AI Personal Operating System Developer

I am a Senior B2B SaaS Account Executive based in the UK. I have built the foundation of a personal AI operating system - what I call "The Council" - using Claude, Google Drive, and supporting tools. The system is designed to function like a round table of world-class specialist advisors across every domain of my life: finance, career, relationships, fitness, health, and lifestyle. Every day I sit down with this system and have a conversation about life direction, decisions, and execution. The AI synthesises across all domains and gives me one integrated, expert-level answer. This is not a standard chatbot or productivity tool. It is a structured intelligence layer that knows me deeply, tracks what I say versus what I do, holds me accountable, and gets smarter every session. Think of it as an external brain. The foundation is built. I need a developer to take it significantly further. What has already been built - A General Manager (GM) agent running inside Claude Desktop on Mac, with a detailed system prompt embedding the intellectual frameworks of four specialist advisors: Finance, Career, Relationships, and Fitness - A living identity file (TIM_IDENTITY.md) in Google Drive containing personal context, real priority stack, known behavioural patterns, commitments log, and full session history - Four specialist Google Drive documents (FINANCE, CAREER, RELATIONSHIPS, FITNESS) each containing deep domain context built from targeted research. Specialist personas draw on: MEDDPICC, Challenger, Command of the Message for Career; UK tax planning, behavioural finance, sudden income psychology for Finance; attachment theory, Casey Zander, and Rollo Tomassi for Relationships; hybrid athlete performance, APT correction, and ADHD adherence research for Fitness - A personal finance spreadsheet (Google Sheets) with dynamic monthly income projections, actuals tracking, and 11-month HubSpot compensation modelling including draw schedule, pension, tax, and RSU tracking - Google Drive MCP connected at account level for live read/write to all files - Google Calendar MCP and Gmail MCP connected - Mem0 connected as a local MCP server for persistent memory across sessions (although questions remiain if it is properly connected) - A state management file tracking all architectural decisions, document IDs, and open tasks - Daily session ritual: voice input via Wispr Flow, GM retrieves context, conversation happens, files updated at close The core problems to solve: 1. Latency and token efficiency Every session currently starts with the GM fetching multiple large Google Drive files sequentially. This creates 30-60 second delays before any response. On mobile this is painful. Token costs are high because entire documents are loaded every session regardless of relevance. Need a smarter retrieval architecture - likely chunked retrieval, summarisation layers, or a proper vector store for semantic search. Context window limitations are a real constraint. Any architecture that ignores token economics will degrade quickly. 2. True multi-agent orchestration Currently the GM has one context window and reads specialist files as flat documents. It is a jack-of-all-trades rather than genuinely consulting experts. The ideal architecture is a coordinator/subagent model where the GM routes questions to specialist agents, each running with their own dedicated context and deep persona, receives their responses, and synthesises into one integrated answer. This is a multi-agent orchestration problem - likely LangGraph, CrewAI, or a custom API setup. 3. Proactive alerts and time-awareness The system is entirely reactive. I want: Google Calendar events created automatically when the GM identifies a time-sensitive action, WhatsApp or push notifications for important reminders, scheduled check-ins triggered by dates in the identity file (book physio in 30 days, review pension strategy January 2027, draw cliff warning at month 9, probation gate December 2026), and morning briefings delivered automatically. 4. Continuous passive data capture Currently only deliberate session input enters the memory layer. I want to capture and categorise automatically: WhatsApp conversations (key decisions, commitments, relationship context), voice memos via Plaud device (already confirmed MCP and Zapier integration available), biometric data from WHOOP (daily HRV, strain, sleep feeding the Fitness agent for readiness-based recommendations), financial account data (real-time or daily balance updates from Monzo, Marcus, AJ Bell, Nexo via open banking), and anything else that reveals useful context. Each capture should be automatically categorised by domain and routed to the relevant specialist file or a daily digest. 