AI Automation Platform for Business Operations
Full-stack app (Python, React.js, Node.js) integrating third-party LLM APIs to automate multi-step business workflows, with a Node.js orchestration layer handling workflow state and persistence.
Technologies
Engineering Focus
Engineered through the complete development lifecycle: requirements analysis, database normalization (3NF), modular service architecture, concurrency control, and systematic testing.
01. The Real-World Problem Space
Small and mid-sized enterprises lose valuable time executing repetitive multi-step operational workflows such as document routing, client communication, and record synchronization.
02. Strategic Engineering Approach
Delivered the feature end to end — React.js interface through to the database layer — integrating LLM APIs and a robust Node.js orchestration engine.
System Architecture & Service Flow
Trigger Ingestion → Node.js State Machine → LLM Task Decomposition → Tool/API Dispatch → Audited State Persistence
Workflow Definition & Trigger
Ingests operational documents and incoming webhook events into an asynchronous queue.
Node.js Orchestration Layer
Coordinates multi-step workflow logic, maintains step state, and enforces retry boundaries.
LLM API Integration
Prompts third-party foundation models with strict JSON schema constraints for automated classification and generation.
Persistence & Audit Trail
Records transaction logs to MySQL and dispatches status updates to the interactive React.js interface.
Technical Challenges Overcome
- •Enforcing deterministic responses and schema compliance from external non-deterministic LLM APIs.
- •Building a reliable resumption mechanism for multi-step workflows during intermittent network latency.
Demonstrated Outcome & Verification
Ranked Top 10 Finalist at XYPERIA Hackathon 2026 and presented the finished system to a panel of senior industry engineers.
Future Roadmap
Implementing Model Context Protocol (MCP) integrations and connectors for popular CRM and ERP platforms.