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About

My professional focus is Data & Applied AI Solutions Architecture: designing data pipelines, integrations and AI-powered automation that connect business requirements to reliable implementations.

Since October 1, 2026, I have been part of the CNX Mobile (CNX Móvel) squad at Claro Brasil as a Data Steward / Data Translation Specialist. My focus is data quality, integrity and traceability: connecting technical information with business needs, documenting definitions and rules, and supporting source reconciliation and validation.

My background in Anti-Fraud, Marketing, Data Engineering and Backend Development shapes how I approach this role. I build pipelines and automation, and help teams understand what the data means and how it supports decisions.

Founder of Datano — a consultancy focused on data solution architecture, data engineering, systems integration and applied AI, using Azure, AWS, Python, SQL and Power BI.

My work combines data governance & quality, AI/LLM agents & MCP tooling, data engineering & ML pipelines, cloud/DevOps & VPS orchestration, Microsoft 365 / SharePoint / Graph API integrations, and network automation (MikroTik, RouterOS, proxies).

Creator of pyaccelerate — a Python acceleration engine with multi-GPU, NPU, IoT, and Kubernetes support — and Animus, a self-hosted AI memory synchronization network.

Currently working on GPU-aware optimization for ollama and contributing to the AI inference ecosystem.

Career at Claro Brasil

Period	Role	Focus
Oct 1, 2026 – Present	Data Steward / Data Translation Specialist — CNX Mobile	Data quality, integrity, traceability, business definitions, metadata, source reconciliation and validation criteria
Dec 2024 – Sep 2026	Marketing Specialist	Data engineering, automated workflows, strategic reporting and business indicators
Oct 2023 – Dec 2024	Senior Marketing Analyst	Process automation, reporting and data solutions for business decisions
May 2022 – Oct 2023	Senior Anti-Fraud Analyst	Fraud-prevention indicators, analysis and operational automation

Across my previous roles at Claro, I reduced data processing time by 97.22% through workflow redesign and automation, built indicators that supported fraud-prevention decisions, and developed BI dashboards and data environments. I bring that engineering experience to my current work connecting data, technology and business teams.

Tech Stack

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Focus Areas

Domain	What I build	Key tech
Data Governance & Quality	Business definitions, data dictionaries, metadata and lineage documentation, source reconciliation, validation and acceptance criteria	SQL, PL/SQL, Python, BI, collaborative documentation
AI / LLM / Agents	Memory sync networks, agent squads, MCP toolkits, local LLM optimization, transcription & diarization	Python, FastAPI, MCP, Ollama, Whisper, SQLite FTS5
Data Engineering & ML	ETL/ELT pipelines, GPU-accelerated analytics, NPS/verbatim classification, data warehouses, visual workflow orchestration	Python, Java, SQL, PL/SQL, CUDA, scikit-learn, KNIME, Pandas
Cloud, DevOps & VPS	Multi-project VPS orchestration, Docker stacks, CI/CD, licensing systems, boot/recovery tools	Docker, Nginx, PostgreSQL, Redis, Linux, PowerShell
Microsoft 365 & SharePoint	Graph API integrations, Power Automate MCP, SharePoint CRUD, mail archiving	TypeScript, PowerShell, Microsoft Graph, SharePoint
Network & Infrastructure	MikroTik/RouterOS automation, load balancers, proxies, hotspot managers, network optimizers	RouterOS Script, Python, Shell, C
Mobile & Embedded	Android toolkit, device rooting, multiboot USB, cross-platform legacy browsers	Python, Kotlin, Swift, Java, JavaScript
ERP & Web	Integrated ERPs, portals, landing pages, Markdown-to-HTML pipelines	Python, TypeScript, JavaScript, HTML/CSS

