APPLIED SOLUTIONS & CLIENT BUILDS

CASE STUDIES & REFERENCE SYSTEMS

Documented implementations of AI automation pipelines, internal knowledge assistants, and scalable backend services. Each case study details the concrete business problem, what changed, and the operational results.

AutomationReference build

Multi-Channel AI Workflow Automation System

Event-driven business pipeline uniting webhooks, AI document parsing, and instant team notifications

THE PROBLEM:

Operations teams spend dozens of hours every week manually copying leads from intake forms, re-typing invoice attachments, and alerting staff across disconnected spreadsheets and chat tools.

WHAT CHANGED:

Architected an end-to-end event-driven n8n automation pipeline. Incoming webhooks and email attachments are parsed by structured AI extraction models, validated against strict schemas, saved directly into PostgreSQL, and routed with role-specific notifications.

THE RESULT:

Eliminated manual copy-paste handoffs across incoming form inquiries, delivering immediate lead alerts and normalized data records with dead-letter queue recovery for edge-case failures.

System Architecture Footnote:

n8nWebhooksPostgreSQLNode.jsOpenAI APISlack / Email APIs

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AI SystemsReference build

Enterprise Document RAG Knowledge Assistant

Semantic search and grounded question-answering system over private company documentation

THE PROBLEM:

Team members waste hours searching across hundreds of internal PDFs, handbooks, and operational specifications, frequently relying on guesswork or outdated information.

WHAT CHANGED:

Built a Retrieval-Augmented Generation (RAG) assistant that indexes company documents into a vector-enabled PostgreSQL database, retrieves context using hybrid vector-keyword search, and produces cited answers.

THE RESULT:

Staff receive fast, factual answers that cite the exact internal source document, with safety guardrails that refuse to speculate when knowledge base context is insufficient.

System Architecture Footnote:

PythonFastAPIPostgreSQL (pgvector)LangChainOpenAI EmbeddingsDocker

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AI SystemsHackathon Prototype (Proof of Concept)

Klinik — AI Patient Intake Voice Assistant Prototype

Voice-driven patient intake & triage agent prototype built for medical hackathons

Note: Klinik is an experimental hackathon prototype and proof-of-concept demonstration, not a licensed medical device. Latency metrics represent simulated test conditions.

THE PROBLEM:

Emergency room and clinic admissions face bottlenecks during initial intake because intake staff must manually transcribe spoken descriptions into rigid electronic forms while managing patient distress.

WHAT CHANGED:

Developed a hackathon proof-of-concept voice assistant that captures spoken symptom descriptions in real time, parses observations into structured Pydantic schemas, and routes dialogue through a stateful LangGraph machine.

THE RESULT:

Demonstrated functional real-time speech capture and structured intake summaries during live hackathon judging, with deterministic safety guardrails preventing any medical diagnosis.

System Architecture Footnote:

FastAPILangGraphPythonSpeech-to-Text APIsWebSocketsPydantic

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BackendReference build

Scalable REST API & Relational Backend Service

Reliable backend API architecture with relational data modeling, RBAC, and transactional integrity

THE PROBLEM:

Growing applications require secure, auditable, and performant backend services that handle concurrent requests without data race conditions or silent failures.

WHAT CHANGED:

Designed and developed a production-ready RESTful backend service with TypeScript, role-based access control (RBAC), robust input validation, database connection pooling, and structured error logging.

THE RESULT:

Delivered a dependable backend core with zero unhandled database race conditions and clean API documentation ready for client frontend or mobile consumption.

System Architecture Footnote:

Node.jsExpress.jsTypeScriptPostgreSQLKnex / PrismaDocker

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Cloud & DevOpsReference build / Lab

Containerized Cloud Deployment & CI/CD Pipeline

Reproducible container environments, automated testing workflows, and cloud infrastructure setup

THE PROBLEM:

Inconsistent environments between local machines and cloud servers create frequent deployment failures, missing dependencies, and unpredictable downtime.

WHAT CHANGED:

Built multi-stage Dockerized environments for backend services, automated test runners with GitHub Actions, and authored declarative Terraform configurations for reproducible cloud resource provisioning.

THE RESULT:

Achieved predictable one-command deployments with lean production container images and automated pull-request validation.

System Architecture Footnote:

DockerDocker ComposeGitHub ActionsLinuxNginxTerraform (Labs)

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