Commercial auto evidence packet to limit and schedule data

Line-Specific Proof

Commercial auto packets contain symbols, combined single limits, hired/non-owned auto posture, vehicle schedules, drivers, and supporting evidence. PolDex keeps those facts separate from personal auto output.

This shows the commercial-auto control path and release-gate posture. It does not claim customer production volume.

Status	Public-document proof case
Audience	Fleet brokers, commercial auto underwriters, compliance teams, and agency systems
Schema or surface	`commercial_auto`

All Case Studies Commercial Auto Schema ->

Before / After

The job PolDex takes off the workflow.

The product boundary is narrow: turn messy insurance evidence or parser output into reliable, schema-controlled data that another system can use.

Before

Manual or generic extraction

Commercial auto and personal auto language can both contain the word auto, causing broad extraction systems to mix schemas.
Liability symbols, hired auto, non-owned auto, and schedule details are often scattered across packets.
Manual reviewers need to trace every extracted value back to the packet page that supports it.

After

PolDex output

Commercial Auto output stays scoped to commercial-auto fields and does not activate personal_auto by keyword accident.
FastScript normalizes limits, schedule rows, symbols, and evidence into a stable data contract.
Agents and workflow tools can poll the job and write validated values into policy admin, compliance, or CRM systems.

Workflow

How the proof path runs.

Classify the packet

The classifier identifies commercial auto fit, source role, confidence, and whether the schema is release-ready.

Resolve limits and schedule signals

FastScript focuses on CSL, symbols, hired/non-owned posture, and schedule evidence rather than generic auto language.

Reject cross-line noise

Output shaping prevents unrelated personal auto fields from leaking into commercial auto artifacts.

Export or webhook

The final data can flow to JSON, CSV, XLSX, signed artifacts, webhooks, processor review, CLI, or MCP.

Output

What downstream systems can use.

These are the fields or artifacts this case study is meant to make legible to databases, workflows, operators, and AI agents.

Structured output

Data contract

policy_number
policy_period
combined_single_limit
covered_auto_symbols
hired_auto
non_owned_auto
vehicle_schedule
state_or_territory
evidence
conflicts
unresolved_items

Proof

Evidence contract

Policy or certificate source reference
Page/section for limit and symbol evidence
Schedule row evidence where present
Schema-scoped confidence and abstention count
Release-gate benchmark state for the schema

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