External expertise, safely deployed.
Build an AI agent once. Point it at real work. Deploy the same method into a client's private context — without your method or their data ever leaking.
The problem
An expert has a method worth paying for. A client has data too private to hand over. Today there's nowhere safe to put the two in the same room — so the expert ships instructions and hopes, or the client ships data and hopes. Both are leaks waiting to happen.
GaugeWright is that room. A controlled boundary runs the method against the data so that neither side has to trust the other — or the runtime.
How it works
A method-owner builds the agent. A context-owner provides the data. GaugeWright runs the two together inside a controlled boundary that neither side can cross.
Build the method
Define an agent — its instructions, skills, and tools — in your own library. Refine it in an edit chat until it does the job.
Place it on the work
Install the agent onto a project and give it tasks. Each run works in an isolated sandbox and hands you a diff to keep or discard.
Deploy without leaking
Package the method and deploy it against a client's private context. The method stays protected; the data stays protected; every release is auditable.
Two sides, one boundary
The expert and the client have opposite fears. The same boundary answers both.
Your method never leaks
Package your agent and deploy it onto a client's work. It runs read-only at their side — your instructions, skills, and prompts are never exported or revealed. Build once, deploy repeatedly, keep your edge.
Your data never leaks
Let an outside expert's agent work your private context without it escaping. Data is admitted by handle, never read or exported without an explicit rule, and fail-closed by default. Every access is on the record.
Why GaugeWright
One substrate that scales from a personal project to a governed, cross-party deployment — governance is added, never re-architected.
Agentic workbench
Point an agent at your files, give it a task, review the diff. It remembers context across turns within a chat — no re-priming.
Package & reuse
Build an agent once and deploy it repeatedly on new contexts without shipping your source or touching theirs.
Strict protection
Method and data are protected resources. Nothing is read, exported, or revealed without explicit admission. Fail-closed by default.
Local-first
Orchestration and your data stay on your machine — nothing is hosted by us to get started, and federation is opt-in. (Agent reasoning uses the LLM provider you configure.)
Audit & rollback
Every run is a commit plus an admitted event. Full history, reversible changes, integrity you can verify.
Progressive governance
The same architecture spans solo work, team collaboration, and governed deployments across machines and parties.
Don't take our word for it
Confidentiality is the whole product, so we show our work. Read how the boundary is enforced — invariants, audit, and threat model.
Security & architecture