Insurance data foundation demo

Build the insurance data foundation your AI strategy needs.

Explore how an insurance data lake, shared business definitions, generative BI, and cited AI workflows can support actuarial, claims, and underwriting teams.

Schedule this demo See the use cases

The operating problem

Our data is everywhere, our semantic layer is missing, and every AI conversation stalls at the data step.

Insurance capabilities

What the demo explores.

Insurance data lake

Bring selected policy, claims, billing, third-party, and operational data into a shared AWS foundation with catalog, lineage, and access controls.

Generative BI

Let actuarial, claims, and underwriting leaders ask questions in plain English and review the definitions, sources, and drill-downs behind the answer.

Generative AI tooling for insurance

Provide the generative AI tooling and patterns insurance teams need: document understanding, summarization, drafting, and reasoning over claims and submissions.

Semantic and governance layer

Define the business meaning of policy, claim, premium, and exposure concepts so AI tools speak the carrier's language.

Land data

Bring selected policy, claims, billing, and third-party data into a lake with catalog, lineage, and access controls.

Define semantics

Encode the insurance business meaning of policy, claim, premium, and exposure concepts.

Open to AI

Connect generative BI and AI workflows to curated data, shared definitions, and source lineage.

Govern

Configure catalog, masking, retention, and access with your data and control owners.

Shared data foundation

Review coverage, freshness, lineage, access, and definitions for the data used in the pilot.

Time to answer

Measure request wait time, analyst effort, query performance, and user review time.

Reviewable AI output

Check whether each pilot response shows the data, definitions, and sources a reviewer needs.

Data run rate

Compare current and candidate storage, compute, movement, license, and operating costs.

Use cases to evaluate

Three workflows to put in front of your team.

Each use case starts with an operating problem, shows a candidate workflow, and identifies the outputs and measures your team can review in a pilot.

UC1

Insurance data lake landing

"Policy, claims, billing, and third-party data sit in silos, so every analysis takes a week."

Carriers with multiple core systems where every analytics question has to assemble data first.

What this demo shows

The demo maps selected policy, claims, billing, and third-party data to a lake architecture with catalog, lineage, masking, and shared business definitions.

What a pilot includes

Pick the source systems, land data on the lake, build catalog and lineage, define the semantic layer, and roll out to a pilot group.

Example prompt or trigger

Land our policy, claims, and billing data on the lake with catalog, lineage, masking, and a defined semantic layer.

Example outputs

Insurance data lakeCatalog and lineageMasking rulesSemantic layerPilot dataset

UC2

Generative BI for insurance leaders

"Actuarial, claims, and underwriting leaders wait for analyst answers that should be self-service."

Carriers where the analytics team is the bottleneck and leaders want answers they can trust.

What this demo shows

The demo lets leaders ask questions in plain English about loss ratio, frequency, severity, premium mix, and channel performance, then review charts, definitions, source links, and drill-downs.

What a pilot includes

Connect curated data sets, configure the semantic layer with insurance definitions, and roll out to a pilot group of leaders.

Example prompt or trigger

Show me loss ratio trend for personal auto by channel this quarter, then break it down by severity bucket.

Example outputs

On-demand chartDrill-down viewScheduled summarySaved question libraryInsurance semantic layer

UC3

Generative AI tools for actuarial and claims

"Our actuarial and claims teams want AI tools but cannot trust outputs without lineage."

Carriers where AI tooling adoption is blocked by lineage and trust gaps.

What this demo shows

The demo uses connected data and lineage to prepare document summaries, extracted risk factors, and cited drafts for actuarial or claims review.

What a pilot includes

Pick the priority use case (document understanding, summarization, drafting), connect data with lineage, and pilot with one team.

Example prompt or trigger

Summarize this set of claim files, extract key risk factors, and draft an actuarial review note with linked evidence.

Example outputs

Claim summariesRisk factor extractionActuarial review noteLinked evidenceLineage view

Pilot path

A five-step path to a useful pilot.

Start with one workflow, agree on the baseline, connect the approved data, test with users, and let the results guide the operating plan.

Plan a pilot

Select

Pick the sources

Choose the systems, datasets, and business questions for the pilot.

Connect

Map and prepare data

Configure catalog, lineage, masking, quality checks, and access.

Define

Agree on business meaning

Document the policy, claim, premium, exposure, and outcome definitions leaders use.

Configure

Build the experience

Configure a generative BI or document workflow on the selected data.

Evaluate

Pilot with users

Compare answers, source coverage, review time, and user feedback with the current process.

Who this is for

Data, actuarial, claims, and underwriting leaders at carriers who own the data foundation that AI needs.

Insurance demo

Part of the Tactical Edge insurance demo library. See related demos for the rest of the customer journey.

Browse the library

What to measure

Agree on a baseline before the pilot, then compare the output, review effort, exceptions, quality, and operating cost with your current process.