Atlas · Partner data room for card issuers
Give a co-brand partner program insight in days, without one cardholder row leaving the bank.
Atlas onboards a partner as a governed data domain. The partner gets contracted metrics with small cells suppressed. Your analysts, models and AI assistant get rows masked by role and purpose. Postgres enforces every rule on every path, and every read lands in a tamper-evident log.
Live on the synthetic book
- Transactions
- 50,000
- Aurora program
- 10,117
- Rows the partner can read
- 0
- Smallest published cell
- 25 cardholders
The proof, in three steps
- 1Pick a persona
The co-brand partner, a marketing analyst, a fraud analyst, an auditor, the platform admin.
Open access controls - 2Try to see cardholder data
Any way you like: row queries, a different purpose, or asking the AI analyst for emails and phone numbers.
Ask the AI as the partner - 3The database refuses, and logs it
Masked, suppressed or refused by Postgres itself, the same way on every path, with every attempt in a tamper-evident log.
Verify the log
The problem
Co-brand partners want program insight in days. Regulators want provable control. Today that means bespoke extracts: each one a new copy of cardholder data, a new review, and a new way to leak.
A new partner is one program code and one persona against policies that already passed their tests. Nothing is rebuilt per partner.
The partner receives contracted aggregates only. Cells under 25 cardholders are suppressed by the database, value and count both.
The same rules hold for the analyst's screen, the API and the AI assistant, and one self-test proves it for every persona and purpose.
Explore
What the partner receives, what it never receives, and how a new program goes live in days.
Act as any persona and purpose and watch Postgres scope, mask, suppress or refuse. Live.
Plain-English questions that compile onto masked views. The model never writes SQL.
Owned, contracted, classified products with lineage and field-level rules.
Fraud, credit-line and attrition models with SR 26-2 tiers and fair-lending notes.
Roadmap, build-versus-buy, delivery plan and the governance register.
Principles
Program scope, purpose, masking and logging are designed into the data product, not bolted onto each consumer.
The partner reads governed aggregates computed where the data lives. There is no extract to leak or to clean up.
Who AND why. The fraud analyst sees clear contact data under Fraud Prevention and under no other purpose.
The assistant asks through the same masked views as every analyst, so a clever prompt cannot become a data leak.