Systems we've shipped

Real client projects. Real problems solved. From enterprise AI to fullstack platforms to IoT in the field.

IoT & Embedded

Environmental Sensor Network

Arduino-based environmental monitoring network deployed across multiple sites in the Philippines, with cloud data collection and real-time alerting.

Arduino ESP32 MQTT Python Time-Series DB Dashboards

The Challenge

A local environmental organization needed continuous monitoring of air quality, temperature, humidity, and water levels across remote sites in the Philippines. Commercial environmental monitoring stations were too expensive, and the data had to be collected reliably in areas with intermittent connectivity and harsh tropical conditions.

What We Did

  • ✓Arduino + ESP32 sensor nodes: Custom-built weather-resistant sensor units measuring PM2.5, temperature, humidity, barometric pressure, and water level using calibrated industrial-grade sensors
  • ✓MQTT data pipeline: Lightweight publish-subscribe messaging with local buffering for intermittent connectivity, ensuring no data loss during network outages
  • ✓Time-series storage: Optimized for high-frequency sensor data with retention policies and downsampling for long-term trends
  • ✓Real-time dashboards + alerting: Visualization of all sensor data with threshold-based alerts via SMS and email for critical environmental events

What Actually Mattered

The biggest challenge wasn't the sensors or the cloud—it was reliability in the field. Tropical heat, humidity, and typhoon season mean hardware has to survive conditions that destroy consumer electronics. We designed weatherproof enclosures with solar power backup and cellular failover.

The local buffering design was critical. When cellular connectivity drops—which it does regularly in remote areas—sensor nodes store readings locally and sync when the connection returns. No data gaps, no manual intervention needed.

Fullstack & Mobile

Agricultural Cooperative Logistics Platform

Order management and delivery tracking system for a Mindanao agricultural cooperative, connecting smallholder farmers to urban buyers across the Davao region.

React Native Node.js PostgreSQL GCash API SMS Gateway

The Challenge

A Mindanao agricultural cooperative needed to digitize their supply chain—from harvest scheduling and order management to delivery tracking and payment settlement. Their farmers were spread across barangays with varying connectivity, buyers expected real-time order status, and payments had to integrate with GCash and bank transfers. Everything ran on paper and group chats before.

What We Did

  • ✓React Native mobile app: Lightweight app for farmers and drivers with offline-first design—works on low-end Android phones with intermittent signal
  • ✓Order & logistics backend: Node.js API handling order placement, route optimization, and real-time delivery tracking with GPS
  • ✓Payment integration: GCash and bank transfer settlement with automated reconciliation for cooperative bookkeeping
  • ✓SMS fallback notifications: Critical order updates via SMS for areas without reliable mobile data, using local telco gateway

What Actually Mattered

The hardest constraint wasn't technical—it was designing for users who had never used anything beyond Facebook Messenger and GCash. The UI had to be dead simple, work offline, and sync gracefully when connectivity returned. We tested with actual farmers in the field, not in a boardroom.

Keeping costs near zero mattered. We ran the entire backend on a single small cloud instance with PostgreSQL. No Kubernetes, no microservices—just a well-built monolith that handles the cooperative's volume without breaking a sweat.

AI/ML Engineering

Enterprise GenAI Platform

Enterprise generative AI platform with multi-provider orchestration, regulatory compliance, and automated evaluation for a multinational infrastructure operator.

Cloud AI Services Python LLM Orchestration Evaluation Pipelines Tool Integrations

The Challenge

A multinational infrastructure and utilities operator needed to bring generative AI into their operations—but in a way that met regulatory and data privacy requirements, supported multiple model providers, and could be governed and evaluated at enterprise scale. Not a chatbot demo. A production platform that business units could actually build on.

What We Did

  • ✓Cloud AI platform: Enterprise AI infrastructure with centralized model management, fine-tuning pipelines, and governed data access
  • ✓Multi-provider model orchestration: Multiple LLM providers behind a unified API, with routing based on task requirements and cost
  • ✓GenAIOps evaluation framework: Automated quality evaluation, regression testing for prompt changes, performance benchmarking
  • ✓Tool integrations: Connecting LLMs to internal systems via standardized protocols, enabling agentic workflows
  • ✓Zero-trust security & compliance: Data privacy regulation compliant architecture, data residency controls, audit logging

What Actually Mattered

Enterprise GenAI isn't about picking the best model—it's about governance. Which data can flow where, who approved which prompt template, how do you prove to regulators that your AI system meets compliance requirements.

The evaluation framework was critical. Without automated quality checks, every prompt change is a gamble.

DevOps & Security

Financial Transaction Platform Modernization

Container platform modernization and CI/CD pipeline overhaul for a regulated financial institution's core transaction processing.

Container Platform Java Microservices Config Management CI/CD Event Streaming

The Challenge

A regulated financial institution's core transaction processing system—handling millions of operations daily—needed its containerized stack modernized. The existing infrastructure had grown organically, with inconsistent deployment practices, manual configuration management, and aging CI/CD pipelines. In financial services, reliability isn't optional and every change carries regulatory scrutiny.

What We Did

  • ✓Container platform modernization: Standardized orchestration for Java microservices across the transaction stack with consistent deployment patterns
  • ✓Configuration automation: Replacing manual server setup with declarative config management, ensuring consistency across environments
  • ✓CI/CD pipelines: Modernized build and deployment with testing stages, security scanning, and approval gates
  • ✓Event streaming infrastructure: Reliable, ordered message processing for high-volume financial transactions

What Actually Mattered

Financial infrastructure moves slowly for good reason. Every change needs audit trails, rollback plans, and regulatory sign-off. The challenge was implementing changes in an environment where a failed deployment could affect millions of transactions.

We introduced changes incrementally, with extensive testing at each stage. The event streaming layer was particularly sensitive—message ordering and exactly-once delivery aren't negotiable when processing financial transactions.

Fullstack Development

Online Assessment Platform

Online assessment and scoring platform with a modern web frontend, JVM backend services, and container orchestration for an HR technology company.

React JVM IaC Container Orchestration

The Challenge

An HR technology company needed a modern platform for delivering professional assessments at scale. The system had to handle sensitive candidate data, provide a smooth experience for test-takers, and support complex scoring algorithms—all running on reliable infrastructure with zero-downtime deployments.

What We Did

  • ✓Server-rendered frontend: Modern web UI for assessment delivery, optimized for accessibility and cross-device compatibility
  • ✓JVM backend services: High-performance scoring engine and API layer for complex psychometric calculations
  • ✓Cloud infrastructure with IaC: Fully automated provisioning with version-controlled infrastructure definitions
  • ✓Lightweight container orchestration: Zero-downtime deployments, chosen for operational simplicity over heavier alternatives

What Actually Mattered

Assessment data is sensitive—test integrity depends on it. The platform needed strict access controls, audit logging, and data isolation between organizations.

We chose a simpler orchestration tool over Kubernetes. For a team this size, operational simplicity meant they could run the platform themselves without dedicated DevOps staff. The right tool isn't always the most popular one.

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