SYSTEM / 007
AI Partner Business Management
Centralized data layer with department-specific views and a single AI chat layer for cross-domain queries. Event-driven and cron-based automation.
Context
What this system is
When inventory, HR, and operations data live in silos, cross-domain questions and automation require either brittle point-to-point integrations or a single source of truth with clear access patterns. Ad-hoc integrations do not scale as departments or data volume grow.
Ownership
What I was responsible for
This is a self-directed system — I owned the architecture, engineering, and product decisions end to end.
Architecture · Engineering · Product
The Problem
What made it necessary
When inventory, HR, and operations data live in silos, cross-domain questions and automation require either brittle point-to-point integrations or a single source of truth with clear access patterns.
Architecture
How it's structured
- 01
Centralized data layer with a single schema and department-specific views so one source serves many consumers.
- 02
AI chat layer as a separate service that queries the data layer and executes actions via defined interfaces.
- 03
Persistent context (e.g. session or conversation state) so multi-turn interactions stay coherent without re-fetching everything.
Engineering Decisions
What shaped the system
- 01
Event-driven and cron-based workflows for alerts and reports so heavy work runs off the critical path.
- 02
Modular automation pipelines so new departments or workflows can be added without rewriting core logic.
- 03
Cost-aware design for AI inference (caching, batching, or tiering) where applicable.
Trade-offs
What was optimized for
Centralized data layer favors consistency and a single mental model over department-level autonomy; access control and views enforce boundaries.
Chat-first interface prioritized adoption and flexibility over maximum automation depth in v1.
Outcome
Event-driven and cron-based workflows so the system scales with load without blocking.
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