ANALYTICS AND BI
Governed metric definitions and analytics context on a BI platform
Every dashboard defines "revenue" slightly differently, and corrections get lost. Wexa brought the BI apps into the context graph and made metric definitions governed: a correction in chat becomes a proposed rule, the metric owner approves it, and the master measure is updated with a full audit trail.
- graph nodes / edges from 6 tables and 353 rows
- new Wexa connectors, 10 actions each
- of metric changes approved by the business owner
- rollback of rule and measure together
- CLIENT
- Enterprise analytics team on a leading BI platform (with the BI vendor)
- REGION
- Global
- WHEN
- 2026
- ENGAGEMENT
- Partner build
- WEXA COMPONENTS
- Wexa · new BI connectors · ontology derived from the BI data model · context graph · owner approvals
01Case 23
The challenge
- Inconsistent metric definitions across apps
- Corrections in conversation never reach the semantic layer
- No audit of who changed a definition and why
What Wexa built
- Ontology derived from the BI data model: key analysis turns tables into entities and associations
- Chat with routing, grounding and tools: evaluate, graph query, forecast, impact
- Rule lifecycle: correction → checked proposal → owner approval → rule stored in Wexa and measure updated → rollback
- Forecasts with ranges and backtests; role-based chart visibility; credentials only in Wexa connectors
02Case 23
How it runs on Wexa
03Case 23
Results
| Measure | Before / without Wexa | With Wexa |
|---|---|---|
Analyst time reconciling metric definitions (10 analysts) | 20% of time | 5% of time |
Annual cost of the process | $240K | $60K |
Time to implement the AI solution | 4 months | 2 weeks |
AI build cost (one-time) | $192K | $7K |
AI operating cost per year (models, infrastructure, upkeep, platform) | $141K | $69K |
Total AI cost in year 1 | $333K | $77K |
With Wexa
net annual value to the client after Wexa's own operating cost
faster to implement
lower AI operating cost
less AI spend in year 1
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