Operating Intelligence
Contract-level profitability, governed metrics and decision interfaces.
About
I work between operating context and engineering delivery: defining the problem, structuring the data, choosing the boundary and leaving a system that can be maintained.
01 Introduction
My day-to-day work spans enterprise digital transformation, data engineering, contract profitability, operating analytics and data governance. In parallel, I build local-first AI and agent workflows for real work rather than isolated demonstrations.
The work usually starts with an ambiguous operating problem. I align the language and ownership, create the data and engineering structure, and deliver a system that can be used, reviewed and extended.
I care about maintainability, recoverability and evidence boundaries. Tools change quickly; responsibility for the system does not.
02 Principles
Define the operating question, owner and evidence boundary before selecting tools.
A metric or model output should remain traceable to its source, definition and limits.
Treat data export as an explicit choice, not the default cost of using an AI system.
Prefer recoverable workflows, replaceable tools and structures that survive the demo.
03 Practice
Contract-level profitability, governed metrics and decision interfaces.
Data pipelines, warehouse layers, governance boundaries and implementation sequencing.
On-device inference, model routing, agent workflows and privacy-aware tool orchestration.
Repeatable tasks, failure boundaries and evidence-led selection of AI tools.
CONTACT · SHANGHAI / REMOTE
Send the current context, the constraint that matters and the result you need. I will first determine whether my experience is a good fit.
Start a conversation hello@liorbase.com