Already on Claude or Copilot? Put an open-source control layer in front.

Northwood routes each request to the best-fit model your policy allows, prices every call, and keeps a record you can audit, then helps move the workloads that shouldn't sit in a third-party tool into a deployment you control.

Learn moreExplore the stack
Starting pointExisting AI tools
OutcomeLower cost, every call on record
MigrationWorkload by workload

Where to draw the line between general-purpose and controlled deployment.

General-purpose AI tools are useful for broad access. Northwood focuses on the production workloads where the bill, the model choice, and the audit trail matter: high-volume inference, repeatable processes, sensitive context, and anything you need to price and prove.

General-purpose AI tools are useful for
·Broad internal access and general Q&A
·Drafting and editing without sensitive context
·Individual productivity workflows
·Public or non-proprietary knowledge tasks
A controlled deployment layer is needed for
High-volume inference where per-token pricing is now a real line item
Repeatable processes that need cost evidence, evals, and an audit trail
Context that cannot appear in third-party logs or training data
Workloads where you need to own the model choice, routing, and deployment

What Northwood helps with

Analyze your current inference spend and where it actually goes
Identify the workloads that should route through the control layer
Define the model policy, routing rules, and provider strategy
Deploy the open-source control layer in front of your traffic
Add cost evidence, evals, and audit logs for production use
Operate and expand the deployment as new workloads are added

Cut the bill and keep a record of every call.

If your team already runs on an enterprise AI platform, Northwood adds an open-source control layer that routes each request to the best-fit model, prices every call, and records the reason, then helps move the workloads that need it into a deployment you own.

Learn moreExplore the stack