SYSTEM_STATUS: AUTONOMOUS

NEURAL
ORCHESTRATION.

I architect the Central Nervous System of your business. Autonomous Agents and Self-Healing Workflows that bridge the gap between your Raw Data and AI Models.

24/7OPERATION

<200msLATENCY

100%OBSERVABILITY

EXECUTION_LOGLIVE

1.0 // The Automation Stack

COGNITIVE PIPELINES

01 // VECTOR INGESTION

Automating the "Learning" process for AI. I build workflows that watch Google Drive, sanitize documents, compute OpenAI Embeddings, and upsert them into Pinecone without human intervention.

Tech: Pinecone, OpenAI, Vectors

AGENTIC LOGIC

02 // PROTOCOL-BASED AGENTS

Moving beyond simple prompts. I design LangChain Agents with strict "Protocols" (Sales, Support, Lead Capture) ensuring the AI adheres to business rules and uses tools correctly.

Tech: LangChain, Tools, JSON

SYSTEM INTEGRATION

03 // DYNAMIC LOGGING

Every interaction is data. I build self-maintaining logging systems that create new Google Sheets tabs dynamically based on date/config and append structured session logs.

Tech: Google APIs, Webhooks, HTTP

2.0 // Case Studies

PROJECT SYNAPSE

THE LIVING KNOWLEDGE BASE

The Challenge: AI models are useless if their data is old. The client needed a way to "teach" the AI new policies instantly by just dropping a PDF into a folder.

The Architecture: A webhook-triggered flow that lists files from a Google Drive Inbox. Custom JS nodes convert Docs to text while handling PDFs as binaries. Before upserting, we delete old vectors to prevent "Ghost Data".

"I don't just copy files. I manage Vector Lifecycles."

smart_binary_handler.js

if (!item.binary && item.json.data) {
  // Handle Google Doc Export
  const textContent = item.json.data;
  item.binary = {;
    data: Buffer.from(textContent).toString('base64'),
    mimeType: 'text/plain',
    fileName: original.name + '.txt'
  };
} else {
  // Handle Native Binary (PDF/IMG)
  item.binary.data.fileName = original.name;
}
return $input.all();

PROJECT NEXUS

THE OMNI-AGENT BACKEND

The Challenge: Standard chatbots hallucinate. The client needed a bot that acted like a trained Sales Rep.

The Architecture: A LangChain Agent with 4 distinct Protocols (Consult, Product Search, Info, Closing). The agent autonomously decides when to query the Inventory DB or capture lead data into Sheets.

Protocol Enforcement: Strict System Prompts prevent off-topic chatter.
Tool Orchestration: Autonomous JSON-based tool calling.
Hybrid Output: Returns Text + UI JSON for frontend rendering.

USER CHAT

LangChain Agent

Protocol 1: Consult

Protocol 2: Search

Protocol 3: Capture

JSON UI

Inventory DB

Google Sheets

UPWORK_MONITOR_V2|LIVE FEED

CONNECTED

Full Stack AI Developer Needed...MATCH: 98%

Min: $60/hr • Verified • $10k+ Spent

> DETECTED: RAG System Requirement
> ACTION: Generating Proposal (GPT-4o)
> STATUS: DRAFTING...

Simple Scraper Script...MATCH: 12%

> REJECTED: Budget < $500

SCANNED: 1,240 JOBSPROPOSALS SENT: 12

PROJECT UPWORK-AI

THE FREELANCE AUTOPILOT

The Challenge: The best freelance jobs are gone in minutes. Manually refreshing feeds is a losing game. I needed a system to beat standard notifications and draft 6-figure proposals instantly.

The Architecture: A real-time monitor that bypasses RSS for direct GraphQL access. It filters for high-ticket clients (Payment Verified, Spend > $500), then triggers a "Chief Engineer" agent to estimate the work and a "Sales Agent" to write the pitch.

Zero Latency: Detects jobs < 10 seconds after posting via GraphQL.
Smart Estimation: AI analyzes requirements to produce a line-item hour estimate automatically.
Sniper Mode: Sends instant Telegram alerts with "Apply Now" links for 95%+ matches.

