VECTORIO“For risk teams asked to prove last quarter's number, and to leave their cloud vendor”
Risk numbers you can reproduce on demand.
Identical results on rerun and on different hardware: reproducible risk figures.
- The supervisor asks to reproduce last quarter's figure and we can't, exactly.
- BCBS 239 compliance is a permanent project.
- DORA makes our cloud vendor a regulated dependency we must be able to leave.
- Identical results on rerun and on different hardware: reproducible risk figures.
- Lineage from input snapshot to reported number, suited to BCBS 239 accuracy and adaptability principles.
- Intraday what-if and drill-down on a desk workstation; no hyperscaler dependency under DORA.
From today to the first result.
a risk grid with scripts around it
Intraday risk takes hours on the grid while traders decide in minutes, and last quarter's figure cannot be reproduced exactly for the supervisor.
1 billion rows aggregated in 32.1 ms on one notebook, with identical results on rerun and lineage from input snapshot to reported number.
Run one intraday aggregation next to the grid and compare the figures.
one week pilotTransactions scored against each customer. Step by step.
Every transaction is compared with the customer's normal behaviour; risky ones are stopped, raised as a critical alert and filed as a case.
- Customer Profiles · Transaction StreamRandomDataProvider · RandomDataProvider
- Lookup CustomerLookup
- Compute Risk ScoreCompute Fields
- Fraud Detection GateQuality Gate
- Approved Transactions · Fraud AlertCSV Publisher · Alert
- Fraud Cases CSVCSV Publisher
customer-profiles → Generators.Random
tx-gen → Generators.Random
profile-lookup → ETL.Lookup
risk-compute → ETL.Compute
fraud-gate → Sync.QualityGate
approved-csv → CSV.Publisher
fraud-alert → ETL.Alert
fraud-csv → CSV.Publisher- Customer ProfilesLoads the customer reference table (average amount, frequency, risk tier).
- Lookup CustomerWaits until profiles are ready, then attaches each customer profile to its transaction.
- Compute Risk ScoreComputes a risk score from amount versus the customer norm.
- Fraud Detection GateRoutes anything with risk at or above 3.0 to a dead-letter table.
- Approved TransactionsWrites approved transactions.
- Fraud AlertRaises a critical FraudDetected alert for every routed row.
A real workflow from our end-to-end test suite; the designer view is illustrated.
What you get. From the first pilot.
Identical results on rerun and on different hardware: reproducible risk figures.
Lineage from input snapshot to reported number, suited to BCBS 239 accuracy and adaptability principles.
Intraday what-if and drill-down on a desk workstation; no hyperscaler dependency under DORA.
Why your tools fall short.
Legacy risk warehouses are batch-bound and hard to reproduce.
Cloud analytics add third-party risk under DORA.
Spreadsheets and desk-built tools undermine data lineage.
- Supervisory findings on BCBS 239
- DORA application and third-party exit requirements
- Intraday liquidity and market-risk demands
- Cost of grid compute
Your pilot. Three steps.
Your data shape
Tell us what data you have, where it lives and what the result must be.
A scoped pilot
On your data and your hardware, with the success measure written down first.
A clear decision
You see the result against that measure — and keep every workflow and record.
Related roles in Cost & reproducibility.
Data/analytics engineering teams fighting cloud warehouse bills
Stop paying a cluster for laptop-sized queries.
Retail, e-commerce and quick-commerce operations
City-scale inventory analytics on one box.
System integrators and consultancies building data platforms for clients
One engine to deliver, audit and hand over.
ISVs and SaaS vendors needing an embedded analytics / ETL engine
Built into your product.
AI/ML teams
Reproducible data prep at memory speed.
Insurance analytics
Reproducible reserves, run in minutes.
Mid-size companies without a data team
Replace the spreadsheet chain, keep the data.
Bring us your hardest data problem. Let's solve it together.
A short pilot on your data and your hardware. We agree on what success looks like before we start.