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Public Projects

Project	Description	Tech
pyaccelerate	High-performance Python acceleration engine -- CPU, multi-GPU, NPU, IoT, K8s, auto-tuning	Python, CUDA, Cython, Rust
ollama-accelerate	Automatic hardware detection and performance optimization for Ollama	Python
python-gpu-statistical-analysis	GPU-accelerated statistical analysis with CuPy/CUDA and NumPy fallback	Python, CUDA
win-inventory-portable	Portable Windows inventory toolkit for collecting and exporting system information	PowerShell
bf6-optimizer	Battlefield 6 performance & network optimization scripts for Windows	PowerShell
CSVToolBox	Toolkit for processing CSV, Excel and tabular formats	Python
CSVSplitter	Split large CSV files preserving charset, separator and formatting	Python
Loader	CLI tool for loading CSV data into Oracle databases	Java
Spooler	Efficient Oracle Database spool execution and CSV report generation	Java
adb-toolkit	Complete Android backup, recovery & transfer with GPU acceleration	Python
IEPortable	Cross-platform legacy IE browser with Wine ActiveX for DVRs/cameras	JavaScript
PyExeBuilder	Simple compiler for Python scripts	Python

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Private Activity

Beyond public repos, I maintain 67 private projects spanning:

Language	Projects
Python	25
TypeScript	7
PowerShell	7
Shell	4
Other	4
HTML	4
JavaScript	3
RouterOS Script	3
Java	2
C#	2
PHP	1
TSQL	1
Go	1
VBA	1
Jupyter Notebook	1
Batchfile	1

Areas include data engineering, web apps, network automation, enterprise integrations, and tooling.

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pyaccelerate

Metric	Value
Version	v0.11.2
Modules	27
Test Files	20
Stars	0
GPU Vendors	NVIDIA, AMD, Intel, ARM
Platforms	Linux, Windows, macOS, Android/Termux, IoT/SBC

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AI & ML Projects

Animus — AI Memory Synchronization Network

Animus is a private, self-hosted memory synchronization network for AI assistants. It collects, indexes, curates, and syncs context across machines through a secured hub:

• Multi-assistant collection: GitHub Copilot Chat and CLI, Claude Code, Antigravity, project instructions, prompts, agent definitions, and curated notes.
• Knowledge retrieval: SQLite FTS5 search across sessions, projects, decisions, and reusable technical context.
• Knowledge OS: Verified material is promoted into a structured Obsidian vault, keeping raw memory separate from curated knowledge.
• Secure synchronization: Signed HTTPS requests, replay protection, peer identity and version visibility through a self-hosted hub.
• Peer lifecycle: Automatic release checks plus idempotent ZIP packages for installation, update, or repair on Linux and Windows.
• MCP and Web: MCP retrieval for compatible assistants and a sanitized Markdown viewer with Mermaid diagram rendering.

The MCP interface makes the network practical day-to-day: assistants can retrieve relevant notes, prior decisions, project conventions, and previous sessions on demand without exposing infrastructure details publicly.

Tech: Python, FastAPI, Pydantic, SQLite FTS5, HTTPS, HMAC-SHA256, Obsidian, Mermaid, Docker, systemd, Windows Task Scheduler, MCP.

Multi-Agent Orchestration Platform

Private platform for coordinating specialist AI squads through reusable workflows, explicit task routing, contextual memory, and Builder/Validator quality gates. Publicly shareable patterns include multi-stage execution, specialist handoffs, and evidence-based validation; agent definitions, infrastructure, and operational data remain private.

NPS Classification Engine

Enterprise feedback classification system for NPS verbatims. It runs an auditable cascade that separates customer feedback from operational boilerplate and classifies each record by product, reason, sub-reason, sentiment, root cause, and recommended action.

Layer	Technique	Role
0	Separator	Extracts customer feedback vs. operator boilerplate
1	Deterministic rules	Priority regex -- fast and auditable
2	Custom ML model	TF-IDF (word + char) + ensemble (LogReg + ComplementNB)
3	Pluggable AI	Low-confidence escalation to LLM (opt-in)
--	Fallback	Manual review when confidence is too low

Key design decisions:

• Confidence measured as margin between top-2 classes: top1 / (top1 + top2).
• Multi-scenario architecture: core/ engine + profiles/ plug-ins; the first profile is verbatim NPS, new domains add new profiles without touching core.
• Status bands: CLASSIFICADO (≥ 0.90), PENDENTE (0.40–0.90), ANÁLISE_MANUAL (< 0.40).

Tech: Python, scikit-learn, pandas, regex-based rules, pluggable LLM layer.

Repository is private and corporate-bound, so only technical approach and public patterns are shared here.

IRobot-Not

Proprietary desktop ETL workflow orchestrator built from scratch with Java 23 + Swing. It provides a visual, graphical interface for designing, scheduling, and executing data pipelines without relying on enterprise ETL suites.

