Give a forecast
the evidence it deserves.

Use the narrative briefing and structured JSON together. The narrative should explain the decision context; the JSON should preserve precise, time-stamped facts and comparable events. Neither should include information that was unavailable at the cutoff.

Forecast Studio accepts a typed evidence packet defined by lems.forecast-context.v1. Use ISO 8601 timestamps. Every evidence event should have an ID, type, observed time, fields, and—when it became knowable—available_at. That availability time must be at or before forecast.forecast_time.

{
  "schema_version": "lems.forecast-context.v1",
  "forecast": {
    "event_type": "retail.weekly_demand",
    "forecast_time": "2026-08-01T14:00:00Z",
    "target_time": "2026-08-08T00:00:00Z",
    "question": "Which demand band will apply next week?",
    "output": {"kind": "categorical", "values": ["low", "base", "high"]}
  },
  "same_type_events": [{
    "event_id": "week-2026-07-25", "event_type": "retail.weekly_demand",
    "observed_at": "2026-07-25T00:00:00Z", "available_at": "2026-07-25T00:00:00Z",
    "fields": {"promotion": false, "inventory_index": 0.91}, "outcome": "base",
    "source": "internal weekly demand record"
  }],
  "adjacent_events": [],
  "assumptions": ["No unannounced promotion before the target week"],
  "provenance": {"demand_extract": "approved query reference"}
}

Formatting rules: valid JSON uses double-quoted keys and strings, no trailing commas, and no comments. Keep raw evidence in fields; use outcome only for resolved historical events. Never place credentials, customer secrets, or post-cutoff facts in the packet.

Define the uncertain event first, then use one exhaustive outcome set and the same evidence for all candidate actions. Enter the value of each action under each outcome. Decision Studio compares expected values; it does not guarantee an outcome or replace approval controls.

Upload CSV or Parquet records that reflect what could have been known before each historical event resolved. Include a stable event ID, outcome, observed/resolved time, and availability times for evidence fields. Choose private training by default: you authorize LEMs to process the data solely as your cloud/service provider for your private model. A separate contribution-permitted election is a permanent shared-training license for LEMs to use the data in shared models; it requires an owner/admin's separate terms acceptance, representation, and indemnity, and cannot later be revoked or converted to private treatment.

Validation, profiling, and the dataset-derived training quote are non-billable. After validation, LEMs uses the immutable dataset version to generate the fixed training charge for your approval. The training plan does not expose model size or internal capacity sizing.

Account owners and authorized billing administrators manage organization access, payment methods, auto recharge, credit packages, receipts, invoices, statements, notifications, and API keys. They can inspect the full immutable credit ledger and download monthly or custom-period CSV/PDF statements for their accounting teams.

Failed or unsettled payments do not create credits; a reversal can stop paid work or make a credit balance negative. A verified payment chargeback is different: it suspends the tenant, stops paid work, and leaves account owners/billing administrators read-only access to the ledger, statements, receipts, dispute status, and remediation workflow until a server-authorized resolution.

This guide supports effective use of LEMs. Results depend on the quality and timing of evidence, sound outcome/value design, implementation discipline, and ongoing evaluation.