Architecting Intelligence: A Deep Dive into My Top 3 Automation Builds

The Difference Between Coding and Engineering

In the current landscape of AI, everyone is a "prompter," but few are builders. There is a massive chasm between generating a script with ChatGPT and engineering a resilient, fault-tolerant system that solves a business problem at scale.

As an AI Automation Engineer, my work isn't just about stringing APIs together. It's about architecture, state management, latency optimization, and user experience. I don't just build software; I build digital employees and intelligent interfaces.

If you are looking to understand the caliber of my work—or why you should trust me with your next automation infrastructure—this post is the evidence. Below, I break down my top three builds, stripping away the marketing fluff to focus on the problem, the technical approach, the result, and the hard-learned lessons.

Build #1: The Recursive Content Orchestrator

Type: Internal Automation System
Stack: n8n, Python (FastAPI), OpenAI GPT-4o, AirTable, Slack Webhooks

The Problem

Content creation for developers is high-friction. I found myself spending 20% of my time writing code and 80% of my time formatting that code for Twitter threads, LinkedIn articles, and blog posts. The context switching was killing my flow. I needed a way to dump raw technical notes and have a system intelligently format, schedule, and distribute them without hallucinating syntax.

The Approach

Most people use a linear automation: Trigger → Generate → Post. This fails because LLMs often miss nuance in technical writing.

I built a Recursive Refinement Agent using n8n.

1. Ingest: Raw notes are dropped into an AirTable Kanban board.
2. Drafting Node: The system analyzes the code snippets using a custom Python script to extract logic, then passes it to GPT-4o to write a draft.
3. Critique Node (The Secret Sauce): A second, separate LLM agent acts as a "Senior Editor." It reads the draft and compares it against a style guide (uploaded as vector embeddings). It rejects the draft if it sounds too robotic.
4. Loop: If rejected, the draft is sent back to the writer node with specific feedback. This loops up to 3 times.
5. Human-in-the-Loop: The final version is sent to Slack with a "Approve" button.

The Result

Outcome: Content production increased by 400% while reducing my manual writing time to 15 minutes per week.

The system now handles cross-platform formatting automatically, converting Markdown for the blog into threaded hooks for Twitter.

The Lesson

Agentic workflows beat linear chains. By implementing a "Critique Node," I reduced the need for human editing by 90%. If you want high-quality AI output, you need AI to check AI.

Build #2: DocuChat – RAG for Legal Documentation

Type: Micro-SaaS / MVP
Stack: Next.js, LangChain, Pinecone, Supabase, Vercel AI SDK

The Problem

A client in the legal tech space approached me with a scalability issue. They had gigabytes of PDF contracts and needed a way to query them. Standard GPT wrappers were failing because the context window was too small for 500-page documents, and the hallucinations were a liability risk.

The Approach

I architected a Retrieval-Augmented Generation (RAG) pipeline with a focus on citation accuracy.

1. Chunking Strategy: Standard chunking cuts off sentences. I wrote a semantic chunker that respects legal paragraph numbering and clauses.
2. Hybrid Search: We used Pinecone for vector search but added a keyword layer (BM25) to ensure specific legal terms weren't lost in semantic translation.
3. Strict Citation: The prompt engineering forced the model to return [Page X, Paragraph Y] citations. If the information wasn't in the context, the model was hard-coded to reply: "Insufficient data."

The Result

The Lesson

Data preprocessing is more important than the model. The success of this build wasn't GPT-4; it was the semantic chunking strategy. When building RAG apps, how you ingest data defines how smart your AI appears.

Build #3: SentimentStream – Real-Time Market Analysis

Type: Data Engineering / Dashboard
Stack: Kafka, Docker, Redis, React, Hugging Face Transformers

The Problem

Crypto and DevTools markets move faster than traditional news. A simple "sentiment analysis" API wasn't enough; I needed to visualize the velocity of sentiment change across Reddit and X (Twitter) in real-time to spot trending developer tools before they blew up.

The Approach

This required a high-throughput architecture. A standard REST API would choke under the firehose of data.

1. The Pipeline: I set up a Kafka producer to ingest stream data from social APIs.
2. The Brain: A worker service pulls messages and runs them through a finetuned BERT model (hosted locally via Docker to save API costs) to score sentiment (-1 to 1).
3. The Cache: Scores are aggregated in Redis for 1-second retrieval.
4. The UI: A React dashboard connects via WebSockets to show a live, ticking graph of sentiment velocity.

The Result

Outcome: The system processes 50 tweets/second with under 200ms latency. It successfully predicted the rise of three major open-source libraries 48 hours before they trended on GitHub.

The Lesson

Local models save margins. By running a small, specialized BERT model instead of calling OpenAI for every tweet, I reduced operational costs by roughly 95%. Not every problem needs an LLM; sometimes you just need good engineering.

Why This Matters to You

I show you these builds not to brag, but to prove a point: Complexity requires craftsmanship.

Many developers can connect an API. But if you are building a product that needs to scale, handle sensitive data, or operate autonomously, you need an engineer who understands the entire stack—from the database schema to the prompt syntax.

My Philosophy

• User First: Automation is useless if the UX is confusing.
• Fail Gracefully: AI is probabilistic. My systems handle errors without crashing.
• Ship Fast, Iterate Faster: I believe in getting a functional MVP into production in days, not months.

Let's Build Something Impossible

You have a vision for an AI tool, a SaaS platform, or an internal automation system. You have the domain expertise, but you need the technical execution partner who can bridge the gap between "idea" and "deployed."

I am currently opening two spots for high-impact collaborations or contract builds for the upcoming quarter.

I am the right fit if:

• You need a custom AI agent that actually works reliably.
• You are building a Micro-SaaS and need a full-stack MVP.
• You want to automate a complex, expensive business workflow.

I am NOT the right fit if:

• You want a $10 Wordpress site.
• You are looking for a "quick fix" without a clear strategy.

If you are ready to build systems that scale, let's look at your architecture.

[Book a 15-Min Discovery Call] or [Email Me Your Specs].