By 2026, every engineering team, marketing team, legal team, and finance team is using AI. The monthly invoice from OpenAI, Anthropic, and Google is growing. Nobody can tell you which department drove it, which project it belongs to, or whether the model tier used was even necessary.
P402 Enterprise is the metering layer that makes AI spend auditable, down to the team, the session, the token, and the model tier choice. Routing observations are surfaced for review; Optimize recommendations remain blocked in the app until a measured baseline is in place.
P402 Enterprise maintains a full attribution chain from the organization down to the individual token. Every LLM call is tagged at ingestion and attributed at billing.
Every LLM call priced at token granularity. Cost calculated at request time, not billing time. Model tier, token count, and USD cost recorded per request.
Full request trace: which employee, which session, which model, which prompt pattern, what output, what cost. Traceable from the monthly invoice back to the individual keystroke.
Real-time spend tracking across the hierarchy. Department dashboards, project burn rates, budget allocation by team. Budget consumption visible live, not at month-end.
Immutable session receipts: model used, tokens consumed, routing decision rationale, cost, timestamp, Tempo settlement hash. Exportable as JSON or PDF.
Every session produces an audit artifact: who did what with which AI, when, at what cost, with what output. SOC2, ISO 27001, and internal legal review ready.
Per-department routing policies. Engineering: quality-first for complex tasks. Marketing: cost-first for content generation. Legal: compliance-aware, minimum Claude Sonnet. Each policy enforced per request.
Gemini Pro analyzes routing history and task-type similarity scores from your tenant. Surfaces tasks where premium models were used alongside economy alternatives. Optimize recommendations remain blocked until a measured quality baseline is established.
Budget projection engine: current velocity × remaining days = end-of-period forecast. Alert thresholds at 80% and 95% budget consumption. Projected overage surfaced 2 weeks early.
The engine compares output quality scores across model tiers for each task type in your tenant. It surfaces tasks where economy models match premium-tier output. Any actual savings depend on a measured baseline and a human routing change; the app does not promise a percentage.
Each department gets a monthly budget in USD. When projected spend (current velocity × remaining days) exceeds 90% of the cap, the department head is notified. When the cap is reached, requests are blocked or downgraded to economy tier, configurable per policy.
Individual projects get their own sub-cap within the department budget. A client engagement can be capped at $50 regardless of the department's remaining budget. The cap is enforced at the session level before any LLM call is made.
Each department can set a minimum and maximum model tier. Legal: minimum claude-sonnet-4-5 (no economy models for contract work). Marketing: maximum claude-sonnet-4-5 (no premium models for copy). Enforced at routing time, not billed after the fact.
Session costs 3x above the team member's 30-day average are flagged automatically. A sudden $10 single-session run surfaces in the anomaly review queue within 60 seconds; the session is not blocked.
The audit requirement is different in every industry. But the underlying need is the same: prove what the AI did, when, for whom, at what cost, with what output.
Org KPIs, department breakdown, workflow value by team, model mix, budget projections, routing observation panel, and session log, all wired and running with synthetic data. Connect your P402 API key to see real org spend.