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- urgentAcme Corp 06:14
- routed#ops-intake 06:11
- picking upIntake form 06:09
- queuedvendor@… 06:02
- queued#client-acme 05:58
Operations, handled.
Gridex turns work demand into AI capacity. We build and operate AI systems that absorb recurring work across intake, review, follow-up, and system updates—so your team gets capacity, not another platform to manage.
No new platform to adopt Runs across your existing stack Operated by Gridex, reviewed by your team
Where Gridex Sits
Your business runs as it does. Gridex picks up the streams that should not stay on your team.
Existing tools, channels, and processes keep operating. Gridex moves into specific streams of work without asking your team to adopt new software, learn new tools, or change how the work arrives.
Common Starting Points
Most teams arrive in one of four shapes.
One Workflow Run
The work just gets handled — inside the software your team already uses.
Gridex operates the apps you already pay for. You don't open a dashboard, assign tasks, or learn new tools. The result shows up where it should.
Output Artifacts
What lands in your team's hands.
Intake Brief
Sent when a new lead arrives.
Review Packet
Queued when your team needs to approve.
Weekly Report
Summary of the week, every Monday morning.
How we work
Research
How Gridex thinks about AI work in production.
Field notes on workflow design, deployment risk, infrastructure choices, and the controls that make AI-run work usable inside real operations.
Guides Guides Guides
The Workflow Gap: Why AI Implementations Stall Before They Start
Why most enterprise AI projects fail despite better, cheaper models. The bottleneck isn't technology. It's the design work that turns AI capabilities into functioning business workflows.
AI at Work: The Gap Between What Companies Deploy and What Employees Use
Enterprise AI deployments are producing shelf software while employees use personal AI accounts that leak company data into uncontrolled channels. The missing layer isn't better AI — it's workflow design.
Local LLM vs Cloud API: A Decision Framework for Self-Hosted, Cloud, and Hybrid LLM Inference
When to run LLMs locally, when to use a cloud API, and when to go hybrid — a decision framework across cost, compliance, capability, and latency.