How We Built an Autonomous AI Stack That Works While We Sleep

The Models We Rely On

We use two language models regularly, and each plays a different role.

ChatGPT is the go-to for writing. We use it for blog outlines, SEO content, captions, scripts, and early drafts. It's fast and versatile.

Claude Opus 4.5 handles everything else. That includes internal logic, analysis, long-form documents, technical workflows, coding, and any task where context or reasoning matters. It is highly accurate and incredibly consistent.

ChatGPT is great for outputs. Claude is better for process and structure.

Making Context Work for Us

Context is where most people's AI workflows break down. We use Pinecone as a vector store to manage long-term memory across agents and workflows.

It stores documents, knowledge graphs, notes, and past outputs. That lets our systems remember what has already been done and what matters next.

This eliminates repetition and increases reliability across all tasks.

The Automation Layer

We use n8n to connect everything. It's open source, self-hosted, and highly flexible.

Here's how we use it:

• Automate workflows between models, storage, and endpoints
• Take an input from Slack or Telegram, process it with Claude, and push it into Notion or back into a scheduling tool
• Chain together multi-step logic like parsing PDFs, updating indexes, or syncing databases

We don't use Zapier or Make (although you can). n8n gives us full control and visibility, which is non-negotiable when building autonomous systems.

Our Autonomy Engine: OpenClaw

This is the part that changed everything.

OpenClaw is an open-source autonomous agent OS that runs on our own machines. It listens through messaging apps like Telegram or Slack and executes real tasks in the background, continuously.

It connects to Claude for reasoning. Then it acts, accessing files, running code, writing to logs, making decisions, and checking its own work.

Here's what OpenClaw handles for us:

• Summarising research and documents
• Scheduling and follow-up task generation
• Local automations like content updates, backups, or indexing
• Fetching, processing, and summarizing external content

We talk to it like we would talk to a teammate. It responds with memory and intent. We give it goals, not just prompts.

This is not about quick wins or shortcuts. It's about building real workflows that run without us hovering over.