P402
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> _ ENTERPRISE AI BUDGET DASHBOARD

Enterprise AI budget
dashboard.

P402 Monitor turns the metering ledger into a dashboard for AI spend by department, employee, workflow, model, vendor, budget, outcome, and evidence status.

Run AI Spend AuditSee dashboard

Built on the P402 Monitor surface. Parent product page: /enterprise.

The problem

Finance owns the AI line item without the AI breakout.

Provider invoices show monthly totals. They do not show which department, employee, workflow, vendor, or customer the spend belonged to. An enterprise AI budget dashboard needs that breakout at event time, not at month-end.

What this dashboard shows
Department
Spend grouped by the department the event drew its budget from.
Employee
Spend grouped by the employee or API key that produced the event.
Workflow
Spend grouped by the task or process the call served.
Model
Spend grouped by requested and used model. Provider and model both surfaced.
Vendor
Share of spend per vendor across the organization, with single-vendor risk flagged.
Budget
Budget the event drew from, remaining balance, and breach status.
Policy result
Approved, warned, denied, or requires review, per event and rolled up.
Outcome
Accepted, rejected, revised, escalated, or failed, with accepted-output share.
Evidence
Receipt and evidence bundle coverage per workflow for audit and finance review.
How it works
  1. 01
    Create a P402 key.
    Sign in, generate a key. The key is returned once. P402 stores only a hash.
  2. 02
    Route AI calls through P402 or report meter-only events.
    Use the OpenAI-compatible endpoint, or post metadata-only events from your backend if you call the provider directly. Both land in the same ledger.
  3. 03
    Attach department, employee, workflow, vendor, and customer owners.
    Events are tagged with the owner taxonomy finance already uses. P402 indexes the event under every owner you supply.
  4. 04
    Read the dashboard.
    The Monitor surface groups AI spend by department, employee, workflow, model, vendor, budget, outcome, and evidence status. Finance and engineering read the same record.
Privacy

Meter economics, not content.

The enterprise AI budget dashboard reads metadata. Prompt and response storage are off by default. Sensitive workflows can run under private gateway mode, where the inference stays in your environment and P402 records the economic event over a signed channel.

  • metadata_onlyOwner, cost, tokens, budget, policy, outcome, and evidence status. No prompts. No responses. Default.
  • fingerprint_onlyAdds a hash fingerprint of prompt and response for dedup and replay protection. Content stays out.
  • redacted_tracePrompt and response retained after a redaction pass for fields the tenant policy allows.
  • private_gatewayYour environment hosts the inference, P402 records the economic event over a signed channel.
  • full_traceOpt-in. Prompts and responses retained verbatim with the event. Requires explicit tenant policy.
Read the trust posture
Proof

Same metering layer, four shipped workflows.

Each vertical demo is a working surface on the same metering layer. Use them to see how event detail, ownership attribution, policy result, and evidence status feed the dashboard.

Healthcare
Medicaid prior authorization with HIPAA-aligned demo mode and human review gate.
Legal
M&A contract due diligence across an 8-document synthetic data room.
Real estate
Tenant application screening with fraud scoring and HUD fair-housing audit trail.
Enterprise
Department, employee, model, and workflow attribution across one organization.
For finance

Read AI spend by department, not by invoice.

Spend lands in the dashboard with department, employee, workflow, model, vendor, and customer attribution at event time. Outcome and evidence status are already attached. Reconciliation moves from monthly invoice to event-time ledger.

See dashboard
For compliance

Audit-grade evidence per AI call.

The dashboard exposes evidence readiness per workflow, with privacy mode, policy result, and receipt coverage on every event. Audit and legal can review the same record finance reports from, without exposing prompts.

Read the trust posture
FAQ
What dimensions does the enterprise AI budget dashboard cover?+

Department, employee, workflow, model, vendor, customer, budget, policy result, outcome, and evidence status. Each AI call is recorded under every owner it belongs to and rolled up into the dashboard view finance reads from.

Does the dashboard require sharing prompts?+

No. The default privacy mode is metadata-only. The dashboard reads owner, model, tokens, cost, budget, policy result, outcome, and evidence status. Prompt and response content stay out unless the tenant explicitly opts in.

Does this work with our existing AI providers?+

Yes. OpenAI, Anthropic, Gemini, Bedrock, OpenRouter, and any HTTP-callable model land in the same ledger under a consistent owner taxonomy. Calls routed through P402 and calls reported as meter-only events both appear in the dashboard.

How is this different from an LLM observability dashboard?+

LLM observability shows prompts, responses, latency, and errors. The P402 enterprise AI budget dashboard shows the economic event around each call: owner, budget drawn from, policy result, outcome, and evidence status. Finance and engineering read from the same record.

How long does it take to see the dashboard with real data?+

Once a P402 key is created, the first metered event lands in the ledger and the dashboard renders against real data. Two weeks of metering is usually enough to surface attribution and leakage patterns finance acts on.

What is the relationship between the dashboard and the AI Spend Audit?+

The AI Spend Audit is a one-time engagement that produces a delivered report on top of the same metering layer the dashboard reads from. The dashboard is the live surface. The audit is the discrete deliverable.

Get started

Give finance the AI ledger they already report from.

Start with the enterprise surface, or run the one-time AI Spend Audit to produce a delivered report on top of the same metering layer.

See enterpriseRun AI Spend Audit