Projects
Production-grade platforms built with modern service-oriented architectures. Each project demonstrates end-to-end engineering, from business problem through architecture, development, deployment, and measurable outcomes. I focus on designing independently deployable services that support long-term platform growth.
Lead Capture & Messaging Intelligence Platform
A platform designed to help service-based businesses capture, organize, and act on customer engagement across messaging channels. The platform transforms inbound conversations, customer interest, and engagement signals into structured business data that supports follow-up workflows, lead management, analytics, and future AI-assisted automation. The Messaging Workflow and Demand Intelligence projects below are capabilities within this evolving platform.
Platform roadmap:
AI-assisted lead qualification · Intelligent message categorization · Business intelligence dashboards · Multi-tenant support · Advanced workflow automation · Messaging intelligence and engagement analytics
Messaging Workflow Automation
A service-oriented messaging platform that converts inbound SMS into trackable engagement events with full attribution and lead capture workflows.
Demand Intelligence Engine
An event-driven demand capture pipeline that transforms customer engagement into structured, offering-level demand forecasting data for business decision support.
Authentication Platform
A modular authentication service with JWT-based sessions, secure credential handling, and a PostgreSQL-backed user model designed for horizontal extensibility.
Messaging Workflow Automation
Converting inbound messaging into measurable, attributable engagement with full event-driven processing.
Problem
Businesses investing in social media engagement had no reliable way to capture, attribute, or measure the downstream impact of customer interactions. Engagement lived on third-party platforms with no structured data, no funnel visibility, and no owned communication channel for follow-up.
Architecture
- Inbound SMS webhook service to parse keywords and route to campaign-specific workflows.
- Event-driven redirect service that logs every interaction as an immutable, timestamped event.
- Unique user tracking with normalized identifiers for reliable funnel metrics.
- Consent capture service with auditable timestamps and campaign association.
- Relational schema designed for downstream reporting queries (CTR, opt-in rate, unique engagement).
Data Flow
Social Post → SMS Keyword → Webhook Service (log inbound event) → Auto-reply with branded link → Redirect Service (log click event) → Landing Page → Consent Capture → Analytics Aggregation
Every interaction is treated as an event, enabling the business to measure complete funnels instead of relying on platform-level vanity metrics.
Technology Stack
- Frontend: Astro landing pages (static, fast, SEO-optimized)
- Backend: Node.js + Express webhook and redirect services
- Database: PostgreSQL event schema (campaigns, users, events, consent)
- Integration: Twilio SMS API for inbound/outbound messaging
- Deployment: Docker containers on Render with environment-based configuration
Quality & Reliability
The primary risk is inaccurate attribution and corrupted event data. Testing focuses on data integrity and end-to-end workflow validation:
- Webhook payload validation (missing fields, malformed inputs, unknown keywords)
- Redirect logging verification (campaign association, timestamps, idempotency)
- Duplicate event prevention (repeated user interactions, spam scenarios)
- Consent enforcement (explicit opt-in, auditable timestamps, opt-out support)
- Integration testing: simulate full SMS to consent flow end-to-end
- Data integrity checks: unique counts, foreign key constraints, query correctness
Outcome
- Created an owned audience channel (opt-in list) replacing dependency on ephemeral social platform reach.
- Enabled measurable campaign performance with unique user tracking, click-through rates, and opt-in conversion.
- Provided structured data foundation for data-driven follow-up workflows and targeted promotions.
Demand Intelligence Engine
Demand capture that transforms customer interest into structured forecasting data.
Problem
Businesses making resource and capacity decisions had no structured way to gauge customer demand before committing capital. Interest signals were scattered across social platforms with no connection to specific offerings, and business decisions relied on intuition rather than behavioral data.
Architecture
- Offering preview pages with offering-level interaction tracking.
- Event ingestion service to capture and store interest signals with timestamps and campaign association.
- Consent capture with auditable timestamps and campaign relationship tracking.
- Aggregation endpoints for demand reporting: top offerings, unique interest, opt-in conversion rates.
- Relational schema designed for fast analytical queries across offerings, interest signals, campaigns, and users.
Data Flow
Social Channel → SMS Trigger → Offering Preview Page → Interest Event Logged → Consent Capture → Demand Aggregation Service → Business Decisions (resource planning, promotions)
Customer interest is treated as a structured demand signal connected to an owned messaging channel, enabling data-driven business and marketing decisions.
Technology Stack
- Frontend: Astro offering preview pages with lightweight event tracking
- Backend: Node.js event ingestion service + analytics aggregation endpoints
- Database: PostgreSQL schema (campaigns, offerings, engagement_interest, consent)
- APIs: RESTful endpoints for event capture and demand reporting
- Deployment: Docker containers on Render
Quality & Reliability
The primary risk is misleading analytics from incorrect aggregation, duplicate events, or inaccurate unique user counts. Testing targets data correctness:
- Click event capture integrity (offering association, timestamps, campaign relationship)
- Duplicate and rapid-click handling (throttling rules, idempotency)
- Consent edge cases (explicit opt-in, auditable timestamps, opt-out support)
- Aggregation query verification (top offerings, unique users, conversion rates)
- Integration testing: full SMS to analytics pipeline end-to-end
- Load simulation: high-volume event ingestion and aggregation stability
Outcome
- Enabled offering-level demand forecasting before resource commitment, reducing capital risk.
- Provided structured data for prioritizing high-interest offerings in launch decisions.
- Connected demand signals to owned messaging channels for targeted follow-up promotions.
Authentication Platform
A modular authentication service designed as independently deployable platform infrastructure. Built to demonstrate clean API design, secure credential handling, and extensibility for role-based access control across service-oriented architectures.
Problem
Modern platforms require secure, reusable authentication infrastructure that can serve multiple services independently. This project establishes a production-ready authentication foundation designed for horizontal scaling and integration across service-oriented platform architectures.
Technology Stack
- Frontend: React with modern component architecture
- Backend: Express.js REST API with JWT authentication
- Database: PostgreSQL with migration-friendly schema design
- Infrastructure: Docker + Docker Compose for containerized development
- Testing: API testing workflows with Postman + structured error handling
Architecture & Outcome
- JWT-based authentication with secure session management patterns
- User registration with input validation and duplicate prevention
- PostgreSQL schema designed for migration-friendly evolution
- Containerized local development environment (Docker Compose)
- RESTful API design with structured error responses
- Extensible foundation for role-based access control and multi-service integration