F FanOn

Remove AI logistics from software development

Build software. FanOn decides where AI should run.

FanOn keeps developers in their existing workflow while choosing the right execution path for each task: local when appropriate, providers only when needed, and every decision visible.

Install FanOn Documentation
Developer-first Privacy-first Transparent execution Local/dev MVP
Developer Existing workflow
FanOn Chooses the execution path
Local When it fits routine, private, close
Provider When called for hard, unavailable, specialized

The fan story

The moment is simple: the fan turns on.

Developers have powerful computers in front of them. Most AI requests still go to the cloud by default.

FanOn exists for the moment when local capacity should help, provider calls should be intentional, and the developer should not have to think about any of it.

Problem / Observation / Belief

AI moved into the IDE. The execution decision did not.

Coding assistants spread fast. Local models became useful. Provider costs and privacy questions became real. But the default path still sends almost everything outward.

Problem Every tool becomes another place to manage models, cost, privacy, and fallback.
Observation Many everyday development tasks do not need a frontier cloud model.
Belief Use local when it is a fit. Use providers when the request or availability calls for it.

Why teams adopt FanOn

Practical outcomes without making developers manage the plumbing.

FanOn is not a generic cost dashboard. It is a way to make better AI execution decisions while preserving the workflow developers already use. FanOn hides AI infrastructure complexity without hiding developer control.

Less waste Use provider calls intentionally.
More privacy Keep more eligible work local or on team-owned capacity.
Better use Put existing developer hardware to work when it is a fit.
More clarity Make AI execution decisions visible instead of implicit.
Same workflow Keep developers in the AI tools they already use.

How FanOn works

A small decision layer between developer tools and AI execution.

FanOn sits between developer intent and AI execution. Developers ask for a capability, FanOn checks what can run locally, and the result stays visible.

1 Ask for a capability

Summarize, review, draft tests, plan a refactor, or explain code.

2 FanOn chooses execution

Eligible work stays local; provider calls are reserved for when they are needed.

3 Visible decisions

Developers keep control and can inspect what happened.

Why FanOn

Local AI needs a product layer, not another pile of configuration.

Local models run the work. Tool protocols connect assistants. FanOn turns those pieces into developer-facing capabilities, readiness checks, policies, metrics, and setup instructions without making every developer become an AI infrastructure operator.

Not just Ollama FanOn organizes local models into capabilities, diagnostics, and workflows.
Not just a protocol FanOn is the execution and capability layer behind connected tools.
Local first Eligible tasks run close to the developer before reaching for cloud providers.
Visible Readiness, usage, and dashboard output make execution decisions inspectable.

Trust

Execution decisions should be visible.

FanOn is developer infrastructure, not employee monitoring. The product is shaped around local-first defaults, transparent execution, and developer control.

Read the trust model

Pilot / status / fit

Useful enough to test. Early enough to shape.

FanOn is a local/dev MVP, actively dogfooded, and not production-ready yet. It is currently being tested through real CLI and local-model workflows. We are looking for design partners dealing with AI provider sprawl, rising AI costs, local model experimentation, or privacy concerns around cloud-by-default AI workflows.

Status Local/dev MVP in learning and validation.
Best fit Engineering managers, staff engineers, platform teams, and AI infrastructure teams.
Workflows Coding assistants, local model experiments, and AI cost optimization.

Design partners

Help shape the layer that should already exist.

We are looking for conversations and feedback from teams who want AI execution to feel simpler, more private, and more intentional.

Join the Design Partner Program

Takes about 2 minutes. This is a design-partner conversation, not a sales process.