OFFERING
AI Orchestrator
One control layer between the developer's coding tools and the models. Routes each task, records usage, applies policy.
Every developer tool is wired to its own model, and the bill grows with adoption. The AI Orchestrator is a common layer between the harnesses developers already use and the models underneath, with one control point for usage visibility, model routing and governance.
Developers keep their workflow. A plugin in each harness syncs the routing policy assigned to the developer, their team or the org, routes each task to the model the policy names, and ships a run record to the governance console.
Inside AI Orchestrator.
Connect
The plugin is installed in VS Code with GitHub Copilot, Claude Code, Antigravity or Codex. No change to the developer workflow; the policy is synced per developer, team or org.
Route
Each task goes to the model the policy names: planning, implementation, review, testing, documentation and debugging can each go to a different model and provider.
Record
Every run is recorded with its harness, team, task, model, provider, tokens and cost. Governed spend shows beside unmanaged spend, and each cost states its basis.
Govern
Approved models per team, budgets and thresholds, versioned policies, and the policy decision captured per run.
How an engagement runs
The steps of the engagement, and how we charge for them.
Study
We install the plugin for one team, record runs for the study window, and report usage by harness, team and task, with the routing policy we would publish and its modelled saving.
Setup
We stand up the governance console, connect every harness, publish a routing policy per team with approved models and budgets, and put it into production.
Run
We operate the policy: review usage monthly, publish policy versions, and keep Gemini the default for the tasks it fits.
A fixed-fee study to start, then a setup scoped to your size, then an outcome-based monthly run, priced against the savings.