Diagnose
Reconcile subscriptions, additional usage, credits and requests. Identify usage increases by model, day, team and task where IDs exist.
Interactive research demonstration
AI Cost & Efficiency Assurance
Where does AI spending go? Why does usage spike? Which employees, teams and projects are responsible—and which improvements actually save money without reducing quality?
Investigate before optimizing
Usage and cost dashboard
Synthetic organization by default. Import your own usage CSV for local analysis.
No file selected.
This page reads the file locally in your browser. No data is transmitted or saved by this demonstration. Import size limit: 10 MB. Employee/project attribution is unavailable unless fields exist.
Numeric recorded costs—Not necessarily cash paid
Processed tokens—Includes cached reads
Cached context—High share is not inherently waste
Records analyzed—Not necessarily complete tasks
Model token consumption
Top models in the selected period
Daily usage spikes
Largest token-volume days
Cost allocation
Reconcile numeric cost fields with plan allowances, invoices and credits.
Employee and project attribution
Investigation notes
Illustrative savings scenario
Use a hypothetical expense basis, not the recorded CSV cost. Savings must be verified against accepted work and quality.
Illustrative net monthly difference: —
What General Reliability can assess
Prevent
Evaluate model routing, context control, retry limits, cost thresholds and checkpoint/restart practices.
Verify
Test whether proposed savings preserve accepted output, rework rates and reliability—not merely lower the token count.
Extend
Combine appropriately permissioned enterprise usage feeds with department, employee and project identifiers. No employee content surveillance is required for basic accounting.