Control AI spend and data exposure without rewriting your app.
AIRoute reviews model bills, usage exports, token volumes, latency, errors, retries, workload labels, and sensitivity tags to show where AI spend is over-provisioned and where private data needs stricter routing policy.
Spend by workload
Findings
Overpowered model laneClassification workload should test cheaper routes.
Retry loop spikeTwo API keys generated duplicate spend.
Data exposure holdLegal and finance labels require private-route policy.
The first control report does not need prompts.
Most companies already have enough billing, usage, and workflow metadata to expose obvious waste and data-exposure patterns. AIRoute starts there so legal, security, finance, and engineering can say yes faster.
Provider, model, token count, request count, timestamp, status, latency, and cost.
API key, app, endpoint, team, customer tier, or manually supplied workflow mapping.
Cost by provider, model, team, workflow, time window, retry pattern, and growth rate.
Sensitivity inferred from metadata labels, provider routes, request volume, and policy gaps.
Earn the right to route traffic.
AIRoute should feel like a careful software company, not a risky black box. The sequence moves from metadata audit to observe mode, then benchmark comparisons, then controlled routing only after cost, quality, and sensitivity policy are clear.
Read invoices and usage metadata. Produce a savings map, sensitivity map, and policy gaps.
Add an OpenAI-compatible gateway that records metadata and sensitivity headers while behavior stays unchanged.
Shadow approved samples against cheaper or private routes and measure quality, latency, failure rate, and exposure.
Route only approved workloads with sensitivity rules, provider allowlists, fallbacks, and audit receipts.
See the report a pilot customer would receive.
The sample report shows the exact commercial artifact: executive summary, savings range, data-exposure findings, policy recommendations, and the data AIRoute did not inspect.