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.

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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 more→Explore the stack