SpectraFlow: AI Productivity Assistant
A fluid, intelligent assistant that remembers, plans, and executes across your productivity stack.
- Client:: Power users and productivity professionals (pilot deployment)
- Solution Type: Context-Aware Conversational AI Agent
- Use Case: Automating scheduling, reminders, task planning, and information recall across tools and platforms
Problem
While voice assistants exist, they often lack memory, context persistence, and the ability to perform
complex tasks across multiple apps. Users—especially executives and knowledge workers—need a
human-like assistant that can remember, plan, act, and communicate in natural language.
Objective
To build an AI-powered personal assistant that:
- Remembers context and personal data persistently across sessions
- Automates scheduling, task execution, and routine workflows
- Integrates with productivity tools (calendar, task manager, email, docs)
AI Agent Architecture
1) Input & Memory Preprocessing Layer
- Tools Used: LangChain, FAISS
- User messages and assistant replies are chunked and embedded
- Stored in FAISS with episodic (conversation) and semantic (factual) memory
- Cosine similarity used to dynamically retrieve relevant memory on each query
2) Core Intelligence Layer (RAG + GPT-4)
- Tools Used: LangChain RetrievalChain, GPT-4
- Combines user prompt + memory from FAISS to generate accurate follow-ups
- Example queries:
- “What was the restaurant I liked last week?”
- “Remind me to finish the deck I started Tuesday.”
3) Agentic Task Execution Framework
- Tools Used: LangChain + n8n (workflow automation)
- Planning module parses tasks into sub-actions:
- “Book a table at Theobroma for Saturday, notify John, and add to calendar.”
- Each sub-task routed to specialized tools or agents via Zapier/n8n
- Error handling through retry chains and clarifying responses
4) Output & Post-Processing Layer
- Tools Used:
- Google Calendar API: Event creation & conflict detection
- Todoist API: Task creation & tagging
- Gmail Parser: Email summarization via IMAP + regex/NLP
- Notion & Google Sheets API: Personal log storage, digest generation
- Whisper: (Optional) Voice-to-text conversion for hands-free input
Deployment & Integration
| Category | Details |
|---|---|
| Deployment | Python, LangChain, Docker, n8n |
| Channels/Platforms | Google Calendar, Gmail, Notion, Todoist, Sheets |
| Monitoring Tools | Logs + workflow status via n8n dashboard |
Business Results
| Metric | Result |
|---|---|
| Task Execution Success Rate | >85% end-to-end multi-step task completion |
| Time Saved | \~80% reduction in user effort for repetitive tasks |
| Contextual Recall Accuracy | High precision memory recall across sessions |
| Productivity Uplift | Enables 24x7 AI-assistant behavior for professionals |
Document Types Processed
- Natural language messages (typed or voice)
- Productivity tasks: scheduling, reminders, to-dos, logs
- Email content parsing and summarization
- Voice commands (via Whisper integration)
Tech Stack Summary
| Layer | Tools Used |
|---|---|
| Input | Whisper (voice), LangChain + FAISS (text memory) |
| Core AI | GPT-4, LangChain RetrievalChain |
| Matching Logic | FAISS vector similarity |
| Output | Google Calendar, Todoist, Gmail Parser, Notion, Sheets APIs |
| Backend | LangChain, Python, n8n |
| Deployment | Docker, API-integrated workflows |
| Monitoring | n8n logs + status dashboards |
Expansion Potential
Following the success of this implementation, the assistant is positioned to support:
- Vertical use cases like AI assistants for sales teams, legal analysts, or personal health trackin
- Voice avatar integration to enable real-time, 24×7 spoken interaction
- CRM and workflow integrations for executive follow-ups and calendar intelligence
- Autonomous planning agents using AutoGen-style multi-agent coordination