5. Richer daily session experience I want to sit down daily with the feeling of five expert advisors around a table, each with deep knowledge of their domain and of me personally. The GM should be able to actually query dedicated specialist agents rather than reading flat files. The conversation should feel like a genuine advisory session, not a chatbot. Additional features to build: - HubSpot pipeline bridge: HubSpot does not permit Claude licences for staff. Any HubSpot integration must route through ChatGPT or an indirect bridge - Zapier, Make, or custom connector pulling deal and activity data into a Google Drive summary the Career agent reads. - WhatsApp integration: ability to message the GM directly from WhatsApp and receive responses there. Daily briefings and proactive nudges delivered via WhatsApp. - Visual dashboard: simple web or mobile interface showing current status across all domains, open commitments, streak count, and key upcoming dates. A personal command centre. Relationship CRM layer: lightweight tracking of key people (friends, family, professional contacts, dating) with notes on recent interactions, commitments made, and nudges when relationships have gone quiet. - Session quality scoring: simple mechanism to rate sessions and track whether the system is actually improving decision quality over time. - Personal archetype development: structured self-discovery module building a richer psychological profile from session inputs, surfaced patterns, and deliberate reflection over time. Multi-model flexibility: architecture should not be locked to Claude. Ability to swap in GPT-4, Gemini, or others for specific agents without rebuilding the memory layer. Technical stack currently in use - Claude Desktop (Mac) and Claude API (Sonnet 4.6) - Google Drive, Sheets, Calendar, Gmail (all MCP connected) Mem0 (local MCP server for persistent memory) - Wispr Flow (voice to text) Plaud device (under evaluation for passive capture - MCP confirmed available) - ChatGPT Pro (used in parallel, required for HubSpot bridge) Monzo, Marcus, AJ Bell, Nexo (financial accounts - no current integration) - WHOOP (fitness tracker - no current integration) WhatsApp (no current integration) What I am looking for A developer or small team with experience across: - LLM agent orchestration (LangGraph, AutoGen, CrewAI, or equivalent multi-agent frameworks) - MCP server development and integration Vector stores and RAG architectures (Mem0, Pinecone, Chroma, or equivalent) - Automation platforms (Make.com, n8n, Zapier) for data pipeline work - Google Workspace APIs (Drive, Calendar, Gmail) - Open banking APIs (BankSync, TrueLayer, or Plaid UK) - WhatsApp Business API or Twilio - WHOOP API - Python or Node.js for custom connectors - Prompt engineering at production level - not just writing prompts but designing systems that degrade gracefully and maintain quality over time Experience building personal AI systems, second-brain tools, or life operating systems is a strong advantage. You need to understand that this is a thinking and decision-making tool, not a task manager. Known pitfalls and constraints - Tooling budget: maximum £100/month total across all subscriptions. Any proposed architecture must work within this. Tool selection must be cost-conscious from the start - free tiers, open-source, and self-hosted solutions preferred where they do not compromise reliability. - The memory layer must stay portable and model-agnostic. No vendor lock-in beyond what already exists. - HubSpot does not permit Claude licences for staff. Any HubSpot integration must route through ChatGPT or an indirect bridge. - The system must remain simple enough for a non-engineer to use daily. Complexity in the backend is acceptable. The daily interface must be frictionless. Previous attempts at personal AI systems have most often failed at the habit layer, not the technical layer. Simplicity of daily use is the primary success metric, not feature completeness. - Context window limitations are a real constraint. Any architecture that ignores token economics will degrade quickly. The system handles sensitive personal information across health, finances, and relationships. Data privacy and security must be considered from the start. - This is likely ongoing freelance work, not a single project. Initial scope is architecture review and solving the four core problems above. Longer term there is a full roadmap of features to build as the system matures. To apply Share examples of similar multi-agent or personal AI systems you have built. Describe specifically how you would approach the latency and token efficiency problem - that is the most immediately painful issue and your approach to it will tell me a lot about your thinking.