Solutions for insurance

Give AI a safe copy of your policy and claims data

GraphReplica builds a realistic replica of your policy, claim, and customer tables that keeps every relationship intact. Your AI and BI teams move now. Compliance stays satisfied. Real customers stay out of it.

0%

PII leakage across stress tests

100M+

records held consistent across tables

~5%

replica utility gap vs real data

< 1 wk

to your first safe dataset

See it on a claim

One claim, three tables, relationships intact

Flip the source data to a safe replica. Sensitive entities change to realistic stand-ins. The join keys that tie policy to claim to customer never move.

Policy, claim, and customer tables

Toggle the source data to a safe replica. Watch the join keys stay put.

Source data
Safe replica

customer

policy

claim

Sensitive, replaced
Join key, preserved
Non-sensitive, unchanged

Cross-table relationships

CU-40912→PL-77310→CL-558201

3 of 3 foreign keys join. Same keys in source and replica.

Claim note, same stand-ins

Adjuster spoke with Elena Cruz regarding claim CL-558201 on policy PL-77310. Vehicle VIN 5XYZU3LB7DG118274 inspected. Follow-up sent to elena.cruz@mail.com.

Replica quality vs real data

Model and BI utility
96% within 5% of real

Cross-table joins preserved
100% every foreign key

PII leakage
0%

Analysts can point AI and BI tools here today. No real PII to expose.

Why carriers choose GraphReplica

Ship AI on regulated data without the wait

Your data platform spans Databricks and Snowflake and holds policy, claims, and customer PII. GraphReplica turns it into something your teams can actually use.

Unblock AI and Gen BI on regulated data

Point coding agents and BI copilots at a safe replica of policy and claims data. Teams build now instead of waiting on the long privacy review.

Let analysts learn AI-powered BI safely

Analysts practice on a realistic copy before they ever touch production. Same shapes, same relationships, no real customer exposed.

Analytics stay accurate

Keys and relationships survive across policy, claim, and customer tables. Joins return the right rows so reports and models still hold. Utility stays within about 5% of real data.

What GraphReplica does

A replica your analysts can trust

Realistic entity replacement is the flagship. The same real-world entity gets the same stand-in everywhere, so the data stays usable and the math stays sound.

Relationships survive across every table

The same customer maps to the same stand-in in every table, file, and claim note. Primary keys and foreign keys stay intact so cross-table joins still work. Replacement consistency runs about 0.91 F1 with zero false identity merges.

Realistic stand-ins, not masking

Sensitive entities become believable stand-ins while everything non-sensitive stays exactly as it was. A VIN stays VIN-shaped, a ZIP stays a real area, tables keep their row counts and types. The replica reads like real data.

Zero leakage, audit-ready

Every run passes a leak check and a consistency check and produces audit-ready reports. 0% PII leakage across stress tests where unprotected models leak about 27.5% of injected sensitive values. Built to meet GDPR, CCPA, and HIPAA requirements.

Runs inside your environment

A container runs in your cloud, data center, or Databricks. Data never leaves and never reaches Secludy. Air-gapped. Setup takes about an hour and your first safe dataset lands in under a week.

Bring a sample of your policy and claims data. We will show you a safe replica that still joins. You get it in under a week.

Case study

A top-5 US property and casualty insurer

A global data platform spanning Databricks and Snowflake, blocked from giving AI and BI tools access to production policy and claims data.

The situation

Compliance would not let teams point coding agents or BI copilots at production policy and claims tables. Existing synthetic-data tools worked one table at a time and lost the relationships across tables, so the output was useless for real analytics.

What GraphReplica did

GraphReplica built a safe replica that kept primary-key and foreign-key relationships across policy, claims, and customer tables. The same customer got the same stand-in across structured fields and free-text claim notes, so joins and context survived end to end.

The outcome

AI adoption was unblocked on regulated data. Analytics teams moved right away instead of waiting on the long approval to use real data. The replica held up under the same models and dashboards the team already ran.

0%

PII leakage

100%

cross-table joins preserved

~5%

utility gap vs real data

< 1 wk

first safe dataset

“We stopped waiting on privacy review. Our analysts and agents work on data that still joins. Not one real customer is exposed.”

Put your claims data to work

Setup takes about an hour.