Case Study
Aiporo - AI Productivity Agent
An autonomous terminal-based AI assistant designed to act as a personal Operating System for productivity. By leveraging Retrieval-Augmented Generation (RAG) and direct API integrations, the agent interprets natural language to manage emails, schedule calendar events, and provide context-aware summaries of personal data.
The Problem
Professional workflows are fragmented across multiple platforms (Gmail, Google Calendar, local docs). Users suffer from context switching fatigue, where the time spent navigating interfaces to find information or perform simple administrative tasks (like "Find a time for a meeting tomorrow") often exceeds the time required for the task itself.
The Challenge
Contextual Accuracy: Standard LLMs cannot "know" a user's specific schedule or previous email threads without a mechanism to feed private, real-time data into the prompt.
Action Reliability: Building a reliable interface that maps vague natural language commands (e.g., "Clean up my inbox") to specific, safe API executions without accidentally deleting critical data.
Data Latency: Processing large volumes of personal text data into vector embeddings and retrieving them in real-time for a smooth terminal experience.
The Solution
RAG Pipeline with Vector Embeddings: Engineered a Retrieval-Augmented Generation system that indexes local documents and recent communications into a vector database, allowing the LLM to query "semantically similar" context before answering.
Tool-Augmented LLM (Agentic AI): Developed a "Function Calling" layer where the AI can autonomously decide to call Google Calendar or Gmail APIs based on the user’s intent.
FastAPI Orchestration: Utilized FastAPI to build a high-performance backend that manages the flow between the user interface, the LLM provider, and the external data sources.
Command Validation Framework: Implemented a validation layer that tested and refined over 50+ natural language commands to ensure 95%+ intent accuracy.
Summary
Built a terminal-based AI assistant that integrates Google APIs for automated event scheduling, email triage, and meeting summaries.
Implemented RAG with vector embeddings, enabling the agent to provide highly accurate, context-aware responses based on private user data.
Reduced manual workflow steps by ~60% for common productivity tasks through autonomous agent execution.
Validated AI workflows across 50+ diverse natural language commands, ensuring robust performance in real-world scenarios