The same capability keeps being rebuilt.
Local delivery looks fast, but the organization pays for repeated implementation, support, and change.
01 Operator proof + Decision Architecture
Complex systems get expensive before they break.
I help CTOs and engineering leaders fix the ownership and architecture decisions that make data, platform, and AI systems expensive to change.
Verified practitioner evidence
DATAData enablement leadership at Life360
10–20 → 2,000+Routine server-scale range in a system Gevorg created
MILLIONS / SECPeak request rate, with service maintained during an AWS regional outage
02 Where the decision breaks
The architecture may be sound. The decision can still be in the wrong place.
These are not generic transformation symptoms. They are signs that capability boundaries, ownership, or decision rights need to be reviewed.
The platform exists, but teams route around it.
Availability is being mistaken for adoption. The capability contract, owner, or path to value is unclear.
The migration keeps expanding.
A technical change has become an ownership and sequencing problem across teams, dependencies, and incentives.
AI capability has no durable owner.
Models and features are multiplying before the data loop, consumer boundary, evidence, and operating responsibility are settled.
03 Why this judgment
Proof should show the work, not ask for trust.
The commercial offer is new. The practitioner evidence is not. These facts establish relevant operating experience without pretending they are consulting-client outcomes.
Scale with failure in the loop
At Life360, I created a system that routinely scales from 10–20 servers to more than 2,000 and serves millions of requests per second at peak. It remained functional during an AWS regional outage.
Capability, not infrastructure alone
I lead data enablement work focused on making trusted data easier to discover, understand, and use across a large consumer technology platform.
From core system to team ownership
I created the core of a remote-care platform that reached 5,000 daily active patients, then led the full development team.
The Life360 scale figure describes a system Gevorg created. The remote-care figure describes platform adoption. Neither is presented as advisory-client proof.
See the full evidence ledger ↗04 Systems That Scale
Read the decisions in full.
Field notes on decision architecture, data and platform capability, AI operating models, ownership, reversibility, and the economics of technical choices.
05 Paid diagnostic
One decision. Fixed scope. Explicit boundaries.Executive Decision Altitude Review
A focused engagement for one consequential data, platform, distributed-system, or AI capability decision. We make the problem visible, identify the right decision layer and owner, and define a practical sequence for changing it.
- Current-state capability, ownership, and dependency map
- Recommended decision altitude and ownership model
- Capability or consumer contract
- Risks, uncertainties, and sequenced next decisions
Review the engagement ↗Paid and fixed-scope. Fee quoted after a fit conversation.