypeScript/Python)
- Ansible (config management)
- Cloudflare Wrangler (Workers + DNS)
- AWS CDK (when teams prefer it)
- AI-augmented IaC (Claude / GPT review of changes)
Edge & CDN
- Cloudflare Workers + R2 (single Worker, multi-tenant routing)
- Cloudflare Pages (300+ POPs static delivery)
- Vercel / Netlify / AWS Amplify (when fit)
- Fastly / Varnish 7.x containerized
- Wildcard DNS + auto-TLS (Let's Encrypt)
- Edge auth (Cloudflare Access, signed cookies)
Observability Stack
- Grafana + Prometheus + Loki + Tempo (self-hosted reference)
- OpenTelemetry instrumentation
- Zabbix (infra monitoring at scale)
- Alertmanager + Telegram / Slack on-call
- Datadog / Grafana Cloud (when managed fits)
- Custom dashboards with AI-summarized incident timelines
Security & Hardening
- Cloudflare Access + WAF + Bot Management
- fail2ban + UFW + TLS hardening
- 1Password CLI for secret injection (zero-token-on-disk patterns)
- Audit trails immutable (DB triggers, single-shot revert)
- OAuth 2.0 / SAML / Cloudflare Tunnel for zero-trust access
- LGPD / GDPR-aware data design
Tools & Stack
The actual tooling I work with daily — across AI, DevOps, QA, SEO and automation. The same set of capabilities a small agency would need, orchestrated by one Technical Lead with AI doing the heavy lifting.
AI Models & LLMs
- Claude Opus (deep reasoning, code)
- Claude Sonnet (balanced, daily driver)
- Claude Haiku (fast, structured outputs)
- Claude Code (CLI agent)
- OpenAI GPT-4o / GPT-5
- OpenAI Codex (programmatic code generation)
- Google Gemini 2.5 Pro / Flash
- Perplexity (research with citations)
- Hugging Face open-source models
Image Generation
- Flux Pro / Schnell (Replicate)
- Gemini 2.5 Flash Image (Nano Banana)
- Gemini Imagen
- DALL-E 3
- Midjourney (concept exploration)
- Stable Diffusion (self-hosted)
- AI image refinement & multi-turn editing
AI for DevOps & SRE
- AI-powered code review (Claude / GPT)
- AI-augmented Terraform / Pulumi (IaC copilots)
- Lighthouse CI quality gates
- Intelligent log analysis (Loki, OpenSearch + LLM summaries)
- AIOps anomaly detection (Prophet, Isolation Forest)
- Auto-remediation pipelines with human-in-the-loop guardrails
- MCP servers exposing internal services to LLMs
Automation Platforms
- N8N (self-hosted workflow automation)
- GitHub Actions (CI/CD + scheduled jobs)
- Cloudflare Workers (edge automation)
- Custom Telegram / Discord / Slack bots
- BullMQ + Redis (job queues)
- Zapier / Make.com (when justified)
- Webhooks + event-driven orchestration
QA & Testing with AI
- Playwright with AI-assisted test generation
- Lighthouse CI for performance/a11y/SEO gates
- Visual regression with AI diff
- AI-powered E2E test maintenance
- Test coverage analysis with LLM-driven prioritization
SEO & Content with AI
- DataForSEO (SERP, audit, keywords)
- Google Search Console + AI analysis
- AI-augmented content pipelines (Perplexity → GPT → Claude → Image)
- Schema.org JSON-LD structured data
- Programmatic SEO (mass content generation with quality gates)
- Multi-language content with shared slugs (EN / PT)
Infrastructure & Observability
- AWS (EC2, RDS Aurora, ECS, S3, CloudFront)
- DigitalOcean (Droplets, Managed DBs, Spaces)
- Cloudflare (Workers, R2, Tunnel, WAF, CDN)
- Docker / Docker Compose
- Zabbix + Grafana + Prometheus + Loki
- Varnish 7.x containerized
- Postgres + Redis + Vector databases (pgvector, Qdrant)
Languages & Frameworks
- TypeScript / JavaScript / Node.js
- Next.js 15 (App Router, static export)
- PHP / WordPress (mature operations)
- Python (data + AI scripting)
- Bash (orchestration glue)
- SQL (Postgres, MySQL, Aurora)
What We Deliver
AI-Augmented DevOps
IaC copilots, GitOps with AI, deploy bots and Kubernetes agents — automation that pays for itself in incidents prevented and engineer hours saved.
Intelligent Observability
Stacks combining Zabbix, Grafana and Prometheus with anomaly detection, predictive alerting and AI-summarized incident timelines.
Operations Automation
ChatOps and multi-channel bots (Telegram, Discord, Slack) integrating tickets, deploys and on-call workflows with safe AI assistance.
RAG & Knowledge Systems
Searchable internal runbooks, corporate documentation indexed for semantic retrieval, and AI agents that consult and execute playbooks.
AI Agents Architecture
Single and multi-agent system design with LangGraph, function calling and reflection loops — moving from prompts to dependable workflows.
Auto-Remediation Engineering
Alert → verification → mitigation pipelines with circuit breakers, dry-run validation and human-in-the-loop guardrails on critical actions.
AI Governance & Compliance
LGPD-aware design, audit trails, prompt and decision tracing, content moderation and risk frameworks aligned with regulatory expectations.
Trajectory
Career Begins
Started in web development, taking on the full lifecycle from requirements to production.
Independent Consulting
First experience as an autonomous consultant, delivering end-to-end solutions across multiple industries.
Promoted to Technical Lead
Took the lead in infrastructure and reliability, becoming the escalation point for complex technical and operational issues.
AI Goes Mainstream
Early adopter of large language models in real DevOps workflows from the moment ChatGPT became publicly available.
Tyber.io Lab Is Born
Personal lab launched as a spin-off — multi-plugin Telegram bots, AI-powered code review and intelligent monitoring stacks running 24/7.
From Lab to Platform
Internal stack evolves into a SaaS-grade platform; certified in AI-assisted media production; postgraduate specialization in Applied AI Engineering completed.
Boutique Consultancy
Tyber.io is structured as a boutique consultancy focused on shipping AI into reliable production systems.