Florida class 5551 payroll on a workers' comp underwriting submission, reconstructed from stored records alone.
ACORD 130 FL, page 3, file fingerprint af27…
Extracted by the model, verified by code, used in a total, flagged.
Skill, model, carrier pack and rules versions at each step.
Reviewer, time and reason for every correction, plus final approval.
The summary, email, downstream Skill or transaction it fed.
Across many decisions: ask the same questions at portfolio scale, like every submission where a class code was corrected, or every output produced with model version X.
AI governance for insurance needs numbers a regulator can test. Data Lineage Foundation commits to four targets, confirmed in ADR-15, with human-in-the-loop approval on every sealed record.
Any edit, deletion or reordering is detected at load and on audit.
Follows your record-retention schedule, with legal hold.
Reconstruction needs no access to Claude, the Skill or the conversation, so the evidence outlives any one of your Insurance AI agents.
Data Lineage Foundation captures lineage inside the Skill run, seals it when a person approves, then verifies and anchors it on your Azure. This is AI in Data Lineage built for the auditor, not around them.
Sources are uploaded and fingerprinted. The Skill and lineage.py write an event at every step.
A reviewer corrects and approves. This human-in-the-loop checkpoint seals the record, events and manifest into one bundle.
The bundle is saved to SharePoint or Blob. The loader verifies, anchors and loads it.
Seal anchors go to immutable WORM Blob, the lineage store runs on PostgreSQL, and failures are quarantined with an alert.
The Lineage API reconstructs any decision, Fabric and Power BI get an analytics copy, and evidence export serves audits and NAIC exams.
The same pipeline covers claims intake after first notice of loss (FNOL) and underwriting submissions: one data lineage model for every insurance workflow automation you run.
Your reviewers' judgment compounds. Each correction is logged with who, when and why, then fed back so the next run starts from what your team already knows.
Because every fix lives in the lineage record, you can see exactly which corrections shaped which outputs, and prove it to an auditor.
Your data stays yours.
It lives on your own Azure, is never pooled with another customer's data, and is never handed to outside vendors.
AI in insurance only scales when the numbers behind it hold up.
A permanent raw data layer, ACORD canonical modelling, and drill-through from any reported figure to its originating record. Platform-agnostic: Fabric, Databricks or Snowflake.
MGAs and program administrators under $500M. They owe carrier partners traceable claims and underwriting data but rarely have the engineering function to produce it.
A short paid assessment opens the relationship, then tiers into foundation build, reporting and extensions. Each tier is a fixed scope.
Data Lineage Foundation is the base layer for insurance-native AI agents: once every output is traceable, new agents for FNOL triage, claims and underwriting plug into the same record.
Every run, from first input to delivered output.
The design extends unchanged in shape to A+1, A+2 and A+3.
Prompts, tokens or chat transcripts, and carrier system lineage.
Why it leads: it demonstrates in ninety seconds and answers NAIC Exhibit D directly, the core of AI governance for insurance. It also turns every later package, from FNOL to new Insurance AI agents and insurance workflow automation, into an expansion conversation instead of a new sale.
It is the data lineage record layer that ties every AI-produced value, such as a class code, a reserve or a payroll figure, back to the document, page and step that created it. It covers claims, underwriting and intake workflows and is stored on your own Azure, so you can reconstruct any approved output for any past date.
Every correction a reviewer makes is logged with who, when and why, and final approval seals the bundle. After sealing, any edit, deletion or reordering is detected at load and on audit. Human-in-the-loop review is not a step outside the lineage; it is part of the record, and the backbone of AI governance for insurance.
Yes. FNOL documents are fingerprinted at upload, and each extraction, validation and hand-off to a downstream Skill becomes an event. An adjuster or auditor can follow a value from the first notice of loss (FNOL) report to the summary, email or transaction it reached.
It answers NAIC Exhibit D directly. Evidence export produces audit-ready packages, and reconstruction needs no access to Claude, the Skill or the conversation. Regulators test the record, not the vendor's word, which is the standard AI in insurance is moving toward.
It covers all ten Skills in scope, on every run, and the data layer is platform-agnostic across Fabric, Databricks and Snowflake. New insurance-native AI agents inherit the same lineage, so AI in Data Lineage scales with your agent portfolio and claims and underwriting automation adds no new audit gaps.
Start with a short paid assessment. We trace a live value from your own FNOL, claims or underwriting submissions and show you the human-in-the-loop record your carrier partners and auditors expect from your Insurance AI agents. That is Data Lineage Foundation in practice.
Explore Data Lineage for Insurance
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