• Visual pipeline designer: drag-and-drop style workflow composition via Swing UI.
• Multi-source connectivity: JDBC integration with Oracle, SQL Server, PostgreSQL, MySQL, and flat files.
• Execution engine: multi-threaded orchestration with ExecutorService, retry logic, error handling, and execution logging.
• Extensible architecture: plugin-friendly transformation steps and configurable pipeline definitions.
• Observability: real-time execution status, logs, and failure diagnostics directly in the UI.

Tech: Java 23, Swing, JDBC, ExecutorService, JSON/YAML, Maven, Oracle, SQL Server, PostgreSQL, MySQL.

Repository is private and proprietary; only the technical approach and public architecture patterns are shared here.

Selected Private Systems (Anonymized)

System	Public architecture summary
Integrated operations ERP	Modular commercial, field-service, project, inventory, finance, HR, accounting, supplier, fleet, and contract workflows
Franchise operations platform	Web portals for operational workflows, feedback, authentication, and structured business data
Secure network operations portal	Controlled remote network management with encrypted connectivity, auditability, and role-based access
Microsoft 365 integration gateway	Application-authenticated automation for mail, files, and collaboration services with least-privilege access
Accessible music technology	Assistive score interpretation combining optical recognition, structured music notation, audio, and accessible interaction

Client identities, domains, hostnames, repositories, environment topology, credentials, and business-specific rules are intentionally omitted.

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Open Source Contributions

Forks & Branches

Fork	Upstream	Branch	Status
ADModule	samratashok/ADModule	feature/ad-query-tool-v2	PR Open
Aios	harshitgavita-07/Aios	feat/sdk-streaming-hardware-aware	PR Merged
kedro	kedro-org/kedro	2973-enable-access-to-omegaconfigloader-dictconfig-instead-of-dict-only, base-0.19.15, chore/reflection_system	--
kedro-mlflow	Galileo-Galilei/kedro-mlflow	645-on_pipeline_error-raises-an-error-when-the-pipeline-fails-and-threadrunner-is-used, dependabot/pip/mlflow-gte-2.7.0-and-lt-4.0.0, dependabot/pip/pytest-cov-gte-2.8.0-and-lt-8.0.0	PR Open
kedro-viz	kedro-org/kedro-viz	2660-metadata-backup, bug/icon-color, chore/backend_v2_strat	PR Open
mcp-server-excel	sbroenne/mcp-server-excel	analytics-data, dependabot/npm_and_yarn/multi-c5ebbb4833, dependabot/npm_and_yarn/vscode-extension/fast-uri-3.1.7	--
mcp-windows	sbroenne/mcp-windows	dependabot/npm_and_yarn/vscode-extension/oxlint-1.82.0, dependabot/npm_and_yarn/vscode-extension/types/node-26.5.1, dependabot/pip/tests/Sbroenne.WindowsMcp.LLM.Tests/anyio-4.15.1	--
ollama	ollama/ollama	api, bmizerany/client-registry, bmizerany/embedspeedup	PR Open
phpmixbill	ibnux/phpmixbill	GuilhermeP96-patch-1, GuilhermeP96-patch-2	--

Pull Requests

PR	Repository	Status	Date
perf: index outgoing edges for transitive traversal	kedro-org/kedro-viz		2026-10-08
Add opt-in synchronous batching for metric histories	Galileo-Galilei/kedro-mlflow		2026-10-08
fix: prevent re.sub backreference corruption in README section updates	GuilhermeP96/GuilhermeP96		2026-03-29
feat: Ollama SDK integration, streaming UI, hardware-aware model selection	harshitgavita-07/Aios		2026-03-27
feat: autotune - automatic hardware-based performance optimization	ollama/ollama		2026-03-26
feat: weekly PyPI download stats via GitHub Actions + README badges	GuilhermeP96/pyaccelerate		2026-03-24
feat: Add comprehensive AD user query tool v2.0	samratashok/ADModule		2026-01-06
Added Brazilian Portuguese language.	GuilhermeP96/phpmixbill		2017-04-03
Brazilian Portuguese language added	GuilhermeP96/phpmixbill		2017-04-03

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GitHub Stats

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