20+ years operator experience
Growth, product strategy, marketing operations, automation, and entrepreneurship.
Growth & Creative Systems Operator
Trent Doney
I build the systems that turn attention into measurable growth.
CMO-scale ecommerce leadership plus hands-on applied AI, automation, and multimodal production. I connect strategy to operating systems that real teams can run, measure, and improve.
Tempe, AZ / Remote
Growth, automation, and AI systems
Founder / Former CMO / Builder
Creative AI Production Reel
1:00 · VFX · Motion · Avatars · UGC$40M+ annual Shopify portfolio
Executive operating context across an eight-store ecommerce portfolio.
5x+ ROAS at $2M+ spend
Performance marketing systems across seven-plus acquisition channels.
47,869 BrainCore facts
Evidence-backed operational memory with 13,465 tracked entities and 265,970 evidence segments.
Operator Profile
AI systems shaped by business judgment.
I build AI systems from the operator's side of the table: infrastructure that has to run, remember, recover, and produce useful work. My background spans growth, ecommerce, performance marketing, automation, product strategy, and entrepreneurship, but the current priority is the local AI operating system running across SynapseGrid Ops.
That system combines an application and automation host, a primary local AI engine, private runbooks, service memory, retrieval, orchestration, agent handoffs, and GPU-backed image and model workflows. It is not a demo stack; it is a working infrastructure layer for building, testing, routing, and improving AI-assisted operations.
Server 1
Primary App Host + Worker Node
- CPU
- AMD Ryzen 9 5900X
- GPU
- RTX 3090 FE 24GB VRAM
- Memory
- 64GB
- Storage
- 3 TB SSD 29 TB archive storage
Runs production apps, automations, worker jobs, media tooling, and always-on services.
Server 2
Primary Local AI Engine
- CPU
- AMD Ryzen Threadripper PRO 9975WX
- GPU
- RTX 6000 Blackwell 96GB VRAM
- Memory
- 128GB
- Storage
- 13 TB SSD 10 TB archive storage
Powers the local LLM stack, image generation, heavy GPU work, and AI production runtime.
Razer Blade Mobile Workstation
Portable AI development, browser automation, client review, Codex control, media ops, and on-the-go orchestration for the local SynapseGrid environment.
- CPU
- Intel Core i9-14900HX 24 cores / 32 threads
- GPU
- RTX 4080 16GB VRAM
- Memory
- 64GB RAM
- Storage
- 1TB SSD
Selected Work
Commercial judgment
and technical range.
I connect growth strategy, operating systems, and new tooling into work a real team can run.
AI-generated likeness · disclosed
Ecommerce leadership
The work behind profitable ecommerce growth
Turnaround work across multi-store Shopify operations, lifecycle, paid acquisition, creative, conversion, and the operating cadence tying them together.
SignalCareer case study · public-safe summary
Explore the record
AI-generated likeness · disclosed
Applied AI systems
Production AI measured by what improves
Scoring, enrichment, filing intelligence, memory, and delivery workflows judged by quality, latency, cost, and reliability.
SignalSystem case study · architecture disclosed selectively
Explore the systems
AI-generated identity character sheet · disclosed
Multimodal production
A creative AI pipeline built for repeatable identity
Reference design, dataset preparation, generation, motion, finishing, and human review organized as one production process.
SignalCreative AI case study · generated work disclosed
Explore the production system
Public Work
The work is visible where the systems live.
github.com/trentdoney
Personal engineering surface for AI infrastructure, memory systems, orchestration tools, and production experiments.
Open GitHubgithub.com/SynapseGrid-Labs
AI infrastructure lab for memory-first agents, orchestration, local AI workflows, and operational systems.
Open SynapseGrid LabsPrivate operating layer
Operational backbone for runbooks, incident memory, service context, retrieval, and repeatable recovery workflows.
Discuss public-safe detailsSelected Impact
Systems work with operational proof attached.
Plus 909 published memories, 1,267 episodes, and 265,970 evidence segments.
Archive-first preservation, extraction, trust classes, and MCP retrieval.
Qwen3-Coder lane with fp8 weights/KV cache in a 96GB VRAM envelope.
Restored execution throughput from 5-7 to 12-18 actions per cycle.
Migrated shared agent infrastructure and wired 60+ agent-environment symlinks.
Improved Recall@10 28.7%, MRR 36.6%, storage 50%, and RSS memory 56%.
Flagship Systems
Production AI infrastructure, not demo theater.
These are not isolated demos. They are connected systems: memory that preserves evidence, orchestration that keeps authority clear, research infrastructure that maps the field, product pipelines that turn signals into workflows, and a private local lab that makes the work repeatable.
BrainCore
Public AI memory infrastructure
Evidence-grounded operational memory for AI agents. BrainCore turns incidents, coding sessions, chats, dashboards, and source changes into queryable memory by archiving artifacts first, extracting facts with provenance, tracking trust and validity, and exposing retrieval through a local MCP-ready layer.
- Facts
- 47,869
- Entities
- 13,465
- Evidence
- 265,970
AgentFanout
Private SynapseGrid Ops orchestration layer
Provider-agnostic routing for bounded multi-agent work. AgentFanout decides when to fan out work, which provider or role should handle it, and when validation is required.
The main session keeps authority over secrets, private tools, git state, destructive actions, and final synthesis. Workers receive bounded packets and return reviewable outputs.
Agent Memory Atlas
Public research corpus
A public map of the agent-memory field: papers, repositories, benchmarks, taxonomies, product docs, protocols, and implementation patterns organized into a structured corpus.
The goal is to help builders compare architectures, understand verification status, and avoid designing memory systems from scattered claims.
Local AI Lab
Private AI runtime and operating vault
Local inference, image generation, LoRA training, OCR, captioning, and long-context coding run beside the private OpsVault layer.
OpsVault preserves infrastructure decisions, service context, incidents, remediations, and searchable project memory so agents recover context instead of rediscovering it.
Creative AI Production
The finished work is visible. The rejected frames are not.
The reel and identity studies are backed by dataset standards, model validation, repeatable production workflows, and human approval before an asset reaches the site.
See the production work
- 01ReferenceIdentity direction
- 02DatasetCuration + captions
- 03ProductionStill + motion
- 04ApprovalHuman review
How I Work
Operator discipline with builder speed.
Strategy is only valuable when it survives contact with execution.
-
01
Find the constraint
Start with the business objective and the failure that keeps getting in its way.
-
02
Design the system
Connect people, data, models, automation, and measurement into one operating plan.
-
03
Build it hands-on
Move from strategy into workflows, prompts, code, tooling, and production.
-
04
Prove the result
Instrument the work, document it, and improve what the evidence says is weak.
The Operator Behind the Systems
Commercial judgment. Technical range.
I’m Trent Doney, a founder and former CMO who builds the systems behind modern growth.
I’ve built and sold a DTC brand, led an eight-store ecommerce portfolio, managed multi-channel performance programs, and shipped AI, automation, memory, and creative production workflows. The value is not any single tool. It is knowing how to turn a business constraint into a system a team can operate, measure, and improve.
See the workstation and systems