PROJECT DATA-SALVAGE

THE OCR RECOVERY ENGINE

The Challenge: A client sat on 10,000+ scanned legacy contracts. They were just images—"Dark Data" invisible to search and unreadable by LLMs. Standard tools crashed on the corrupted file headers.

The Architecture: A brutalist Python pipeline. I use PyMuPDF for bit-level surgery to fix broken XREF tables, then pipe the raw image streams into Tesseract to reconstruct a hidden text layer.

Deep Repair: Rebuilds PDF binary structures that Adobe Reader rejects.
LLM Ready: Converts dead pixels into clean, tokenizable JSON/Text.
Metadata Enrichment: Automatically extracts authors, dates, and titles for DB indexing.

RAW_BINARY_STREAM

%PDF-1.4%
1 0 obj<</Type/Catalog/Pages 2 0 R>>endobj
3 0 obj<</Type/Page/MediaBox[0 0 595 842]
/Contents 4 0 R/Resources<</Font<</F1
5 0 R>>>>>>endobj 4 0 obj
<</Length 44>>stream
BT /F1 12 Tf 100 700 Td (H3ll0 W0rld) Tj ET
NO_EOF_MARKER_FOUND
>> CORRUPT_XREF_TABLE_AT_OFFSET_1024
>> UNREADABLE_BYTE_SEQUENCE...

RECOVERED_DATA

{
  "status": "REPAIRED",
  "meta": {
    "author": "J. Doe",
    "pages": 14,
    "has_ocr": true
  },
  "content_layer": "Reconstructed via Tesseract 5.0..."
}

> FIXED: Broken XREF Table
> INJECTED: Hidden Text Layer

MODE: BIT_LEVEL_SURGERYSALVAGED: 100%

CORRUPT_XML_TREE

<w:document>
  <w:body>ERROR_EOF
  <w:p>
    <w:r>
      <w:t>Contract ID: #992</w:BROKEN>
    </w:r>
  <ERROR_TAG_MISMATCH>
</w:document>

PARSER_ERROR: UNEXPECTED_TOKEN

RECONSTRUCTED_MD

Contract #992

**Status:** Active

**Summary:** Binding agreement between...

> XML_TAGS_STRIPPED
> SEMANTIC_STRUCTURE_PRESERVED

ENGINE: PANDOC_CUSTOM_FILTERINTEGRITY: VALID

PROJECT DOC-SURGEON

THE XML RECONSTRUCTOR

The Challenge: A legal firm had 50,000+ legacy MS Word documents from the 2000s. They were riddled with XML corruption that caused standard Python parsers to panic. The mission? Convert them all to clean Markdown for a RAG knowledge base.

The Architecture: A fault-tolerant conversion pipeline. I bypassed the standard python-docx library and went straight for the XML guts using Pandoc with custom Lua filters to perform "tree surgery" on broken tags.

Speed: Processes 1,000 documents per minute via multiprocessing.
Structure Preservation: Keeps headers, lists, and tables intact while stripping garbage styling.
Universal Format: Outputs semantic Markdown that is instantly readable by any LLM.

LIVE_SESSION: RAG_CORE

PROJECT OMEGA

ENTERPRISE KNOWLEDGE-CORE

The Challenge: Clients needed to query internal PDFs quickly, but standard AI tools hallucinated facts and couldn't access private files. They needed a "Corporate Brain" that only spoke the truth.

The Architecture: A precision RAG pipeline. I orchestrated LangChain to chunk thousands of documents and index them in ChromaDB. When a user asks a question, the system retrieves the exact source paragraphs and forces GPT-4o to answer only using that evidence.

Zero Hallucinations: Grounds every answer in retrieved citations. No creative fiction.
Instant Recall: Reduces research time by 90%, finding needles in 10,000-page haystacks instantly.
Hybrid Search: Combines semantic understanding (vectors) with keyword precision.

Visual Evidence

Production workflows and interfaces running in the wild.

RAG Ingestion Flow

Chat Interface

NEURALORCHESTRATION.