SYSTEM STATUS: OPERATIONAL

AI Agentic Systems
Architecture.

I replace manual operations with autonomous AI pipelines.

Most tech companies slap a UI on ChatGPT. I build the backend logic. I pull LLMs out of the chatbox, architect complex DAG workflows, and build heavy RAG pipelines.

Case Studies
THE PITCH

Stop scaling headcount. Scale infrastructure.

I pull LLMs out of the chatbox and build the actual backend logic. I architect complex DAG workflows and heavy RAG pipelines using strict, pure functional Node.js. No classes. No OOP bloat. Just stateless logic handling massive WebSocket traffic without dropping connections.

Proof of Work

Case Study: Automating a Recruiting Agency
01/03

The Brain

Designed an autonomous agentic DAG using Mastra and Gemini 1.5 Flash.

MASTRAGEMINI 1.5 FLASHDAG
02/03

The Data

Built a RAG pipeline to dynamically fetch, contextualize, and score candidate profiles.

RAGVECTOR DBEMBEDDINGS
03/03

The Voice

Engineered a sub-800ms voice screening agent using Vapi and WebSockets.

VAPIWEBSOCKETSREAL-TIME
EXECUTION RESULT

The Result: Agent conducts technical screens and fires webhooks with scores directly to the database.

STATUS: ACTIVE

Services

Active Retainers
SERVICE 01RECOMMENDED

AI INFRASTRUCTURE RETAINER

Direct-to-architecture integration for US and EU teams. Zero employment overhead.

SERVICE 02

AUTONOMOUS RAG/DAG PIPELINES

Dynamic data retrieval and workflow execution.

SERVICE 03

REAL-TIME VOICE AGENTS

Sub-800ms inbound/outbound voice infrastructure.

Audit Your Manual Workflows

Send me your most labor-intensive process. I will send you a 90-second Loom architectural teardown detailing exactly how a stateless Node.js pipeline replaces it.