Personal Project · 2024-02-10
Multi-platform AI Agent
Voice-first assistant architecture that accepts natural language requests and orchestrates actions across messaging platforms, productivity tools, and APIs — one intent layer, many execution surfaces.
Problem
Most automation tools are channel-specific. Users learn separate interfaces for calendar, email, tasks, and search — and operational assistants in support or financial services need a unified intent layer that works across surfaces.
Who it serves
Individuals and operations teams who need fast, conversational task execution without switching between five apps to plan a day or triage work.
Ownership
Full-stack systems design: input pipelines, intent parsing, tool orchestration, platform adapters, error handling, and extensibility model.
Architecture
- Unified input pipeline — voice and text via WhatsApp, Telegram, and web
- Intent interpretation — LLM-assisted parsing of intent, entities, and required tool actions
- Action orchestration — dispatcher routes tasks to calendar, email, tasks, search, and custom scripts
- Response layer — human-readable confirmations and optional voice responses
Technologies
Python/Node integration adapters · LLM intent parsing · REST APIs · messaging platform webhooks · extensible tool registry
Constraints
- Each platform has different message formats, rate limits, and failure modes
- Multi-step conversations require context management without runaway token cost
- New capabilities must plug in without rewiring the core agent
Outcome
Demonstrates full-stack systems thinking applied to AI products — from interface design to orchestration reliability. The same pattern applies to operational assistants in regulated support and financial services teams.
Related services
AI applications · API integration · workflow automation · solution architecture