Three Layers of AI: CorditeOS Executive Summary

Most companies use AI to write code. We use AI to write the code, run the operations, and be the interface.

CorditeOS is an operating system for Amazon sellers — built entirely by one person using AI. Not a prototype. A production multi-tenant SaaS with a lakehouse data pipeline, autonomous AI agents, a conversational assistant, and a dashboard covering every operational surface of an Amazon business.

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The Three Layers

Layer 1: AI builds the system. Claude Code is the engineering team. The entire codebase — dashboard, data pipeline, AI agents, operations engine, cloud infrastructure — lives in a single monorepo designed to be legible to AI. Structured documentation teaches the agent the architecture, conventions, and operating procedures. Custom hooks enforce quality automatically. CI validates every change. Production features ship daily.

Layer 2: AI agents run seller operations. Six specialized agents continuously analyze seller data and act on it. A pricing agent adjusts prices when margins erode. An inventory agent triggers replenishment before stockouts. An ads agent reallocates wasted spend. A listing agent fixes suppressions. A promotions agent recommends economically sound deals. A data agent monitors the pipeline itself.

Every action passes through three layers of guardrails (hardcoded limits, tenant-configurable boundaries, system-wide allowlists), a circuit breaker, and a rollback monitor. Sellers ramp from recommend-only to full autopilot at their own pace.

Beyond reactive fixes, a planning layer handles demand forecasting, replenishment, procurement, and storage optimization — modules that talk to each other across the supply chain.

Layer 3: AI is the interface. The Cordite Assistant is a Claude-powered chat with dozens of tools that query live seller data. P&L, inventory, ads, competitors, strategy briefs — all conversational. It doesn't just present data. It tells you what it means and offers to act on it.

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The Data Foundation

The pipeline ingests from Amazon SP API, Keepa, Catalog APIs, and search query data into an Apache Iceberg lakehouse on Spark Connect. A layered transformation model (raw, staging, dimensions, marts) produces analytical output — full P&L waterfalls, inventory health, ad consolidation, competitor intelligence. Multi-tenancy enforced at every layer with row-level security.

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Why One Person

No alignment meetings. No sprint planning. No translation loss between layers. One mind holds full context across data model, API, UI, and user experience. The system enforces quality through automation. The founder enforces judgment.

The architecture scales without linear headcount. Adding a new agent means defining detection logic, action schema, and guardrails — shared infrastructure handles the rest. Onboarding a new seller means adding credentials and running the pipeline. When the team grows, the harness is already built.

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AI doesn't just write our code. It runs the business.