Selected work

Architecture, decisions, and outcomes—not just screenshots.

Our case studies show how a problem becomes an engineered system, with transparent detail about the approach and technology.

AGENTIC / WORKFLOW / 001

Multi-Agent Content Orchestration Pipeline

Challenge

Automating the research, writing, and review process for large-scale content generation.

Approach

Orchestrated CrewAI and Claude to create distinct agent roles (Researcher, Writer, Editor).

Architecture

Stateful agent workflow built with LangGraph for observable, multi-step execution.

Stack

Python · LangGraph · CrewAI · Claude API

Python
SYSTEM / UI / 002

Sanrid Systems Platform Architecture

Challenge

Build a highly performant, SEO-optimized agency website that seamlessly integrates with backend AI services.

Approach

Adopted TanStack Start for unified frontend/backend SSR rendering and strict type safety.

Architecture

Vite + Nitro deployment to Edge networks with an integrated React component library.

Stack

React · TypeScript · TanStack · Nitro

React
TypeScript
APP / AI / 003

Constitutional AI Chat Interface

Challenge

Provide a conversational interface with absolute strict safety guardrails and boundary adherence.

Approach

Implemented a UI clone of ChatGPT backed by constitutional AI principles.

Architecture

React frontend streaming SSE responses from a heavily guarded Python backend.

Stack

Next.js · Python · LLMs · Streaming

Next.js
Python
ENGINEERING / AUTOMATION / 004

N8N Webhook Automation Pipeline

Challenge

Connect disjointed SaaS applications to trigger data synchronization without human intervention.

Approach

Built a visually mapped node workflow to catch webhooks, process JSON data, and update databases.

Architecture

N8N self-hosted instance managing robust retry logic and third-party API rate limits.

Stack

Node.js · N8N · Webhooks · REST APIs

Node.js
KNOWLEDGE / RAG / 005

Enterprise Knowledge Retrieval System

Challenge

Surface specific operational context from thousands of raw internal PDF documents and wikis.

Approach

Implemented an advanced RAG (Retrieval-Augmented Generation) pipeline using semantic vector search.

Architecture

Python microservice generating document embeddings and storing them in a high-speed vector database.

Stack

Python · LangChain · Vector DB · LLMs

Python
CONCEPT SYSTEM / 002

Enterprise knowledge retrieval assistant

Document ingestion, grounded answers, citations, access-aware retrieval, and evaluation.

CONCEPT SYSTEM / 003

Agentic operations workflow

Goal interpretation, tool use, approval gates, execution, and observable outcomes.

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