Industry Scenarios
Real problems. Real industries. Real results.
These are not hypotheticals. They are the exact challenges we were built to solve.
Scenario 1: Pharma
10 Oncology Sources, 4 Months, Got Nowhere
The Problem
A top-20 pharma company needs to unify 10 oncology data sources: clinical trial management (Oracle), patient registries (Postgres), lab results (REST API), adverse events (MSSQL), and research publications (S3 CSV exports).
They spent 4 months with an internal team and a systems integrator. Result: a stale weekly CSV dump that breaks every time a schema changes.
With Integrius
Deploy Core inside their VPC (air-gapped). Connect all 10 sources via setup wizard. No code, no ETL pipelines.
Create "Unified Oncology View" data product.
Search: "Find all patients in the BEACON-3 trial". Returns federated results from trial management, lab results, and adverse events.
Optic: "Which clinical trial has the most missing data?" Returns an instant KPI showing BEACON-3 at 23% missing fields.
Optic: "Which trial sites have the highest dropout rate?" Returns a ranked table, bar chart, and narrative.
Permission Explorer: compliance officer verifies marketing team CANNOT see patient PII in 10 seconds instead of a 2-week audit.
Forecast: "Predict enrollment completion for BEACON-3". Returns best-case (August) and worst-case (November).
Simulation: "What if we add 2 more trial sites?" Enrollment completion pulls forward by 6 weeks.
Result
What took 4 months and failed is now running in production, governed, with lineage. For €5K/month.
Scenario 2: Banking
8 Systems, 1 Customer, 8 Different Names
The Problem
A mid-tier investment bank has KYC data in 8 systems. When compliance asks "show me everything about Acme Holdings", a junior analyst spends 2 days manually searching each system.
With Integrius
Connect all 8 sources.
Entity resolution discovers "Acme Holdings Ltd" (CRM), "ACME_HOLDINGS" (trading), "Acme Holdings PLC" (billing), and "acme-holdings" (compliance) are the same entity, automatically.
Search: "Acme Holdings". Returns a unified entity card from all 8 systems, with conflict detection flagging the address discrepancy (severity: HIGH).
Optic: "What's our exposure to Acme Holdings?" Returns an aggregated view with KPIs and trend chart.
Watchers: Forecast watcher fires 45 minutes before a transaction volume threshold is breached.
Permission Explorer: proves KYC data access controls for MiFID II with timestamped export.
GDPR: "Right to erasure". One API call traces all records across all 8 systems via lineage.
Result
2 days → 2 seconds
Compliance query time
40%
Duplicate records eliminated by entity resolution
Scenario 3: Enterprise Tech (Series D SaaS)
Data Mesh in Production in 2 Weeks, Not 6 Months
The Problem
A Series D SaaS company has 3 Salesforce instances, 2 data warehouses, a legacy REST API, and Kafka streams. VP of Data was hired to "build a data mesh."
After 6 months: a Confluence page with a diagram and nothing in production.
With Integrius
Connect everything: 3 Salesforce instances, 2 BigQuery projects, REST API, Kafka topics. 14 sources, 20 minutes.
Create 5 data products: Unified Customer, Revenue Analytics, Product Usage, Support Health, Pipeline Forecast.
Lineage graph: for the first time, VP of Data can see visually how every source connects to every product.
CEO asks "What's our revenue growth by region?" in a board prep meeting. Instead of a 2-week data team project, the answer appears in 3 seconds.
Entity resolution finds "Acme Inc" (Salesforce 1), "Acme Incorporated" (Salesforce 2), and "acme_inc" (BigQuery) are the same customer. 200+ duplicate entities resolved automatically.
Result
2 weeks
Data mesh in production (vs. 6 months, zero delivered)
€400K/year
Saved by replacing Collibra + Fivetran + Tableau