
6
3
5+ years of experience
I am Senior Software Engineer with over 6 years of experience in AI-first architecture, I focus on building autonomous systems that are both scalable and safe. My recent achievements include winning 3rd place at the 4th Global AI Hackathon (organized by MIT Club & Hack-Nation) and 1st place in the Agentic Commerce challenge via LabLab.ai and Google DeepMind. I don't just build models; I architect ecosystems. From leading teams at Icommunix to reduce onboarding time by 40% to prototyping zero-click payment SDKs for AI agents, I thrive at the intersection of technical depth and strategic automation. I am currently researching centralized intelligence for multi-model orchestration to create more resilient, decision-driven AI frameworks.

## 1. Executive Summary & Pitch WorkforceOS is an enterprise-grade, autonomous AI workforce platform that turns a plain-language business description into an operational team of specialized AI employees. It automatically generates custom role architectures, calibrates role-specific KPIs and brand guidelines, ingests business documents into a Knowledge Vault, and executes multi-agent sequential workflows with Human-in-the-Loop governance. ────── ## 2. Problem Statement & Solution ### The Problem • Operational Bottlenecks: Small businesses and enterprises spend dozens of hours manually coordinating tasks across marketing, strategy, technical auditing, and content creation. • Lack of Context & Cohesion: Single-prompt AI tools produce disjointed outputs because individual LLM prompts lack institutional memory, SOP alignment, and role specialization. • No Governance: Unsupervised AI generation risks shipping unverified or low-quality deliverables. ### The Solution (WorkforceOS) • Autonomous Multi-Agent Roster: Generates non-overlapping AI specialists tailored to the specific business model (e.g., Conversion Lead, DevOps Auditor, Growth Strategist). • Sequential Workflow Execution: Chained execution where each AI employee receives prior agent outputs as context to build rich, multi-perspective deliverables. • Knowledge Vault Integration: Directly binds uploaded SOPs, brand guidelines, and product data into agent execution prompts. • Human-in-the-Loop Governance: Supervisors review live agent deliverables and record binding Approve or Reject decisions with feedback before output release.
10 Aug 2026

CipherScan AI is an enterprise-grade fraud investigation and trading intelligence platform that combines deterministic risk scoring with AI-powered analysis. The system detects fraud patterns through baseline analysis and deviation detection, while simultaneously analyzing trading behavior to warn traders about repeating bad patterns based on their own historical data. Key Features: Fraud Investigation: Real-time alert processing with AI-generated investigation narratives, severity classification, and complete audit trails Trading Intelligence: Autonomous behavioral analysis that detects emotional trading patterns and warns traders when they're repeating mistakes from their own trading history Enterprise Security: JWT authentication, AES-256 encryption, role-based access control, and compliance-ready audit logging Premium UI/UX: Noir fintech design with glassmorphism effects and intuitive workflows Technology Stack: Node.js, TypeScript, React, Google Gemini AI, SQLite Business Value: Reduces investigation time from hours to seconds, helps traders learn from their mistakes, and provides complete regulatory compliance with automated audit trails.
7 Feb 2026

OmniAgentPay gives AI agents a complete payment infrastructure layer. Think of it as Stripe for AI agents one SDK that lets any autonomous agent handle money safely and instantly. Traditional blockchain SDKs are built for humans with screens and confirmation buttons. Handing an AI agent a private key is a security disaster waiting to happen hallucinations, prompt injection, or simple logic bugs can drain a treasury in seconds. OmniAgentPay solves this. We wrap Circle's developer-controlled USDC wallets in an atomic Safety Kernel that enforces spending rules before any transaction executes. What agents get: Universal pay() Method: One call intelligently routes through direct USDC transfers, x402 pay-per-use APIs, or cross-chain CCTP transfers automatically. Atomic Spending Guards: Budget limits, rate limits, recipient whitelists, and transaction caps enforced atomically, even under high concurrency. Autonomous Cross-Chain: Full Agent-Side CCTP minting to Arc Testnet and other networks no external relayers required. Zero Blockchain Complexity: No ABIs, no gas math, no private keys. Just pay(). We're not building another agent application. We're building the infrastructure any AI agent can plug into to handle money whether it's LangChain, OmniCoreAgent, or a fully custom agent. Built With: Python, Circle Developer-Controlled Wallets, CCTP V2, x402 Protocol, Arc.
24 Jan 2026